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Preliminary insight into cloud computing adoption in a developing country
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DOI: 10.1108/JEIM-09-2014-0094
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Journal of Enterprise Information Management
Preliminary insight into cloud computing adoption in a developing country
Prince Kwame Senyo John Effah Erasmus Addae
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Prince Kwame Senyo John Effah Erasmus Addae , (2016),"Preliminary insight into cloud computing
adoption in a developing country", Journal of Enterprise Information Management, Vol. 29 Iss 4 pp.
505 - 524
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(2011),"Understanding the determinants of cloud computing adoption", Industrial Management &
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(2013),"Cloud computing adoption by SMEs in the north east of England: A multi-perspective
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(2015),"Understanding determinants of cloud computing adoption using an integrated TAM-
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Preliminary insight into cloud Preliminaryinsight
computing adoption in a into CCA
developing country
Prince Kwame Senyo and John Effah 505
Department of Operations and Management Information Systems,
Received 30 September 2014
University of Ghana Business School, Accra, Ghana, and Revised 9 March 2015
Erasmus Addae 1 July 201526 September 2015
Distant Learning Department, South Texas College, McAllen, Texas, USA Accepted 30 September 2015
Abstract
Purpose – The purpose of this paper is to investigate the determinants of cloud computing adoption
(CCA) in a developing country context through the lens of the technology, organisation and
environment (TOE) framework.
Design/methodology/approach – The study was carried out using the quantitative research
methodology based on a survey of 305 organisations from different industries in Ghana. Based on the
TOE framework, a conceptual model consisting of ten hypotheses were proposed and tested through a
confirmatory factor analysis and logistic regression analysis.
Findings – The findings indicate that relative advantage, security concern, top management support,
technology readiness, competitive pressure and trading partners’ pressure were the TOE factors found
to be significant in CCA in a developing country context. Conversely, firm size, scope, compatibility
and regulatory support were found to be insignificant.
Originality/value – This study provides insights into CCA across different industries in a developing
country environment. The study is arguably the first kind of empirical research into CCA in a
developing country context, specifically in Ghana. The findings from this study provide a foundation
for other studies as well as constructive insights for the development of cloud computing, due to its
infancy in the developing world.
Keywords Determinants, TOE, Ghana, Cloud computing, Developing countries, Adoption
Paper type Research paper
1. Introduction
Cloud computing has been described as a new development in the field of information
technology (IT) (Armbrust et al., 2009). With this advancement comes a huge potential
which businesses and governments in the developed world are already utilising to
improve service delivery and performance. Cloud computing basically entails the
provision of IT as a service rather than as a product. Cloud computing has been
adopted in private, public and non-profit sectors in both industrialised and developing
countries. However, the developed economies are far ahead of the developing world in
terms of adoption and use. In spite of the multitudinous benefits associated with the
cloud technology, its potential is yet to be realized in developing countries.
Extant research into cloud computing adoption (CCA) in developing countries
remains limited. Conversely, determinants of CCA in the developed world is well
established in information systems literature. To large extent, the technological,
Journal of Enterprise Information
organisational and environmental (TOE) contexts of cloud computing between the Management
Vol. 29 No. 4, 2016
pp. 505-524
The authors wish to acknowledge the editor and the anonymous reviewers for their thorough, ©Emerald Group Publishing Limited
1741-0398
constructive and critical comments. DOI 10.1108/JEIM-09-2014-0094
Downloaded by University of Ghana At 04:35 27 June 2016 (PT)
JEIM developed and the developing world differ. For example, the technological
29,4 infrastructure in the UK, as a developed country is far more advanced than that of
Ghana, as a developing country. Moreover, it is well noted in the general information
systems literature that research findings from the developed world do not directly
apply to the developing world due to contextual differences (Avgerou, 2001; Effah,
2014; Heeks, 2002). Therefore given that CCA is well established in the developed world
506 but not in the developing world, this paper investigates the determinants of CCA in the
developing country context of Ghana.
The rest of the paper is set out as follows. Section 2 reviews relevant literature on
trend in and adoption of cloud computing, as well as presents a conceptual model based
on the TOE framework and ten hypotheses for testing. Section 3 presents the research
methodology detailing how data were gathered and analysed. Section 4 presents the
findings while Section 5 discusses the findings. Finally, Section 6 concludes the study
and provides contribution as well as implications for future research directions.
2. Literature review
IT has long been regarded as a product. However, this notion seems to be dwindling as
many IT providers are striving to offer IT as a service and at reduced cost. Cloud
computing as an innovation provides opportunity for IT to be delivered as a service
rather than as a product. Cloud computing innovation has been with the IT industry for
a while. However, it was not initially commercialised. The genesis of cloud computing is
found in other technologies such as grid and parallel computing, distributed systems,
hardware and software virtualization, multi-core chips, and internet-based technologies
(Buyya et al., 2009). There is still not a standard definition of what cloud computing
entails (Sultan, 2010). In this paper cloud computing is defined as the delivery of IT
infrastructure and applications as a service on demand to individuals and
organisations via internet platforms.
2.1 Cloud service deployment and delivery models
Cloud services are offered through four deployment models, namely, private, public,
community and hybrid (Mell and Grance, 2010). First, the private cloud deployment
offers internal utilisation of technologies that are maintained in house (Yang and
Tate, 2012). Private cloud deployment is exclusive to an organisation and sometimes
managed by the organisation itself (Wai-Ming et al., 2013). Second, the public cloud
deployment provides services to the general public including organisations and
individuals. Public cloud infrastructure is often owned, hosted and managed by
third-party service providers (Marston et al., 2011). Some popular public cloud services
are Amazon EC2 (Elastic Cloud), S3 (Simple Storage Service), Google AppEngine and
SalesForce.com.
Third, the community cloud deployment offers cloud services to a group of
organisations with similar business missions, security and compliance requirements.
The membership of the group is likened to a community where common interest is
shared by members. Cloud services that the community deploy are exclusive to
members. Community cloud deployment may be managed by member organisations or
a third party and may exist on or off premise of members (Marinos and Briscoe, 2009).
Lastly, the hybrid cloud deployment provides a combination of private, public or
community deployment enabled by a standardized technology that ensures data
and application portability ( Jula et al., 2014). The hybrid deployment provides an
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alternative means to balance the advantages and disadvantages in the other Preliminary
deployment models (Mateescu et al., 2011). insight
There are three-main cloud service delivery models: software-as-a-service (SaaS),
platform-as-a-service (PaaS) and infrastructure-as-a-service (IaaS) (Mell and Grance, into CCA
2010). The SaaS delivery model is based on servers, bandwidth and software that are
offered and managed by a service provider. For SaaS, the client organisation looks for
services that meet or can be tailored to meet specific needs. The responsibility is on the 507
service provider to ensure continuous availability of the services to the client
organisation (Siamak, 2010). SaaS is the popular delivery model of cloud computing
and has been adopted by large companies such as IBM, Salesforce and so on.
The IaaS delivers hardware components consisting of servers, storage and network
infrastructure as well as associated software as a service through the internet.
IaaS users do not have to buy computing infrastructure; rather, the infrastructure is
provisioned to users on demand (Heinle and Strebel, 2010). Instead of traditionally
buying and installing computing infrastructure, IaaS provides these infrastructure via
the internet. Thus, there is no need for an individual organisation to purchase, install
and manage its own computing infrastructure under IaaS. The PaaS delivery model
consists of hardware, operating system and software framework which offers a hosted
web-based application development platforms for other applications to be developed
(Buyya et al., 2009). PaaS is built on IaaS to provide operating systems and other
application services over the internet, eliminating the need to download
and install applications on end-users’ computers. The internet-based development of
PaaS offers developers a method to build applications online (Giessmann and
Stanoevska-Slabeva, 2013).
Beyond, these main service delivery models, a number of variations are currently
found in the literature. These include concepts such as security-as-a-service, data-as-a-
service, communication-as-a-service, business process-as-a-service and IT-as-a-service
(Mujinga, 2012). The coinage of everything as a service or X as a service has
therefore come to stay. However, it is worth noting that all these are off-shoots from the
three-main service delivery models.
2.2 CCA
Generally, CCA has been studied from two contexts, namely developed and developing.
Some studies (e.g. Alshamaila et al., 2013; Sultan, 2014) have made significant contributions
to CCA from the perspective of developed countries. For example, Alshamaila et al. (2013)
studied CCA among small and medium sized (SMEs) organisations in North-East England
using the TOE framework. The findings from the study indicated the determinants of CCA
as relative advantage (RA), uncertainty, geo-restriction, compatibility, trialability, firm size
(FS), top management support (TMS), prior experience, innovativeness, industry, market
scope, supplier efforts and external computing support. Also, the study found competitive
pressure (CP) to be an insignificant determinant of cloud computing. These findings are
enlightening. However, adoption decision by SMEs might be different from large
organisations. Additionally, CCA from the perspective of developed countries might be
different from the perspective of developing countries. Therefore, there is a need to
investigate determinants of CCA across different organisation as well as from developing
country perspective. Considering the focus of this study, attention is turned to CCA in
developing countries.
CCA in developing countries has had a fair share of literature (e.g. Low et al., 2011;
Makena, 2013; Rawal, 2011) and relevant contributions have also been made.
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JEIM For example, Rawal (2011) investigated the adoption of cloud computing in relation to
29,4 e-Government in India and revealed that there is a low level of awareness, trust, and
adoption of cloud computing among government officials. Lian et al. (2014) combined
TOE and HOT-fit frameworks to investigate the factors that affect the decision to
adopt cloud computing in Taiwanese hospitals. The findings pointed out data security,
perceived technical competence, cost, TMS and complexity as significant adoption
508 factors. The study however had some limitations: first, it solely focused on the health
sector. The results from the health sector may not be applicable to other sectors.
Second, the sample size of 60 is less representative making it difficult to apply the
findings to other health sectors. Lastly, the study was also silent on the determinants of
CCA in the developing country context. Even though some studies (e.g. Cegielski et al.,
2012; Cheol et al., 2013; Wu et al., 2011) have attempted to point out respective factors
that influence CCA from individual, organisational and even national perspectives,
their findings are constrained to the context of developed countries. These limitations
in literature calls for more research to highlight context specific determinants of cloud
computing in developing countries.
In a closer look into Africa, some studies (e.g. Dahiru et al., 2014; Makena,
2013; Le Roux and Evans, 2011) have also been conducted on cloud computing.
For instance, Le Roux and Evans (2011) investigated how cloud computing can help
South Africa bridge the digital divide in secondary and basic education. The authors
believed, South Africa has the required skills and technology to reduce the digital
divide gap and set the pace for other developing countries. However, their findings
identified lack of political will and determination as key factors responsible for
widening the digital divide. Nonetheless, their study only reviewed cloud computing
applications and services that were in use by some developed country schools but did
not address the actual adoption issues faced by these institutions. Like others, their
study also did not highlight the determinants of CCA from the context of a developing
country. Their study is viewed to consider readiness factors instead of highlighting the
determinants of CCA. Makena (2013) investigated the factors affecting CCA by SMEs
in Kenya and found some TOE factors to be significant. However, the study did not
cover a broader spectrum of organisations to provide a holistic view on the adoption of
cloud computing. Thus, a study that involves a large number of organisations is
welcoming to provide more understanding and generalisation power to factors that
determine the adoption of cloud computing from a developing country perspective.
2.3 Research framework, model and hypotheses
Extant cloud computing literature has used prominent adoption frameworks and
theories such as technology adoption model (TAM), diffusion of innovation (DOI),
grounded theory, migration theory, theory of reasoned action (TRA), TOE framework
and so on. It has also been observed that two groups of frameworks are used in CCA
research, namely, individual adoption (micro-level adoption) and organisational
adoption (meso-level adoption) frameworks. Models such as TAM, TRA and DOI are
prominent in innovation adoption. However, such models are suitable for individual
level adoption and tend to consider only the technological dimension of innovation
adoption and not the organisational and environmental perspectives. Since this paper
investigates the determinants of CCA among organisations, the TOE framework which
accounts for the TOE factors is considered as the appropriate theoretical framework.
The TOE framework was developed to investigate firms’ decision to adopt and
implement an innovative technology taking into consideration the TOE contexts
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(Tornatzky and Klein, 1982). The three contexts are seen as both enablers and Preliminary
inhibitors of a technological innovation adoption. These contexts have great influence insight
on how firms perceive the need for, search for and adopt new technologies.
Table I illustrates selected CCA studies, factors they studied and their definitions. into CCA
The table also provides a snapshot of factors that are posited in extant literature as
determinants of cloud computing.
Technological context (TC). Cloud computing has attributes that favour or disfavour 509
its attractiveness and intention towards adoption. Rogers (2003) posits that any
Contexts Factor Definitions in this study Sources
Technological Relative The degree to which an Low et al. (2011), Alshamaila et al.
advantage innovation is perceived as being (2013), Tan et al. (2009), To and
better than the idea it supersedes Ngai (2006), Wang et al. (2010),
and Rogers (2003)
Security The fear of vulnerabilities in Chebrolu (2011), Zissis and
concern cloud computing systems Lekkas (2012), and Sultan (2014)
Compatibility The degree to which an innovation Hong and Zhu (2006), Tan et al.
is perceived as being consistent (2009), Rogers (2003), To and
with the existing value, past Ngai (2006), Oliveira and
experiences and needs of receivers Martins (2010), and
Wang et al. (2010)
Organisational Firm size Refers to the magnitude of an Dholakia and Kshetri (2004),
organisation in terms of number Hong and Zhu (2006), Oliveira
of employees, target market size and Martins (2010), Pan and Jang
and capital investment (2008), Wang et al. (2010),
Zhu et al. (2003), and
Alshamaila et al. (2013)
Top The backing important members Lee and Kim (2007),
management in management positions accord Wang et al. (2010), Low et al.
support an innovation and thus result in (2011), Oliveira and
the amount of resource allocated Martins (2010), and
to that innovation Alshamaila et al. (2013)
Technological Refers to the degree to which Kuan and Chau (2001), Oliveira
readiness technological infrastructure and and Martins (2010), Pan and
human resources are needed to Jang (2008), To and Ngai (2006),
support cloud computing adoption Wang et al. (2010), and
Zhu et al. (2006)
Firm scope The scope represents the area of Dewan et al. (1998), and Hitt
operation of the firm (1999)
Environmental Competitive The intensity of competition Lin and Lin (2008), Oliveira and
pressure among firms in an industry Martins (2010), Pan and Jang
(2008), To and Ngai (2006), and
Wang et al. (2010)
Trading Pressure from upstream and Chong and Ooi (2008), Lai et al.
partner’s downstream business partners (2007), Lin and Lin (2008), Oliveira
pressure with whom an organisation and Martins (2010), Pan and
conduct business transactions Jang (2008), Wang et al. (2010),
and Zhu et al. (2003)
Regulatory Refers to support given by Nkhoma and Dang (2013), Table I.
support government to encourage the Zhu et al. (2006), and Factors for cloud
assimilation of IT innovation Makena (2013) computing adoption
by firms and sources
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JEIM innovation that exhibits favourable, appealing and easy-of-use characteristics diffuses
29,4 more quickly than those that exhibit less of these attributes. Significant factors
identified from prior adoption studies (e.g. Alshamaila et al., 2013; Premkumar et al.,
1997; Thong, 1999) as components of the TC are RA, compatibility and complexity.
RA is defined as “the degree to which an innovation is perceived as being better than
the idea it supersedes” (Rogers, 2003, p. 299). RA of cloud computing is expressed in
510 terms of cost flexibility, increased productivity and scalability (Armbrust et al., 2009) to
meet growing demand of businesses. The scalability advantage also propagates the
green IT agenda as scaled infrastructure result in less power consumption (Marston
et al., 2011; Sultan, 2010). The “pay as you go” or pay-per-use feature of cloud
computing helps businesses to control cost. Therefore, pay-per-use motivates CCA.
This study adopts RA to measure CCA from the TC as depicted in the conceptual
model in Figure 1. Accordingly, the study proposes the following hypothesis:
H1. RA has an influence on CCA.
Cloud security remains a concern as many organisations feel unease to put their data
and resources on systems in the cloud for which they have limited control (Sultan, 2014;
Zissis and Lekkas, 2012). Surprisingly, prior adoption studies (e.g. Premkumar and
Technological
Context
H1: Relative Advantage
H2 : Security Concern
H3 : Compatibility
Organizational
Context
H4 : Firm Size
H5 : Firm Scope
H6 : Top Management
Support Cloud
Computing
H7 : Technology Readiness Adoption
Environmental
Context
H8 : Competitive Pressure
H9 : Trading Partners’
Figure 1. Pressure
Conceptual model
for cloud H10 : Regulatory Support
computing adoption
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Ramamurthy, 1995; Thong, 1999; Zhu et al., 2003) have not stressed on security as an Preliminary
important determinant. To fill this knowledge gap, this study includes security concern insight
(SC) as a determinant of CCA. SC is examined in this study in terms of how into CCA
organisations feel about security systems in cloud services and whether they are
willing to store their data in these systems irrespective of concerns. As an outcome,
the study proposes that:
H2. SC influences the adoption of cloud computing. 511
The “degree to which an innovation is perceived as being consistent with existing
value, past experiences and needs of receivers” (Rogers, 2003, p. 240) is referred to as
compatibility. Even though system compatibility has improved over the years, the
ability of a technology to fit and work perfectly with existing systems of an
organisation is seen as a crucial attribute worth considering in any technology
adoption (Lian et al., 2014; Premkumar et al., 1997). Compatibility of existing application
is another factor that deters firms from adopting a technological innovation (Heinle and
Strebel, 2010). Cloud service providers mostly have proprietary software that are not
always compatible with existing systems of their clients. As a result, client of a cloud
service provider needs to change their existing systems in order to deploy cloud
systems. For example, if the existing data encryption method of an organisation varies
from that of a cloud service provider, issues of compatibility will arise. Thus:
H3. Compatibility has an impact on the adoption of cloud computing.
Organisational context (OC). The second construct of the TOE framework is the OC.
For a technology to be fully utilised, it should fit well within an organisational setting.
Earlier information systems studies (e.g. Damanpour, 1987; Moch and Morse, 1977)
identified factors such as formalization, centralisation and integration as organisational
factors that determine the adoption of an innovation, but recent studies have accorded
less support to these variables (Segars and Grover, 1993). The earlier variables are
regarded to treat organisational features as objective realities whose factual character
is unchallenged (Slappendel, 1996) and too simplistic to explain the complexity in
adoption decision of an innovation (Premkumar et al., 1997). On the contrary, current
studies (e.g. Alshamaila et al., 2013; Low et al., 2011; Makena, 2013; Oliveira and
Martins, 2010; Tornatzky and Klein, 1982) identify TMS, FS, scope and technological
readiness (TR) as organisational factors that need to be considered in technology
adoption. Anand and Kulshreshtha (2007) pointed out that FS is determined by number
of employees, target market size and capital investment made in an organisation.
As such, larger firms have the propensity to invest in innovations and stand to benefit
greatly from the technology adoption decision. Similarly, other studies (e.g. Pan and
Jang, 2008; Zhu et al., 2003) have also asserted that large organisations tend to adopt an
innovation due to the tendency to adjust to risk as compared to smaller once. However,
the “pay-per-use” feature of cloud computing makes it easier for smaller firms to also
adopt cloud computing. Thus, the hypothesis:
H4. The size of a firm influences adoption of cloud computing.
FS is closely juxtaposed with firm scope (FSC). The scope represents the area of
operation of a firm. Cloud computing defies geographical restriction. Therefore,
organisations with branches around the world are best suited to adopt cloud
computing. Dewan et al. (1998) found that the greater the FSC the greater the demand
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JEIM for IT investment. Previous studies (e.g. Oliveira and Martins, 2010; Zhu et al., 2003)
29,4 have shown a positive relationship between IT adoption and FSC. This leads to the
following hypothesis:
H5. The scope of a firm influences adoption of cloud computing.
TMS refers to the level of importance placed on an innovation by senior management.
512 As such, the level of support results in the amount of resources allocated to that
innovation (Oliveira and Martins, 2010). TMS is regarded as an important
organisational factor to be considered in CCA since, it either advances or constraints
the adoption decision. Prior studies (e.g. Alshamaila et al., 2013; Pan and Jang, 2008;
Premkumar et al., 1997; Zhu et al., 2003) indicate that TMS positively relates to adoption
of an innovation. TMS is essential for providing adequate resources towards adoption.
Additionally, if the adoption of cloud computing requires business process
reengineering, the power of top management will be a significant organisational
factor. This importance of TMS leads to the proffer of the hypothesis:
H6. TMS influences CCA.
TR refers to the degree to which technological infrastructure and human resources are
needed to support adoption of an innovation (Oliveira and Martins, 2010).
Technological infrastructure consist of hardware, software, network resources and
services required for the existence, operation and management of cloud computing in
an organisation. The human resource readiness on the other hand is the knowledge and
skills of people required to implement and manage cloud computing services in
organisations (Lian et al., 2014). Most cloud service providers are far away from their
users. Hence, user organisation requires reliable internet connection to access cloud
based resources. Also, IT skills of the human resource in organisations is another factor
that may influence that adoption of cloud computing (Oliveira and Martins, 2010; Pan
and Jang, 2008; Wang et al., 2010; Zhu et al., 2003). As people will implement, operate
and manage cloud services if adopted. Therefore, the availability of technological
infrastructure and IT skills of human resource constitute the TR of a firm. As a result,
TR is essential in the adoption of cloud computing. Thus, the proposition of the
hypothesis:
H7. TR influences CCA.
Environmental context (EC). Businesses do not operate in a vacuum but rather in an
environment characterised by conditions that constrain or promote their operations.
It is important to consider environmental issues pertaining to technological adoption
decision of organisations. The EC examines how industry characteristics and structure
such as CP, infrastructure support and regulatory factors affect the adoption of an
innovation. Earlier studies (Chong and Ooi, 2008; Low et al., 2011; Oliveira and Martins,
2010; Wu and Subramaniam, 2009; Zhu et al., 2003) have identified factors such as
industry pressure, competitors, access to resources supplied by others and dealings
with government as significant factors of adoption. These factors are considered
important because they have substantial effect on the success of organisations.
CP according to Oliveira and Martins (2010) refers to the intensity of competition
among firms in an industry. CP in an industry sometimes precipitate adoption of a
technology as firm can derive competitive advantage through adoption of an
innovation. Thus, CP is identified as a motivator of adoption. In another strand,
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Porter and Millar (2010) posits that CP leads to IT adoption as it may alter the rules of Preliminary
competition. The resulting IT can give new ways to organisations to outperform their insight
competitors. Therefore, this study adopts CP as a determinant of CCA. Thus, into CCA
the hypothesis:
H8. CP influences CCA.
Some studies (e.g. Chong and Ooi, 2008; Pan and Jang, 2008) have asserted that trading 513
partner pressure (TPP) is significant in the adoption of an innovation. Therefore, it is
worth considering the significance of TPP in the adoption of cloud computing among
organisations. Trade partners are individuals or organisations with whom an
organisation conducts business (Anand and Kulshreshtha, 2007). Pressure from
trading partners could occur from down or upward streams of a firm. As most firms
rely on inputs and collaboration from partners to satisfy their customers, pressure from
these partners will push an organisation to adopt an innovation in order to maintain
their working relationship. Hence, it is hypothesised that:
H9. TPP influences CCA.
The last factor considered under the EC of the TOE framework is government
regulations. Government regulations come in the form of laws that seek to promote and
protect firms that adopt an innovation (Makena, 2013; Nkhoma and Dang, 2013). Cloud
computing defies geographical boundaries and is accessible in different countries.
Therefore, a legal support is deemed important to protect firms that adopt cloud
computing as laws vary from country to country. For example, if an existing law
prohibits the storage of health data outside the jurisdiction of a country, this will hinder
the adoption of cloud storage because some cloud service providers exist outside the
geographical boundaries of their users. Most cloud computing studies (e.g. Alshamaila
et al., 2013; Low et al., 2011) have ignored this construct. However, a few (Makena, 2013;
Nkhoma and Dang, 2013) have pointed out that it is important to consider the legal
regulations that support and protect CCA. Some firms may feel reluctant to adopt cloud
computing if they are not confident of a legal regulation that protect their data and
privacy (Marston et al., 2011). After consideration of this factor in the EC, the following
hypothesis is proposed:
H10. Regulatory support influences CCA.
3. Research methodology
As noted above, the aim of this study is to investigate determinants of CCA among
organisations from different industries in a developing country context. Data was
collected using questionnaires designed in line with Churchill (1979) and Straub (1989)
proposal for designing a survey instrument in order to ensure reliability and validity.
The target population was organisations operating in Ghana and the sampling
technique adopted was a stratified random sampling since it allows selection of a
balance sample of the population from the subpopulation (Hair et al., 2010). The
subpopulations were Ghanaian and foreign-owned businesses operating in Ghana. For
a respondent to be selected, it must first belong to one of these strata.
The list of companies in Ghana Club 100, firms registered on the Ghana Stock
Exchange, and multinational companies operating in Ghana were consulted for
targeted respondents. Participants were selected from the strata through a simple
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JEIM random method so that they may all have an equal chance of being selected. Data
29,4 collection was carried out from January to March 2014. In terms of positions, the
respondents from the selected organisations were IT staff and managers who have IT
knowledge of the current and future operations of their respective organisations. After
data entry and examination, 305 responses were deemed valid for further analysis. The
valid responses represented 87.7 per cent of the targeted sample.
514 The data analysis technique adopted is structural equation modelling (SEM) and
logistic regression. The data analysis was carried out in four phases, namely data
examination, demographic analysis, confirmatory factor analysis (CFA) under SEM
and logistic regression analysis. The data examination was undertaken to check for
missing data, outliers and test of normality to enable further statistical analysis of the
data set (Byrne, 2010). The demographics of participants was analysed and the results
are present in the findings below. CFA was performed to test and validate the initial
conceptual model. The CFA was conducted using AMOS version 22 on the data set to
measure the model results since CFA helps to determine if a test of validity on a
hypothesised model can be reproduced (Hair et al., 2010; Byrne, 2010). Finally, logistic
regression analysis was also performed to test the hypotheses in Section 2. The logistic
regression technique was selected among other statistical methods because the
dependent variable, CCA, was measured dichotomously.
4. Findings
4.1 Demographics of surveyed organisations
The distribution of respondents based on the industry they operate in are as follows.
The IT services sector recorded the highest percentage of participants with a
percentage of 13.4 per cent followed by educational with a percentage of 11.8 per cent
and financial and banking services with a percentage of 10.8 per cent. The rest are
government sector 9.5 per cent, media and communication 9.4 per cent, electricity,
water supply, oil and gas 7.2 per cent, travel/leisure and hospitality 6.9 per cent,
construction 6.2 per cent, real estate 5.6 per cent, manufacturing 5.2 per cent, health
care 4.3 per cent, telecommunication 3.6, mining and quarrying 3.3 per cent and other
industries 3.0 per cent. The coverage of industries shows that the data was
representative of the population.
4.2 Assessment of the overall measurement model
The analysis on the determinants of CCA in this study is organised in twofolds. First,
analysis of the measurement model validation through CFA and second, test of the
hypotheses through logistic regression analysis. According to Hair et al. (2010), a
method for examining a good representation of constructs in a conceptual model is
through validation of the measurement model. From the CFA analysis, the
second-order latent variable, TC was measured by combination of first-order latent
variables RA, SC and compatibility (C). All the three variables RA, SC and C
demonstrated strong convergent validity with standardized factor loading (FL) above
0.70 thus, were considered statistically valid to measure the TC.
In relation to the second-order latent variable, OC, two of the first-order latent
variables were found not to satisfy convergent validity. OC was measured by latent
variables, namely, FS, FSC, TMS and TR. Latent variables FS and FSC recorded
standardized FL of 0.30 and 0.37, respectively. Their FL are lower than the
recommended threshold of 0.70 hence, they were dropped from the measurement model.
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On the other hand, TMS and TS had valid FL above the recommended threshold thus, Preliminary
they were maintain during the model modification process. insight
The last second-order factor in the measurement model is the latent variable EC,
which is measured by first-order variables: CP, TPP and regulatory support (RS). into CCA
All the first-order latent variables CP, TTP and RS measuring EC recorded significant
FL of 0.91, 0.97 and 0.90, respectively, which are above the recommended threshold.
Thus, these constructs are valid for further analysis and retention in the modified 515
measurement model.
The p-value of the final measurement model was still significant despite the
modification. However, it has been argued that the significance of the p-value is as a
result of large sample size (Hair et al., 2010). Therefore, a better criterion is the ratio of
χ2 to the degrees of freedom which is 1.73 and less than the threshold of 2 in Table II
(Carmines and McIver, 1981). In terms of root mean square error of approximation, the
final model had a value of 0.05, which is in agreement with the benchmark. Therefore,
the measurement model fits the data set. The incremental fit indices obtained in the
final measurement model are greater than the threshold of 0.90 ranging from 0.97 to
0.96 while the parsimonious fit indices also recorded values of 0.82 and 0.78, which are
above the recommended value of 0.50. In relation to the test of discriminant validity, the
average variance estimate (AVE) recorded by the second-order latent variables ranges
from 0.89 to 0.92 as shown in Table III. These values are above the validity threshold of
0.50, an indication of acceptable convergent validity of the measuring scales (Hair et al.,
2010). Also, the AVE for each latent variable is higher than the square of the correlation
between that construct and any other constructs, an indication of adequate
discriminant validity between the constructs (Cable and DeRue, 2002).
Goodness-of-fit Indices Benchmark Initial model
Absolute goodness-of-fit measure
χ2 (CMIN) p⩾ 0.05 0.00
χ2/degree of freedom ⩽ 2 1.73
Absolute badness of fit measure
Root mean square error of approximation (RMSEA) ⩽ 0.08 0.05
Incremental fit measure
Comparative fit index (CFI) ⩾ 0.90 0.97
Incremental fit index (IFI) ⩾ 0.90 0.97
Turker-Lewis index (TLI) ⩾ 0.90 0.96
Table II.
Parsimony fit measure Goodness-of-fit
Parsimony comparative of fit index (PCFI) ⩾ 0.50 0.82 indices for final
Parsimony normed of fit index (PNFI) ⩾ 0.50 0.78 measurement model
Shared variance
Constructs CR AVE EC TC OC
EC 0.963 0.898 0.948 Table III.
TC 0.941 0.841 0.542 0.917 Construct, AVE and
OC 0.956 0.915 0.539 0.331 0.957 shared variance
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JEIM In conclusion, the final measurement model supported the data set with eight latent
29,4 variables out of the initial ten proposed in the conceptual framework. The factors
supported based on the TOE framework among Ghanaian organisations are RA,
SC, compatibility, TMS, TR, CP, TPP and RS.
516 4.3 Hypotheses confirmationThe steps adopted in testing the hypotheses using logistic regression are: a χ2 test for
the change in −2LL value from the base model, Hosmer and Lemeshow test of model fit,
and Wald statistic estimation. The χ2 test is used to conduct an assessment of how the
independent variables have significantly improved the model fit. According to Hair
et al. (2010), logistic regression uses the value of −2L times the log to predict the model.
The −2LL, and the R2 values of the base model are shown in Table IV. Nagelkerke R2
value of 13.2 per cent indicates the variation in the data that is explained by the logistic
regression model.
Hosmer and Lemeshow test is another means of comparing the conceptual model with
the base model (Lemeshow and Hosmer, 1982). From Table V, the Hosmer-Lemeshow
test was insignificant indicating that the conceptual model is not much different from the
base model hence, possesses a good explanatory capability.
Lastly, the level of significance and hypotheses testing of individual independent
variable is performed using the Wald test and significant values, respectively (Hair
et al., 2010). The significant values indicate that out of the ten independent variables,
six were significant in determining CCA. The result of the logistic regression in
Table VI indicates that out of the ten hypotheses proposed at the beginning of the
Step −2 Log likelihood Cox and Snell R2 Nagelkerke R2
Table IV.
Model summary 1 364.032 0.097 0.132
Table V. Step χ2 df Sig.
Hosmer and
Lemeshow test 1 10.538 8 0.229
Factors Coefficient ( β) SE Wald p-value Support for model
Relative advantage (RA) −0.534 0.261 4.176 0.041 H1: supported
Security concern (SC) 0.602 0.257 5.466 0.019 H2: supported
Compatibility (C) 0.244 0.175 1.943 0.163 H3: rejected
Firm size (FS) 0.159 0.147 1.173 0.279 H4: rejected
Firm scope (FSC) 0.055 0.195 0.080 0.777 H5: rejected
Top management support (TMS) −0.417 0.163 6.543 0.011 H6: supported
Technology readiness (TR) 0.665 0.300 4.933 0.026 H7: supported
Competitive pressure (CP) 0.423 0.208 4.139 0.042 H8: supported
Table VI. Trading partners pressure (TPP) −0.780 0.301 6.697 0.010 H9: supported
Hypotheses Regulatory support (TS) 0.150 0.203 0.549 0.459 H10: rejected
confirmation in Constant −1.020 0.845 1.457 0.227
logistic regression Notes: *po0.05; **po0.01
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study, six of them were significant. The six supported factors with p-values less than the Preliminary
threshold of 0.05 are RA, SC, TMS, CP, TPP and TR. The factors with p-values greater insight
than 0.05 and subsequently rejected by the model are FSC, FS, compatibility and RS.
Figure 2 shows the final research model with the respective significant and into CCA
coefficient values of the determinant of CCA. Among the factors with significant
p-values, TPP recorded the highest Wald value, respectively, followed by TMS, SC, TR,
RA and CP, an indication that the most important determinant of CCA in a developing 517
country context is TPP. Table VII also provides inter-item correlation of the variables
used in the study.
5. Discussion
5.1 TC
The TC initially consisted of RA, SC and compatibility. However, after the data
analysis, only two factors, RA and SC, were supported. The significance of RA in the
adoption of cloud computing is consistent with some studies (e.g. Low et al., 2011;
To and Ngai, 2006; Wang et al., 2010). However, its negative relation to the adoption
of cloud computing is inconsistent with the studies above except the findings of
Technological
Context
H1: Relative Advantage
(p<0.041) (=–0.534)
H2 : Security Concern
(p<0.019) (=0.602)
Organizational
Context
H6 : Top Management
Support (p<0.011) (=–0.417)
Cloud
H7 : Technology Readiness Computing
Adoption
(p<0.026) (=0.665)
Environmental
Context
H8 : Competitive Pressure
(p<0.042) (=0.423)
H9 : Trading Partners’
Pressure (p<0.010) (=–0.780) Figure 2.
Cloud computing
adoption model
Notes: *p<0.05; **p<0.01
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JEIM
RA C SC TMS FS FSC TR TPP CP RS CCA
29,4
RA 1.00
C 0.28 1.00
SC 0.81 0.28 1.00
TMS 0.25 0.24 0.26 1.00
518 FS 0.27 0.28 0.25 0.49 1.00FSC 0.26 0.19 0.26 0.20 0.18 1.00
TR 0.28 0.16 0.34 0.20 0.19 0.36 1.00
TPP 0.36 0.17 0.43 0.19 0.21 0.35 0.83 1.00
Table VII. CP 0.39 0.21 0.38 0.22 0.20 0.39 0.70 0.68 1.00
Items inter- RS 0.67 0.23 0.67 0.30 0.28 0.27 0.33 0.43 0.41 1.00
correlation matrix CCA 0.06 0.11 0.14 −0.05 0.06 0.09 0.17 0.09 0.19 0.10 1.00
Low et al. (2011). This negative relationship could be attributed to the technical
knowledge required to understand cloud computing, a relatively new technology, and
its complex billing mechanisms. SC also had significant impact on the adoption of cloud
computing and this is also in agreement with extant studies (e.g. Chebrolu, 2011;
Sultan, 2014; Zissis and Lekkas, 2012). These prior studies emphasised the importance
of security in the adoption of an innovation. The nature of cloud computing dictates a
critical look at security of data, systems and service providers because, a breach could
result in serious problems for adopters. Therefore, its significance as a determinant is in
the right direction. Prior studies (e.g. Oliveira and Martins, 2010; Wang et al., 2010)
pointed out compatibility as a significant determinant of cloud adoption. Conversely,
it was found to be insignificant in the adoption of cloud computing in a developing
country context. Nonetheless, the non-significance of compatibility in this study
is consistent with Low et al. (2011). The non-significance of compatibility is
explained by successful implementation of similar information systems in the past.
Thus, an organisation will then perceive cloud computing to be compatible with
existing systems.
5.2 OC
FS, FSC, TMS and TR were determinants that constituted the OC for CCA. After the
data analysis, TMS and TR were the factors found to have significant impact on the
adoption of cloud computing. The significance of TMS is consistent with other studies
(e.g. Dholakia and Kshetri, 2004; Low et al., 2011; Pan and Jang, 2008; Alshamaila et al.,
2013) as it is regarded a very important determinant in the adoption of a technology.
Business process reengineering and allocation of resources may be required as a result
of a technology adoption, thus, the backing of top management is needed. Therefore,
TMS is vital in the adoption of cloud computing. TR is another factor that had
significant impact on the adoption of cloud computing in a developing country
perspective. Its significance is in line with previous studies (e.g. Oliveira and Martins,
2010; Zhu et al., 2003). The significance of TR reiterates the point that technological
infrastructure and human resources are necessary for technology adoption.
Conversely, FS, and FSC were insignificant. This result is contrary to some extant
studies (e.g. Dholakia and Kshetri, 2004; Low et al., 2011; Pan and Jang, 2008). However,
this situation could be explained by the unique feature of “pay-per-use” in cloud
systems. Thus, providing a better alternative to monitor and control cost. Rendering
the size and scope of the firm less significant in the adoption of cloud computing.
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5.3 EC Preliminary
The EC is constituted by CP, TPP and RS. Competitive and TPPs were the significant insight
determinants of CCA while RS was insignificant. Prior studies (e.g. Oliveira and
Martins, 2010; Pan and Jang, 2008; Wang et al., 2010) also point out that competitors into CCA
and trading partners are significant determinants in technology adoption. The findings
in this study also confirm this position. However, this finding is converse to the
findings of Alshamaila et al. (2013) in which CP was found to be insignificant in CCA 519
from a developed country context. This indicates that organisations from developed
and developing contexts respond differently to CCA. IT adoption may alter the rules of
competition and give an organisation new ways to operate and outperform their
competitors. On the other hand, pressure from trading partners may force a business to
adopt an innovation in order to maintain cooperative relationship with these partners.
The finding on RS is not consistent with results from previous studies (e.g. Makena,
2013; Nkhoma and Dang, 2013; Zhu et al., 2003). However, this may be attributed to
many reasons. The first being the rate at which technological development and growth
surpasses legislation. As a result, organisations may decide that RS is not that
important in adoption. Also, cloud computing is in its infancy and yet to gain
significant consideration for policy regulation especially in developing countries.
Again, government regulations are also seen to be playing catch-up with technological
innovations in developing countries. Therefore, if organisations decide to wait for
complete legislation from governments, adoption of a new technology will become a
problem. Nonetheless, the non-significant nature of RS does not make it less important.
6. Conclusion
This study investigated the determinants of CCA among organisations from different
industries in a developing country. The study adopted the TOE framework as a
guiding lens to tackle adoption from multiple contexts. The constructs employed in the
study were tested and validated using CFA while the hypotheses were tested using
logistic regression. Given the dichotomous nature of the dependent variable (CCA),
logistic regression was chosen among other multivariate techniques to test and validate
the hypotheses proposed in relation to the ten factors. The initial conceptual model
consisted of ten variables. The results from the CFA analysis and the logistic
regression provided support for six variables out of the initial ten. The confirmed
variables are: RA, SC, TMS, TR, CP and TPP. The other four factors, namely FS, FSC,
compatibility and RS were insignificant to the model and were therefore rejected as
determinants of CCA from a developing country context.
The findings in this study once again emphases the point that technological adoption
cannot be studied from one context. Rather, TOE contexts should be considered as they
may promote or inhibit adoption. Also, the mix results in terms of determinants of CCA in
developed and developing country context points out the importance of geographical
context in technology adoption. Considering the clarifications provided by this study, it is
therefore important for organisations to pay more attention to the significant factors
especially in developing country context, particularly to a new construct security, which
most cloud computing studies have ignored.
6.1 Contribution to research, practice and policy
This study has made significant contributions to research, practice and policy.
In regards to research, this study contributes to the body of knowledge on cloud
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JEIM computing by testing and validating the TOE framework in a developing country
29,4 perspective. This is an important contribution given the existence of cultural and
societal idiosyncrasies in different countries. The study also provides empirical support
that, the adoption of cloud computing cannot be studied from just the technological or
OCs since the findings indicate the importance of environmental factors in the adoption
of cloud computing. Lastly, the study bridges the ostensible literature gap on cloud
520 computing between the developed and developing countries.
This study contributes to practice by drawing attention of practitioners to important
factors in CCA. Thus, organisations venturing into cloud computing have a fundamental
understanding of the determinants, a knowledge arguably not previously available to
developing country organisations. Therefore, firms planning to adopt cloud computing in
developing countries need to take critical look at TPP, TMS, SC, TR, RA and CP.
With regards to policy, it is noted that creating a favourable ICT environment will
positively influence the adoption of cloud computing. The enabling environment in the
form of legislation, ICT infrastructure and policies will propagate the cloud computing
agenda. This study reiterates the importance of adequate RS in the form of policies to
support cloud computing as current laws are seen as playing catch-up with
technological developments especially in developing countries.
6.2 Limitations and future research directions
Some limitations have been identified in this study therefore, future research directions
have been suggested. First, the study focused on organisations in one developing
country. Therefore, similar studies should be conducted in other developing countries
to consolidate the findings. Second, this quantitative study might not provide deep
explanation thus, future studies should consider testing the determinants in a
qualitative setting. Also, a challenge is made to both researchers and practitioners to go
beyond developing cloud applications for financial and ICT sectors and consider other
sectors with less CCA such as agriculture and manufacturing.
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About the authors
524 Prince Kwame Senyo lectures in the Department of Operations and Management Information
Systems, University of Ghana. He is also a PhD Student at the University of Reading, UK.
His research interests are digital business ecosystems, cloud computing, emerging technologies,
organisational semiotics and applied informatics. Prince also doubles as the Creative Director
and the Founding Partner of Black Decimal LLC. Prince Kwame Senyo is the corresponding
author and can be contacted at: pksenyo@ug.edu.gh
Dr John Effah holds a PhD in Information Systems and is currently the Head of Department of
Department of Operations and Management Information Systems. He lectures courses in e-Business,
Information Management, Business Information Systems and Systems Analysis and Design at the
MBA, MPhil and Executive MBA Levels. Dr Effah has published in journals such as Journal of
Enterprise Information Management, African Journal of Information Systems, Journal of Information
Systems in Developing Countries, and has also presented papers at international conferences. Prior to
joining the University, he worked with Deloitte, a global Management Consulting and Accounting
firm. Dr Effah has extensive expertise in IT/IS practice and consulting and has a rich experience in
managing both international and national ICT projects across the private as well as the public sector.
Dr Erasmus Addae is currently the Dean of Distance Learning and Virtual Campus at the
South Texas College and serves as an Adjunct Professor at the Collin College, University of
Texas, Brownsville. He is a Member of the Chief Learning Officer Business Intelligence Board.
He received his Bachelor’s Degree from the University of Ghana; Masters in Information Systems
from the Tarleton State University Texas, USA and a PhD in Educational Computing from the
Nova Southeastern University in the USA.
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