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Journal of Entrepreneurship and Business
30
Abstract - Cloud computing is one of the latest Information Technology innovation phenomena that
has risen from the idea of sharing, consolidating, and standardizing of resources in a centralized
infrastructure and facility. This concept offers many advantages such as cost reduction in both
hardware and software investment for organizations. Despite these advantages, cloud computing
adoption among organizations is relatively slow with a low adoption rate. As such, this study
attempts to bridge the gap by offering insight into possible factors that could influence such
adoption decisions. By integrating the Diffusion of Innovation Theory (DOI) and IT personnel
characteristics, a conceptual model is developed and tested as a preliminary study to determine the
influencing factors of cloud computing adoption by the Malaysian public sector to enhance its
service delivery. The results revealed that relative advantage, compatibility, and IT personnel
knowledge are the innovation attributes and the human factor for cloud computing adoption in the
Malaysian public sector. This study contributes to the knowledge domain of cloud computing
adoption literature on theories of IT adoption particularly in the public sector.
Keywords: Cloud computing; Adoption; Diffusion of Innovation; Public Sector; Characteristics
!
1. Introduction
Governments around the world are struggling to deliver more efficient and effective public
services in order to meet the increasing demands and expectations of citizens and dealing
with the main problem of decreased public resources and financing at the same time. In the
prior literature, it is widely accepted that an electronic government (e-Government) is
much more complex than any previous efforts of IT that have brought changes to the
public sector. Therefore, the incorporation of new or latest information technologies in the
e-Government domain poses a challenging task due to several factors such as the
interoperability of applications and systems (Cellary and Strykowski, 2009), the variability
of its target stakeholders, the bureaucratic political influence, and the slow rate of adoption
(Wyld, 2009).
The emergence of the cloud computing technology has opened up new possibilities for
many governments. Cloud computing is one of the latest IT innovation phenomena that has
Factors Influencing Cloud Computing
Adoption in the Public Sector: An
Empirical Analysis
Journal of
Entrepreneurship and Business
E-ISSN: 2289-8298
Vol. 3, Issue 1, pp. 30 - 45. June, 2015
Faculty of Entrepreneurship and
Business, Universiti Malaysia Kelantan
Locked Bag 36, 16100 Pengkalan Chepa
Kota Bharu, Kelantan, Malaysia
http://fkp.umk.edu.my/journal/index.html
Date Received: 5th March 2015
Date Accepted: 23rd April 2015
DOI: 10.17687/JEB.0301.03
Hasimi Sallehudin*
PhD Candidate, Faculty of Entreprenuership and Business
Universiti Malaysia Kelantan, Malaysia.
Email: simung@yahoo.com
Razli Che Razak
Faculty of Entreprenuership and Business
Universiti Malaysia Kelantan, Malaysia.
Email: razlicr@umk.edu.my
Mohammad Ismail
Faculty of Entreprenuership and Business
Universiti Malaysia Kelantan, Malaysia.
Email: mohammad.i@umk.edu.my
This work is licensed under a Creative
Commons Attribution 3.0 Unported
License
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Journal of Entrepreneurship and Business!
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risen from the idea of sharing, consolidating, and standardizing of resources in a
centralized infrastructure and facility. This concept offers many advantages such as cost
reduction in both hardware and software investment for the organization. Cloud computing
technology is now of significant relevance for many different domains of e-Government
and there are well-known examples of its uses in enabling the idea of a connected
government (Shin, 2013; Wyld, 2009). In the literature, the number of studies emerging on
the factors that influence the adoption of cloud computing on various private sector
domains ranging from large organizations to small medium businesses (SME) are
increasing (Carcary et al., 2013; Gupta et al., 2013; Opitz et al., 2012). However, there is a
lack of empirical studies focusing on cloud computing domain and its application adoption
in the context of e-Government especially at federal, state, and local government levels. In
addition, the few studies that exist in this domain lack theoretical underpinning and the
backing of empirical research.
Cloud computing is an innovative IT solution used to operate business applications over
the Internet technology, just like online banking, electronic commerce, and electronic mail.
The cloud computing environment is about not having any more expensive, capital-
intensive hardware and infrastructure, and time-consuming, staff-intensive upgrades
(Buyya et al., 2011; Buyya et al., 2009; Dhar, 2012). Its exclusive feature, such as pay-as-
you-go, enables organizations’ finance, human resources, sales, or service applications
through a web browser. In the literature, the definition of cloud computing varies across
fields of study in business and technical domains (Buyya et al., 2011; Buyya et al., 2009;
Dhar, 2012; Mirashe and Kalyankar, 2010). Cloud computing is defined as a model for
providing on-demand access to computing services via the Internet. The services offered
by cloud computing include infrastructure as a service (data centre and server facilities),
platform as a service (operating systems), and application as a service (business packages,
e.g. Enterprise Resource Planning, Accounting Systems Planning, and Microsoft Office
package). Internet technology is the medium used as the transport mechanism between the
client and the services (servers or applications) located anywhere in cyberspace, as
compared to having this service residing on an “on premise” computer. Another literature
defines cloud computing as a means of renting IT infrastructure such as computers,
storage, and network capacity on an hourly basis from an organization that has these
resources and services in its own data centre and is able to make them available to
organizations and customers via the Internet (Issa et al., 2010). This style of computing is
where massive, scalable IT-related capabilities are provided as a service across the Internet
to multiple external customers. This solution can be interpreted as the illusion of infinite
computing resources available on demand, the elimination of top-front commitments by
cloud users, and the ability to pay for the use of cloud computing resources on a short-term
basis as needed (Buyya et al., 2011).
There are now governments across Europe and Asia that have benefited from the cloud
computing technology and the adoption is increasing (Wyld, 2010). The United States of
America, United Kingdom, and Japan are examples where cloud computing has played a
major role in the governments’ transformation, especially in IT strategy and operations.
The ultimate aim for governments to adopt cloud computing is due to the fact that the
cloud computing innovation can stimulate e-government initiatives by using the “cloud-
government” or c-government innovation (Zhang and Chen, 2010). Research is also being
carried out to design the system architecture for the c-government to integrate
heterogeneous e-government into a generic and large-scale government cloud framework.
According to the Asia Cloud Readiness Index 2014 produced by the Asia Cloud
Computing Association (2014), Malaysia is ranked number eight (8) behind other Asian
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developing countries such as Japan, Australia, South Korea, New Zealand, Hong Kong,
Taiwan, and its neighbor, Singapore. The weakest indicators for Malaysia’s readiness
index are Broadband Quality, Business Efficiency Index, Global Risk, ICT Development,
International Connectivity, Data Protection Policy, and Government Prioritization.
In the context of the Malaysian government, cloud computing is a new concept and to
benefit from cloud computing assimilation, it will require a lot of success factor research
before it can be accepted and adopted. The Malaysian Administrative Modernization and
Management Planning Unit (MAMPU) is the main player and the lead agency in the
Malaysian public sector to play this role. The fact is that the present ICT systems and
infrastructures work wonderfully well on a stand-alone and silo basis for the federal and
state government organizations. The adoption of new technologies requires some legacy
systems in the public sector to be replaced while new systems and the existing compatible
systems need to be integrated. However, the IT personnel realize that there are some
pressing issues in IT that deserve serious attention. To exploit the advantages of the cloud
computing technology, the IT personnel in the Malaysian public sector should gradually
incorporate this innovation into their information systems’ processes to support the e-
Government.
Therefore, this study is concerned about identifying the problems that are hindering the
adoption of cloud computing technology in the Malaysian context and about deriving an
integrated information system theory model that can be used by other organizations. Thus,
the central aim of this study is to identify and examine the determinants of cloud
computing characteristics and IT personnel characteristics in the cloud-based services
adoption in the Malaysian public sector. This study aims to contribute to the emerging
field, specifically by focusing on the following research questions: (a) what is the
relationship between cloud computing technological factors on the propensity to adopt
cloud-based services in the Malaysian public sector?; and (b) what is the relationship
between IT personnel’s characteristics on the propensity to adopt cloud-based services in
the Malaysian public sector?
2. Literature Review
The Diffusion of Innovation Theory is derived from the exertion of Everett Rogers (1995)
and is frequently used to explain technology innovation especially ICT acceptance and
adoption studies. Rogers' DOI theory focuses on the spread of innovation (ideas, processes,
and technologies) over time among the members of a social system (Rogers, 1983, 1995a,
2002). The members of the social system are the adopters, who can be an individual, a
group, or an organization (Rogers, 1995). Rogers' concepts of diffusion include the adopter
categories, S-shaped curve, and the adoption predictors.
According to Rogers (1995), there are five categories of adopters in the DOI theory: (1)
innovators - the first individuals to adopt an innovation. They are generally young, willing
to take risks, have great financial fluidity, are very sociable, have the closest contact with
scientific sources, and interact with other innovators; (2) early adopters - the second
category of adopters to adopt an innovation. The characteristics of these adopters are
similar to those of the innovators; (3) early majority - adopters in this category adopt an
innovation after a varying degree of time, but it is significantly longer than the innovators
and early adopters. In addition, adopters in this category usually have above average social
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status and have contact with early adopters; (4) late majority - adopters in this category
will adopt an innovation with a high level of skepticism and after the majority of a society
has already adopted the innovation. They usually have below average social status and
very little financial fluidity; and (5) laggards - adopters in this category are the last to adopt
an innovation. Adopters in this category tend to focus on traditions, have the lowest social
status, lowest financial fluidity, and are the oldest of all the adopters.
Moreover, the DOI theory is also widely used to find adoption predictors of lT diffusion in
organizations (Frambach, 1993; Hameed et al., 2012; Lee, 2003; Oliveira and Martins,
2011; Zhu et al., 2006). According to Rogers (1995), there are three groups of adoption
predictors: individual (leader) characteristics (individual innovativeness), internal factors,
which consist of firm characteristics (business size, business age, available resources,
organizational slack, and how ICT is used and managed), and external factors of the
organization. Rogers (1995b) also emphasized the impact of technological characteristics,
namely relative advantage, compatibility, complexity, trialability, and observability on
potential adopters.
However, the technological characteristics focus on the primary objective features of the
technology itself (cloud computing technology), rather than the subjective features in the
mind of the decision maker in the Malaysian public sector such as the IT personnel.
Therefore, the researchers have conceptualized the innovation attributes relationship with
the propensity to implement cloud computing in the Malaysian public sector together with
IT personnel characteristics. Based on the theoretical background information and the
review of findings in the literature, this study proposes a two dimensional model, which
incorporates the variables of human and innovation factors in understanding the decision to
implement cloud computing in the Malaysian public sector. Therefore, this study
determines that these two dimensions will affect the Malaysian public sector’s decision on
the adoption of cloud computing technology in a positive manner.
2.1. Relative Advantage
Relative advantage is viewed as an advantage for an organization over previous ways of
performing the same task (Alam, 2007; Moore and Benbasat, 1991). Relative advantage
has been found to be one of the best predictors and is positively related to the innovation
adoption rate (Alam et al., 2011; Borgman et al., 2013; Rogers, 1995b). Borgman et al.
(2013) in their study using European respondents found that relative advantage of cloud
computing was positively linked to cloud computing adoption within the convenience of
the capital expenditure. However, the other concept for relative advantage is based on the
technological features that are offered which include simplicity of business process
(communication, coordination, and mobilization). However, the confidence level will be
low if the organizations do not have experience or are relatively new to cloud computing
systems (Buyya et al., 2009). In view of the advantages that cloud computing offers, it
would be expected that the Malaysian public sector which perceives cloud computing as
advantageous would likely adopt cloud computing thus enhancing the e-Government
service delivery to the stakeholders. Thus, it is hypothesized that:
H1: Relative advantage has a direct influence on the propensity to adopt cloud-based
services in the Malaysian public sector
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2.2. Compatibility
An innovation that is perceived to be in tandem with the organizations’ work behavior,
values, experience, and practice or compatibility will experience a high rate of adoption
(Rogers, 1995b). Compatibility of the technological innovation with an incumbent system
or technology can either speed up or retard its rate of adoption in the organization.
Compatibility of the innovation can be measured by the degree to which the innovation
meets the client’s needs. Organizations should determine the requirement to fulfill their
clients or customers’ needs, then recommend the adoption of innovation (Alam et al.,
2011). The adoption rate will be faster and higher when the innovation is compatible with
these needs. However, if the innovation is irrelevant to the needs, it may not be adopted
even though the innovation can provide better performance and services in accomplishing
a given task (Alam et al., 2011). Cloud computing compatibility within the Malaysian
public sector is expected to influence the adoption more positively because most of the
incumbent systems and technology infrastructures are based on the famous and known
technology solutions such as Microsoft, Linux, Oracle, Cisco, etc. This technology is a
vendor supported cloud computing compatibility. Thus, it is hypothesized that:
H2: Compatibility has a direct influence on the propensity to adopt cloud-based services in
the Malaysian public sector
2.3. Complexity
Complexity of innovation is measured as the degree to which cloud computing innovation
is perceived as being relatively difficult to understand and use. One of the barriers to
innovation is the complexity of the innovation to be understood and used. Cloud
computing is a new business solution model with a high complexity. Cloud computing,
contains three different levels of models such as Infrastructure as a Service (IaaS),
Platform as a Service (Paas), and Software as a Service (SaaS). Therefore, the business
model needs to be carefully analyzed and designed in order to identify the proper, suitable
dimensions and facts, and the hierarchies at each of these levels. Besides, cloud computing
also offers four different deployment models (private, community, public, and hybrid
cloud) to choose from. An inadequate choice can have a major effect on the performance
of the entire system especially in e-government systems. In the context of this study, the
complexity of the cloud computing influences the adoption of cloud-based services in the
Malaysian public sector. Thus, it is hypothesized that:
H3: Complexity has a direct influence on the propensity to adopt cloud-based services in
the Malaysian public sector
2.4. Trialability
Trialability refers to the extent that organizations perceive their ability to try, test, or
experiment with a cloud computing platform with limited scale and full specification
before making a decision on whether to adopt it. An innovation to be adopted must be
tested with “proof of concepts” (POC) to fulfill the customers’ requirements which might
increase the intention of future adoption. In the Malaysian public sector context, POC is
the way in which organizations evaluate, test, and measure the degree of suitability and
compatibility of new innovation to the incumbent systems, infrastructure, and
environment. Triability or POC has become the mandatory requirement for any new ICT
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project adoption in the Malaysian public sector. The duration for a trial of a new
innovation depends on the agreement between parties, namely the Malaysian public sector
agencies and vendors. The POC will be evaluated and tested by the organization with
current deployment in the ICT environments. The longer the duration of the POC, the more
support the vendors will have to give which allows the organization to be further exposed
to the new innovation. A new innovation such as cloud computing innovation that offers
the features of trialability or POC to experiment with before adoption will positively
influence the adoption rate in the Malaysian public sector. Thus, it is hypothesized that:
H4: Trialablity has an influence on the propensity to adopt cloud-based services in the
Malaysian public sector
2.5. IT Personnel Characteristics
IT personnel characteristics play an important role in IT innovation adoption in the public
sector. In some organizations, there is a Chief Information Officer (CIO), Manager or
Head of IT department/unit, and decision maker for IT projects. These individuals are
responsible for introducing, influencing, operationalizing, and guarding new IT innovation
in an organization. In addition, the IT personnel are the only individuals in the organization
who are involved from the beginning of the IT adoption until its completion aside from the
CEO or head of department. Thus, factors such as IT personnel innovation, attitude,
knowledge, and tenure are important individual dimensions that influence the success of
cloud computing in the Malaysian public sector.
The IT personnel can influence IT innovation adoption by transforming their knowledge
and innovativeness. Knowledgeable and innovative IT personnel are always focused on
getting the new technology in place. Due to the dominant role of the IT personnel,
particularly in public sectors, these aspects are essential in the adoption of a new
technology such as cloud computing. Thong and Yap (1995) found that individuals, who
have a willingness to innovate such as the CEO, CIO, and IT personnel, can positively
influence IT innovation adoption. In the public sector, the CIO and IT personnel are
usually the owners of the initial decision making in new IT innovation adoption. Moreover,
innovative CIOs or IT personnel are willing to take risks and always prefer new solutions
that have not been tried before (Gerow et al., 2012). Prior studies found that the CIO and
IT personnel’s innovativeness significantly and positively influences the adoption of IT
innovations (Gerow et al., 2012; Thong, 1999). The slow rate of IT innovation adoption
process in organizations has been attributed to the lack of expertise and knowledge in the
IT personnel (Looi, 2005; Premkumar and Roberts, 1999). Teo et al. (2007) in their study
on the adoption and diffusion of human resources information systems in Singapore found
that the essential long-term success and continuous growth of IT in an organization is
based on the availability of skilled and knowledgeable IT professionals. In addition, a
study by Smith (2008) on successful adoption of IT Projects in the Public Sector in US
shows that IT personnel expertise is a key determinant of public sector IT innovation
adoption. This leads to the following hypotheses:
H5: IT personnel innovativeness has a direct influence on the propensity to adopt cloud-
based services in the Malaysian public sector
H6: IT personnel knowledge has a direct influence on the propensity to adopt cloud-based
services in the Malaysian public sector
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2.6. Propensity to adopt cloud-based services
Propensity to adopt cloud-based services is operationalized as an intention to use or adopt
cloud-based services by the Malaysian public sector. Intention to implement measures is
the dependent variable in this research model. It is defined as an organization’s intention to
implement or adopt cloud computing in the future. These are general measures using
forward-looking statements that capture the propensity of the organization. It will be used
to represent the usage of cloud computing. Psychology researches have indicated that the
intention to use is the best predictor of actual system usage (Ajzen, 1991; Davis, 1989;
Venkatesh and Davis, 1996, 2000; Venkatesh et al, 2012). It has been shown that the
propensity to adopt is directly related to actual use and in some studies, intention to adopt
or use and actual adoption have been used interchangeably.
3. Methodology
3.1. Participants
The study population comprises CIOs, Heads of Departments, IT Managers, and IT
personnel who are working in Malaysian public sectors. The selected sampling frame for
this study consists of 730 IT officers currently in service in various ministries,
departments, and agencies across the country. A purposive sampling technique was
employed. The reason for choosing this sampling frame is the relevance of its working
population to this study. An online survey questionnaire using ‘Google Forms’ was
distributed to the sample of this study via the Malaysian public sector IT officers society’s
(Perjasa) Facebook group in May, 2014. During the data collection period, the number of
responses received for this study was only 85. Compared to the standard response rate of
60 percent as suggested by Hair et al. (2006), this study’s response rate is not high, but
falls in line or even better in some cases than previous studies. The respondents were
informed that their participation was voluntary and the information they provided was
confidential. The characteristics of the samples are shown in Table 1.
Table 1: Sample characteristics (n=85)
Items
Frequency
Percentage
Age
26-35
63
74.1
36-45
22
25.9
Gender
Male
46
54.1
Female
39
45.9
Academic Qualification
Diploma
24
28.2
Bachelor
35
41.2
Master
26
30.6
Service Position
Manager/Head of IT department
9
10.6
IT Officer
54
63.5
IT Staff
18
21.2
Others
4
4.7
Length in service
Less than 2 years
6
7.1
Between 2-4 years
10
11.8
Between 5-7 years
32
37.6
Between 8-10 years
15
17.6
More than 10 years
22
25.9
Experience in IT project
decision making
Less than 2 years
32
37.6
More than 2 years
53
62.4
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3.2. Survey Instrument and Analysis
Measures used in the study are presented in Table 2. All the items’ measures were obtained
from previous researches whose validity and reliability have been demonstrated. All
indicator variables are modeled as reflective because they are seen as functions of their
associated latent variables. The methodology to measure the research model for this
research is the structural equation modeling (SEM) of the data analysis. This approach has
many advantages over other methods such as the multiple regression. Using the SEM
approach, a partial least squares (PLS) method was selected. Partial least squares (PLS) are
a popular Structural Equation Modeling (SEM) technique used to conduct data analysis
(Goodhue, Lewis, and Thompson, 2006). SmartPLS version M3 2.0 (Ringle et al., 2005)
was chosen because the PLS is more suitable in handling relatively small sample sizes
(Hair et al., 2011) in contrast to the co-variance based (CVB SEM) techniques such as
AMOS and LISREL. In this study, the total usable sample of 85 respondents can be
viewed as a small sample size. Moreover, researchers in this field rely greatly on the PLS
for testing path models and theory confirmation (Goodhue et al., 2006).
Table 2: Research instruments
Variables
Measure
No. of
Items
Source of Scale
Relative advantage
Multi-terms
8
Moore and Benbasat (1991)
Compatibility
4
Moore and Benbasat (1991)
Complexity
3
Thompson et al. (1991)
Trialability
7
Moore and Benbasat (1991)
IT personnel Characteristics
(Innovativeness & Knowledge)
7
Thong and Yap (1995);
Ghobakhloo et al. (2011)
Propensity to adopt cloud-based
services
5
Davis (1989);
Martins et al. (2014);
Venkatesh et al. (2003)
Demographic and agency
information
Age, Gender, Education Level, Job title, Current position grade,
Experience in the Malaysian public sector, Experience in decision
making
4. Findings and Discussion
4.1. Validity and Reliability
Validity refers to how accurately the construct reflects what it intends to measure, and
reliability refers to the consistency of the results obtained. As suggested by Hair et al.
(2010), this study used factor loadings, composite reliability, and the average variance
extracted to assess convergent validity. As recommended by Hair et al. (2010), the
loadings for all items exceeded the value of 0.5. In addition, the composite reliability
values, as shown in Table 3, describe the degree to which the construct indicators indicate
the latent construct and they ranged from 0.876 to 0.974. These indicators exceeded the
recommended value of 0.7 (Hair et al., 2010). Moreover, the average variance extracted
(AVE), which reflects the overall amount of variance in the indicators accounted for by the
latent construct, ranged between 0.650 and 0.825. These indicators exceeded the
recommended value of 0.5 (Hair et al., 2010). This suggests that all measures in this study
sufficiently meet the validity. Discriminant validity was also established as the indicator
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variables loaded better on their associated constructs than other constructs as indicated in
the next section.
Table 3: Result of the measurement model
Constructs
Code
Loadings
CA
CR
AVE
Relative advantage
RA1
0.911
0.969
0.974
0.823
RA2
0.969
RA3
0.928
RA4
0.934
RA5
0.937
RA6
0.766
RA7
0.843
RA8
0.954
Compatibility
COMP1
0.890
0.929
0.950
0.825
COMP2
0.932
COMP3
0.907
COMP4
0.903
Complexity
CPLX1
0.982
0.879
0.876
0.706
CPLX2
0.759
CPLX3
0.759
Trialability
TRIAL1
0.870
0.959
0.966
0.802
TRIAL2
0.894
TRIAL3
0.884
TRIAL4
0.929
TRIAL5
0.901
TRIAL6
0.885
TRIAL7
0.902
IT Personnel
Innovativeness
IT Personnel
Knowledge
INNOV1
0.802
0.827
0.882
0.650
INNOV2
0.815
INNOV3
0.819
INNOV4
0.789
KNOW1
0.848
0.755
0.887
0.798
KNOW2
0.936
Propensity to
implement
INT1
0.804
0.905
0.930
0.727
INT2
0.809
INT3
0.921
INT4
0.890
INT5
0.833
Note: CA: Cronbach’s Alpha; CI: Composite Reliability; AVE: Average Variance Extracted
Table 4: Discriminant Validity of Latent Variables
Note: *square root of AVE on diagonal
4.2. Discriminant Validity
Next, this study proceeded to test the discriminant validity of the constructs. Discriminant
validity is the degree to which a construct is truly different from other constructs both in
1
2
3
4
5
6
7
1. Compatibility
0.908*
2. Complexity
0.125
0.840*
3. IT Innovativeness
0.599
0.169
0.806*
4. IT Knowledge
0.496
-0.024
0.742
0.893*
5. Propensity
0.774
0.157
0.632
0.632
0.853*
6. Relative Advantage
0.666
0.050
0.459
0.488
0.748
0.907*
7. Trialability
0.636
0.226
0.545
0.319
0.436
0.252
0.895*
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terms of how much it correlates with other constructs and how its particularly measured
variables represent only this single construct (Hair et al., 2010). Measures of discriminant
validity are supported when the square root of the average variance extracted for each
construct is the highest for its assigned construct (Fornell and Lacker, 1981). As shown in
Table 4, the correlations for each construct are less than the square root of the average
variance extracted by the indicators suggesting adequate discriminant validity. In total, the
measurement model demonstrated adequate convergent validity and discriminant validity.
4.3. Structural Model
Subsequently, this study assessed the structural model for the hypotheses testing. In this
test, the statistical significance was assessed by t-tests based on a bootstrap procedure with
1,000 bootstrapping samples. To evaluate the results, the explained variances, R2, and path
coefficients can be interpreted similar to those in the simple regression (Hair et al., 2010).
This study begins the interpretation with the hypothesized factors. As shown in Table 5,
three out of the six hypotheses were supported. Relative advantage (β = 0.338, ρ = 0.01),
Compatibility (β = 0.409, ρ = 0.01), and IT personnel knowledge (β = 0.227, ρ = 0.01) are
positively related to the propensity to adopt cloud-based services in the Malaysian public
sector. Thus, H1, H2, and H6 are supported while H3, H4, and H5 are not supported. All
the variables explain 75.4% of the variance in the dependent construct. This indicates that
in the proposed research, the conceptualization possesses adequate predictive power and is
useful in explaining future assimilation of cloud computing for the sampled Malaysian
public sector. Further discussion on the results is presented in the next section.
Table 5: Path coefficients and hypotheses testing
Path
β
t-statistics
Decision
H1
Relative Advantage -> Propensity
0.338
3.203**
SUPPORTED
H2
Compatibility -> Propensity
0.409
4.257**
SUPPORTED
H3
Complexity -> Propensity
0.091
1.365
NOT SUPPORTED
H4
Trialability -> Propensity
-0.041
0.493
NOT SUPPORTED
H5
IT Innovativeness -> Propensity
0.071
0.689
NOT SUPPORTED
H6
IT Knowledge -> Propensity
0.227
2.326**
SUPPORTED
Note: **P<0.01; *P<0.05
4.4. Discussion
In this study, Relative advantage was hypothesized to have a direct influence on the
propensity of IT personnel to implement cloud-based services in their department. This
study found that this hypothesis was supported. Therefore, it can be suggested that the
adoption of cloud-based services that is beneficial to the Malaysian public sector will
increase its assimilation. This result supports the studies of Alam et al. (2011), Chen
(2013), Law et al. (2013), and Premkumar and Roberts (1999) that have found relative
advantage to be a very significant factor in the decision to implement information
technology innovation.
Complexity was hypothesized to have a direct influence on the propensity of IT personnel
to implement cloud-based services in their department. This study found that this
hypothesis was not supported. This study also found that the path coefficient for
complexity was positive. It can be argued that while cloud computing features are
complicated and difficult to understand, the IT personnel found that this did not hinder the
adoption. In fact, they may feel that cloud computing features are easy to use in their
Sallehudin et al.
Journal of Entrepreneurship and Business!
40
agencies’ IT system. This result supports the studies of Alam et al. (2011), Chen (2013),
Hung et al. (2010), and Ramdani et al. (2009) that have also found that complexity was not
a significant technological attribute in decisions to implement information technology
innovation.
Compatibility was hypothesized to have a direct influence on the propensity of IT
personnel to implement cloud-based services in their department. This study’s result found
that this hypothesis was supported. Therefore, it can be argued that the cloud computing
features that are compatible with all aspects of IT services in Malaysian public sector lead
to a higher level of intention in the IT personnel to implement it in their department. IT
personnel also found that cloud computing services are completely compatible with the
current IT system in their department, thus increasing cloud computing assimilation. This
result supports the studies of Alam et al. (2011), Chen (2013), Law et al. (2013), Luqman
and Abdullah (2011), and Premkumar and Roberts (1999) that have posited compatibility
as a significant factor in decisions to implement information technology innovation.
Trialability was hypothesized to have a direct influence on the propensity of IT personnel
to implement cloud-based services in their department. However, this study found that this
hypothesis was not supported. This study also found that the path coefficient for trialability
was negative. This finding contradicts with the works of Kassim et al. (2012), Law et al.
(2013), and Ramdani et al. (2009) that have consistently found trialability to be a very
important factor. It can be argued that while cloud computing must be experimented
without any constraints before its actual adoption in the Malaysian public sector, the IT
personnel found that it was not necessary. They may think that cloud computing adoption
with different layers and models of adoption would not support the proof of concept before
the actual adoption. Thus, they found that it is a barrier to adoption.
IT personnel innovativeness was hypothesized to have a direct influence on the propensity
of IT personnel to implement cloud-based services in their department. However, this
study found that this hypothesis was not supported. Therefore, it can be argued that the IT
personnel were not willing to bear the risks of implementing the cloud computing
technology in their department. The dark side of the cloud computing technology would be
the barrier for IT personnel from taking the risk of adoption in the Malaysian public sector.
This result contradicts with the studies of Gerow et al. (2012), Hung et al. (2010), and
Thong (1999) that have consistently found personnel innovativeness to be a significant
factor in decisions to implement information technology innovation.
IT personnel knowledge was hypothesized to have a direct influence on the propensity of
IT personnel to implement cloud-based services in their department. This study found that
this hypothesis was supported. It can be argued that the IT personnel’s experience and
knowledge about cloud computing leads to a higher level of intention to implement it in
their department. This result supports the studies of Looi, (2005), Premkumar and Roberts
(1999), and Teo et al. (2007), who have consistently found executive knowledge to be a
significant factor in decisions to implement information technology innovation.
4.5. Implications
This study has significant implications for both academic literatures both theoretically and
practically in IT innovation for the public sector. In the context of theoretical literature,
this study offers additional validation of the significance of IT adoption theory based on
Sallehudin et al.
Journal of Entrepreneurship and Business!
41
Roger’s innovation attributes (Relative advantage, Compatibility, Complexity and
Trialability) that were used to measure different magnitudes of the approach toward
intention to adopt cloud-based services. There are many explanations that can describe the
possibly difference in results of this study from previous studies. It is implicit that Roger’s
Diffusion of Innovation theory applies to various research domains such as psychology,
sociology, marketing, and technology adoption. Therefore, the findings of the study based
on Roger’s innovation attributes might differ from one research field to another. In
addition, the combined theoretical perspectives can be used to better understand the
influence of various technological factors in cloud-based services adoption in the
Malaysian public sector.
Moreover, this study also found that both technology attributes, namely relative advantage
and compatibility were strong technological factors in IT personnel’s propensity to
implement cloud computing in their department. They viewed cloud-computing adoption
as advantageous in terms of enhancing service delivery and increasing the performance of
their department. They also regarded that cloud computing is compatible with their
department’s values and beliefs. In the dimension of complexity and trialability, this study
found that both Roger’s innovation attributes are not significant for IT personnel to
implement cloud computing in their department.
The findings also show the importance of the IT personnel’s characteristics in cloud
computing assimilation for the Malaysian public sector. A strategy to reduce the risk of
cloud computing adoption would lead to speed up the adoption in the Malaysian public
sector. The IT personnel are reluctant to adopt new IT innovation if the adoption is risky.
This finding supports the argument by Wyld (2009), who stated that the IT personnel in the
public sector were reluctant to jump into the cloud environment due to the fragile
environment of virtualization. Moreover, the IT personnel’s knowledge was found to be
significant in this study. This finding is supported by the reality in the Malaysian public
sector where awareness programs, technology update seminars, and ICT conferences
continuously highlight the cloud computing technology. Therefore, IT personnel in the
Malaysian public sector gain knowledge and experience regarding the cloud computing
technology once they join these programs.
Lastly, this study will also help policy makers in the Malaysian public sector to focus on
similar factors that will have a significant influence in the adoption or deployment of new
IT innovation, systems, or processes in the future. By now, the Malaysian public sector can
also start to ensure a smoother cloud-based services adoption plan by addressing the
characteristics of innovation and IT personnel in which their IT personnel will be most
likely concerned with.
6. Conclusion
The increase in cloud computing adoption by the governments in developed countries has
at least revealed what the governments understand about cloud computing – they are aware
of the operational and strategic roles and the impact of the cloud computing landscape in
today’s business. Despite this understanding, some governments are still waiting, and some
have searched for more convincing evidence that cloud computing assimilation will create
value before deciding on a major cloud computing investment and adoption. This study
Sallehudin et al.
Journal of Entrepreneurship and Business!
42
focused on examining DOI and the effects of IT personnel characteristics on organizational
adoption of cloud computing technology in the Malaysian public sector.
The research was done under the theoretical framework that was developed based on a
previous study. The structural model assessment using t-test on SmartPLS shows that
relative advantage, compatibility, and IT personnel knowledge are significant factors of
intention to adopt cloud computing in the Malaysian public sector. The model explains
75.4% of the variance in IT personnel’s intention to implement cloud computing in the
Malaysian public sector. As the government is concerned about the e-Government
services, connected government, service delivery efficiency, shared-services, and public
service transformation, an understanding of the factors that influence the Malaysian public
sector’s adoption of the cloud computing is invaluable.
Although this study provides insightful results, there are some limitations that need to be
considered when examining the results. The survey was conducted in the Malaysian public
sector context via online questionnaires. Therefore, the findings cannot be generalized to a
global reality and even in Malaysia, the sample only considered those who have access to
the internet and joined the Perjasa’s Facebook group (based on the online questionnaire
posted on Facebook) and the results are not a representation of the whole population’s
perceptions and behaviors. In addition, the framework of this study needs further studies to
understand the factors that encourage the IT personnel to adopt an innovation in their
agency. Although the study was not able to statistically compare the results between cloud
computing adopters and non-adopters’ departments, it sheds some light on possible
differences between these groups. However, the proposed framework offered a few
contributions in understanding the propensity to adopt cloud-based services, since it
resulted in a high effect size in most of the supported path coefficients. In addition, some
factors in the framework, such as complexity, trialability, and IT personnel innovativeness,
did not work as expected. It is suggested that future studies focus more on understanding
why these factors, which are normally important in information technology adoption, were
not significant within the context of propensity to adopt cloud computing. It is also
suggested that future studies focus on other dimensions of factors such as organizational
and environmental factors based on Tornatzky and Fleischer's (1990) framework to better
explain the success factors of cloud computing adoption in the Malaysian public sector.
Studies that look at the adoption process by comparing the public and private sectors might
be interesting in shedding light on the differences between these groups toward the
adoption process of cloud computing technologies.
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