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Association for Information SystemsAIS Electronic Library (AISeL)
PACIS 2013 Proceedings Pacific Asia Conference on Information Systems(PACIS)
6-18-2013
An Integrated Theoretical Framework for CloudComputing Adoption by Small and Medium-SizedEnterprisesAmin SaediUniversiti Teknologi Malaysia, [email protected]
Noorminshah A. IahadUniversiti Teknologi Malaysia, [email protected]
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Recommended CitationSaedi, Amin and Iahad, Noorminshah A., "An Integrated Theoretical Framework for Cloud Computing Adoption by Small andMedium-Sized Enterprises" (2013). PACIS 2013 Proceedings. 48.http://aisel.aisnet.org/pacis2013/48
AN INTEGRATED THEORETICAL FRAMEWORK FOR CLOUD
COMPUTING ADOPTION BY SMALL AND MEDIUM-SIZED
ENTERPRISES
Amin Saedi, Department of Information Systems, Faculty of Computing, Universiti
Teknologi Malaysia, Malaysia, [email protected]
Noorminshah A.Iahad, Department of Information Systems, Faculty of Computing,
Universiti Teknologi Malaysia, Malaysia, [email protected]
Abstract
Due to socio-technical reasons, cloud services are usually deployed in a heterogeneous network
where both human and non-human actors are equally important in the process of technology
adoption. Close examination of Information Systems adoption and diffusion theories shows that
human and non-human actors, as the major elements in every heterogeneous network, cannot be fully
integrated into the current adoption and diffusion theories. Thus, this research is aimed at exploring
other theories in depth, and accordingly proposing a new integrated theoretical framework for cloud
computing adoption in general, particularly in Small and Medium-Sized Enterprises.
Keywords: Cloud Computing, adoption, Technology-Organization-Environment framework, Actor
Network Theory.
1 INTRODUCTION
The term, “Cloud Computing” (CC) can be explained in two parts. Firstly, it involves using a web
browser on the Internet to dynamically allocate or de-allocate the access of the remote computing
resources based on the users’ demands (Naone 2007). The second part refers to paying for the real use
of the computing resources and facilities (Hoover & Martin 2008; Kim et al. 2009). The first part
indicates that online users do not need to occupy the servers or storage in peak time use (Misra &
Mondal 2011). Conversely, this advantage allows those servers and storage to be vacant for most of
the time (Kim et al. 2009). Moreover, users do not need to access the computing resources in their
premises or allocate a space for them, nor do they need to pay for electricity consumption and
maintenance on computing resources (Kim et al. 2009). CC promises to deliver all Information
Technology (IT) services on-demand whereby enabling clients to pay only for the specific amount of
resources they actually use or in other words, follow the pay-as-you-go pricing model (Benton 2010a;
Khajeh-Hosseini et al. 2010). Consequently, to reduce clients’ operating costs, users can simply rent
servers, data storage and applications based on their requirements (Repschläger et al. 2011).
CC comprises three service models, including Software as a Service (SaaS), Platform as a Service
(PaaS), and Infrastructure as a Service (IaaS) (Goscinski & Brock 2010; Low et al. 2011; Wu 2011).
In SaaS, cloud service providers offer their software applications via the Internet (Sultan 2010). In
comparison to conventional IT solutions, in SaaS, there is no need to download and install any
software applications (Jain & Bhardwaj 2010). However, the cloud service providers rent out their
software applications over the Internet (Benton 2010a; Goscinski & Brock 2010; Khajeh-Hosseini et
al. 2010; Low et al. 2011; Wu 2011). Thus, individuals, firms and organisations pay for their services
on-demand, based on a subscription pricing model (Benton 2010a). At the platform level, CC service
providers offer application developers which are nearly identical to the traditional desktop settings
(Benton 2010a). “The emergence of such platforms allows independent software vendors and IT staff
to develop and deploy online applications quickly using third-party infrastructure” (Benton 2010a, p.
3). In IaaS, the CC service providers offer the on-demand raw computing resources to the user
(Goscinski & Brock 2010). Unlike traditional hosting services, which provide dedicated hardware to
customers, IaaS systems accommodate fluctuating demand from different user (Low et al. 2011).
Therefore, greater elasticity and cost advantages compared to traditional data centers are provided
(Benton 2010a).
CC includes three forms: public, private and hybrid cloud (Das et al. 2011; Marston et al. 2011).
Private clouds are on-premises clouds which are built inside the firm’s own firewall (Benton 2010b).
In other words, private clouds are considered as the internal clouds for firms and can be accessed by
users in various departments of the enterprise (Kim et al. 2009). Therefore, individual business units
will pay the IT department for these remote computing services (Low et al. 2011). In comparison,
public clouds are referred to as the off-premises clouds where their IT infrastructures are built outside
of the enterprises’ own firewall (Marston et al. 2011). Hybrid clouds are a combination of public and
private clouds (Khajeh-Hosseini et al. 2010) whereby “typically, non-critical information is
outsourced to the public cloud, while business-critical services and data are kept within the control of
the organization” (Marston et al. 2011, p. 180). To incorporate the benefits of public clouds with the
privacy and security of private clouds, most of the firms are expected to deploy hybrid clouds which
will enable enterprises to transfer part of their IT services to the public cloud, while the rest are
maintained internally (Benton 2010a; Khajeh-Hosseini et al. 2010; Low et al. 2011).
According to National SME Development Council (NSDC) (2005), there are no common definitions
for Small and Medium-Sized Enterprises (SMEs) in Malaysia, with different agencies describing
SMEs based on their own criteria and benchmarks. However, in June 2005 NSDC published standard
definitions for SMEs to be adopted by all government ministries, agencies and financial institutions in
Malaysia. According to NSDC, the coverage of definitions for SMEs in Malaysia is based on two
main criteria: (1) number of employees and (2) annual sales turnover. As such, enterprises are
classified as SMEs if they meet either the specific number of employees or annual sales turnover
criteria. Moreover, the definitions apply for the following sectors including (a) primary agriculture,
(b), manufacturing (including agro-based), (c) manufacturing-related services (MRS) and (d) services
(including Information and Communications Technology).
Sector
Size
Primary Agriculture
Manufacturing (including
AgroBased) & MRS
Services Sector
(including ICT)
Micro < 5 employees OR Less
than RM200,000
< 5 employees OR Less
than RM250,000
< 5 employees OR Less
than RM200,000
Small
5 <employees< 19 OR
Between RM200,000 &
less than RM1 million
5 <employees< 50 OR
Between RM250,000 & less
than RM10 million
5 <employees< 19 OR
Between RM200,000 &
less than RM1 million
Medium-sized
20 <employees< 50 OR
Between RM1 million &
RM5 million
51 <employees< 150 OR
Between RM10 million &
RM25 million
20 <employees< 50 OR
Between RM1 million &
RM5 million
Table 1. The approved SME definitions in Malaysia based on number of full-time employees
and annual sales turnover
As shown in table 2, for example, in terms of services sector (including ICT), a micro enterprise has
less than 5 full-time employees or an annual sales turnover of less than RM200,000. A small
enterprise has between 5 and 19 full-time employees or an annual sales turnover between RM200,000
to RM1million. A medium enterprise has 20 to 50 full-time employees or an annual sales turnover
between RM1million and RM5 million.
2 LITERATURE REVIEW
2.1 Background of the Problem
Theoretically, CC provides vast opportunities for all organizations and enterprises, including SMEs,
to have more flexible and easy-running business models (Benton 2010b). Thus, it would seem that
these organizations could migrate to CC extremely easily. However, in practice, there is currently a
lot of debate among all enterprises regarding CC adoption (Khajeh-Hosseini et al. 2010) and a
considerable debate exists among organizations which are handling sensitive data (Kim et al. 2009).
SMEs are the organizations in which the sensitivity of data they are handling is high (Misra & Mondal
2011). Financial data of companies, quotations for various tenders, company databases, on-going
confidential research, trade secrets, drug formulas, early research findings and email accounts are
some vivid examples of very sensitive data which are handled by SMEs (Misra & Mondal 2011). As a
result, for those organizations including SMEs there is a big decision as to whether to embark on the
cloud or remain with their own interior IT infrastructures (Benton 2010b). In terms of concerns, a
report by Catteddu & Hogben (2009) reveals that the primary reasons with regard to avoiding capital
expenditure in clouds, include privacy, security risks, availability and integrity of services and/or data,
and confidentiality of corporate data. A survey of 349 German companies by Benlian & Hess (2011)
indicates that security threats are the major issue influencing IT executives' overall risk perception.
Wu et al. (2011) indicate that data security is one of the major hindrances when using SaaS. Similarly,
a study by Misra & Mondal (2011) claims that concern for data security is one of the major issues
facing firms who adopt and migrate to CC. According to Koehler et al. (2010, p. 2), “security and
reliability concerns arose and are the major obstacles for the wide adoption of cloud computing.” As
such, due to the high levels of data sensitivity handled by SMEs (Jain & Bhardwaj 2010; Misra &
Mondal 2011), there are several barriers and concerns which discourage the use of cloud-based
systems. In addition, CC is not just referred to as a technological enhancement of datacenters but also
a principle and profound change in how IT is provisioned and deployed (Creeger 2009). Therefore,
organizations including SMEs need to consider both the benefits and risks of CC in order to make a
better decision regarding their adoption (Khajeh-Hosseini et al. 2010). According to Sullivan (2009),
CC adoption cannot occur immediately and it is predicted that it might take around 10 to 15 years
before firms make this trend.
Innovation adoption is the most significant factor that impact on organizations’ competitiveness and
performance (Choudrie & Dwivedi 2005). Due to the small size of SMEs, limited managerial abilities
and limited resources will result in a challenging task in innovation adoption (Brammer et al. 2011;
Caldeira & Ward 2002). SMEs as compared to larger organizations are weakly structured and have
low market power with insufficiency of resources to appreciate the benefits of innovation (Galligan &
Mansor 2011). In terms of CC adoption, according to Galligan & Mansor (2011), a survey by
Microsoft Sponsored Springboard in 2011 reveals that although 62 per cent of larger Asian enterprises
with more than 500 PCs have eagerly embraced CC services or were planning to adopt CC, 68 per
cent of SMEs with less than 50 PCs were lagging behind larger enterprises and had no plans to adopt
CC. This will result in a wasted opportunity for some Asian cultures, including Malaysia, that might
influence competitiveness for the country (Galligan 2011). Additionally, this shows the SMEs’ lack of
confidence in their knowledge of the cloud compared to large organizations (Kwang 2011). Galligan
(2011) attributed this big difference to the fact that IT experts in large enterprises have a greater
chance of being provided with training and education to equip them with cloud-related skills. In
addition, a lack of internal IT professionals and the unwillingness to embrace new IT offerings will
result in the low uptake of CC among SMEs (Kwang 2011). As such, it can be concluded that the
larger the enterprise, the higher its confidence in CC.
Adoption of CC is still in its infancy (Khajeh-Hosseini et al. 2010; Saya et al. 2010). Many studies in
the field of Information Systems (IS) have investigated significant factors influencing the adoption of
new technologies or service solutions (Marston et al. 2011; Saya et al. 2010). However, research on
CC adoption seems to be one of the less explored and examined topics in the IS domains, particularly
for SMEs (Wu et al. 2011). According to Saya et al. (2010), most of the literature on CC has widely
focused on CC architecture (Rochwerger et al. 2009 ), potential applications (Liu & Orban 2008), and
CC costs and benefits (Assuncao et al. 2009). In addition, much of the previous research on CC
adoption in the SME context has focused only on a particular type or service model of CC. For
example, a research study by Wu et al. (2011) has focused solely on adoption of SaaS by
organizations. However, as discussed earlier, CC can be delivered in several forms and service models
and therefore, a reasonable research on CC adoption can only occur when all CC forms and service
models are considered together in the one study. As such, inadequate theoretical frameworks for CC
adoption in general, and the lack of a comprehensive theoretical framework for SMEs, with
simultaneous consideration of all CC forms and service levels in particular, reflect a fundamental need
to further explore the adoption of CC by SMEs (Benton 2010b).
CC adoption is a different scenario compared to conventional innovation adoption (Feuerlicht 2010).
CC technology, in contrast to other innovations, consists of three major players including cloud-based
services, cloud users (clients), and cloud service providers (Dargha 2009). Therefore, in terms of CC
adoption, external factors (environmental factors) including the role of third parties (e.g. cloud based
service providers), and organizational factors such as the firm’s size, are as important as internal
factors (e.g. cloud’s technological characteristics) (Feuerlicht 2010; Low et al. 2011). Many previous
research studies in CC adoption have focused only on the technological aspects of adoption in this
emerging innovation (Low et al. 2011). For example, they have focused exclusively on areas such as
technology complexity, technology compatibility, security requirements, and future expectancy (Low
et al. 2011). However, because of the nature of socio-technical influences in cloud-based services,
there is a fundamental need to further explore how environmental and organizational factors, as well
as technological factors, may influence the adoption of CC by SMEs (Low et al. 2011). Wang et al.
(2010, p. 7) state that “some aspects of business to business adoption are missed if viewed only from a
technological perspective.” Low et al. (2011) believe that environmental and organizational factors
affecting CC adoption vary across different organizational contexts. Nevertheless, despite a wide
range of theories in IS discipline, there is no single study which has explored and examined the role of
environmental factors and mediated the role of institutional influences such as competitors for CC
adoption by SMEs.
Theories of adoption in IS discipline are aimed at understanding, explaining, or predicting how, why
and to what extent individuals, firms or organizations will adopt and agree to deploy a new
technology (Choudrie & Dwivedi 2005). Examination of IS adoption and diffusion innovation
theories shows that the role of organizational and environmental factors are not fully integrated in
most adoption/diffusion theories (Frambach 1993; Parker & Castleman 2009). As such,
adoption/diffusion theories cannot fully integrate the role of cloud users as an adopter of CC and the
role of CC service providers as a provisioner of innovation to organizations including SMEs.
Moreover, based on the socio-technical aspects of CC services, cloud-based solutions are usually
deployed in a heterogeneous network. A network in which numerous dissimilar elements called
actors/actants such as human or non-human, social or technical entities are equally important to
recognize the processes of technological innovation (Tatnall & Burgess 2002). Thus, there are
reasonable motivations for researchers to explore other theories in-depth, such as the Technology-
Organization-Environment (TOE) framework and Actor Network Theory (ANT), and accordingly
propose a comprehensive theoretical framework for the adoption of CC by SMEs.
Based on the study which has been done in the background of the problem, the main research question
for this study is:
How can CC be adopted by SMEs?
The following sub-research questions are framed to answer the research problems.
1. How can technological, organizational and environmental factors in a heterogeneous
network influence the adoption CC by SMEs?
2. What are the barriers of CC adoption by SMEs?
3. What is the theoretical framework for adoption of CC by SMEs?
2.2 Prior Research on Cloud Computing Adoption
CC adoption refers to the acceptance and agreement to use cloud-based services as a new way of
deploying technology (Marston et al. 2011). A new technology or a new service solution results in
improving an organization’s competitiveness. Thus, adoption of a new technology or a new service
solution is an eternal hot topic among scholars, firms, and organizations (Wu et al. 2011). Many
studies have investigated significant factors influencing the adoption of new technologies or service
solutions. However, CC adoption seems to be one of the less explored topics in the IS domains
(Marston et al. 2011; Saya et al. 2010). Moreover, developing a theoretical framework is less explored
and examined for CC adoption. Most of the literature has focused on CC adoption regardless of
considering CC service models and forms. However, each cloud service model and type have their
own privacy and security issues (Catteddu & Hogben 2009; Subashini & Kavitha 2011).
Based on the literature, a considerable number of factors can affect the adoption of CC. For example,
Misra & Mondal (2011) believe that the size of IT resources, the utilization pattern of the resources,
data sensitivity, and the criticality of work done by organizations affect the adoption of CC in
organizations. A survey of 101 IT professionals by Saya et al. (2010) shows that institutional
influences such as perceived accessibility, perceived scalability, perceived cost effectiveness, and a
perceived lack of security can increase the intention to adopt CC. Another survey by Wei-Wen
(2011) indicates that factors including social influence, perceived usefulness, and security and trust
influenced adoption of SaaS for Taiwanese vendors and enterprises. A research by Benlian & Hess
(2011) analysed the opportunities and risks of SaaS adoption as perceived by IT executives at adopter
and non-adopter German companies. According to Benlian & Hess (2011), cost advantages through
SaaS are perceived as most salient benefit.
2.3 Technology- Organization-Environment Framework
Technology-Organization-Environment framework or model (TOE) is proposed by DePietro et al.
(1990) to analyze adoption of technological innovations by firms and organizations (Melville &
Ramirez 2008). The TOE framework posits that adoption of IT technology by firms and organizations
is influenced by three different context groups: technological, organizational, and environmental
contexts (DePietro et al. 1990; Melville & Ramirez 2008).
The technological context refers to the characteristics of innovation such as availability, complexity,
and compatibility which significantly affect adoption of innovation (Low et al. 2011; Melville &
Ramirez 2008). In addition, the technological context is related to both internal/external and to
existing/new technologies which are relevant to the firms or organizations (DePietro et al. 1990;
Doolin & Troshani 2007). The organizational context refers to the characteristics of an organization
such as size, the degree of complexity in managerial structure, degree of formalization, human
resources, amount of slack resources, and linkages among employees (DePietro et al. 1990; Low et al.
2011). For example, Zhu et al. (2003) note that large organizations as compared to SMEs may have
more financial resources to invest in IT innovation and adoption. The environmental context includes
structure of the industry, competitors, and government’s regulations and policies (DePietro et al.
1990). In fact, within this context, the relationship between organizations and trading partners,
competitors, government, pressure from trading partners, and industry community may affect
adoption decisions (DePietro et al. 1990; Melville & Ramirez 2008). For example, to obtain
competitive advantages in the marketplace, the more intense the competition in a business, the more
pressure is on an organization to adopt a new innovation and technology (Doolin & Troshani 2007).
There are several reasonable motivations which make TOE framework feasible for CC adoption. CC
adoption is a different scenario to conventional innovation adoption and diffusion (Feuerlicht 2010).
CC services are usually offered to firms and organizations by a third party (cloud service provider).
Thus, CC technology compared to other conventional innovations, consists of three foremost players:
cloud-based services, cloud users (clients), and cloud service providers (Dargha 2009). As a result,
adoption of CC is influenced by three major factors which include the characteristics of CC
technology as a technological context, the characteristics of firms and organizations as an
organizational context, and the characteristics of a third party as an environmental context (Low et al.
2011). Most prior studies have only identified the technological determinants of CC adoption (Low et
al. 2011). However, because of the nature of socio-technical factors in cloud-based services,
organizational and environmental factors are equally as important as technological factors (Feuerlicht
2010; Low et al. 2011). As discussed earlier, the TOE framework explains the adoption of technology
through three elements: technological, organizational, and environmental contexts. Therefore, TOE
framework compared to other adoption and diffusion theories is a much more relevant analytical tool
to classify all determinants of CC adoption in technological, organizational, and environmental
contexts. In addition, The TOE framework is a useful analytical tool for explaining the adoption of
innovation by firms and organizations (DePietro et al. 1990).
2.4 Actor Network Theory
Actor Network Theory (ANT) is a sociological theory developed by Callon & Latour (1981) to
recognize the processes of technological innovation in a heterogeneous network (Callon 1986; Latour
1986; Latour 1996; Law & Callon 1988). The heterogeneous network is a coextensive network
comprising a range of dissimilar elements called actors/actants (Tatnall & Burgess 2002). ANT claims
that (1) actors, including human or non-human (social or technical) entities are equally important to a
network (Law 1992), (2) the actors are treated as inseparable by ANT (Dolwick 2009), and (3) the
interactions and associations between the actors and networks are the key issue (Tatnall & Burgess
2002). As such, ANT deals with the socio-technical situations in which there are no distinctions
between human or non-human (social or technical) actors (Kennan et al. 2010). Neither social nor
technical elements are favored in the network (Kennan et al. 2010). For example, Tatnall & Burgess
(2002), by employing ANT in a socio-technical situation involving technological innovation, believe
that human actors (e.g. customers, programmers, and development managers) and non-human actors
(e.g. computers, modems, telephone lines, and web development tools) are equally important to
implement a business-to-business e-commerce portal for regional SMEs in Melbourne, Australia.
The ANT approach is conceptually beneficial in helping to appreciate the complexity of an
organization’s network, the fluidity of this network, and the vigorous role of technology in different
contexts (Cresswell et al. 2010). This can demonstrate an understanding of how social influences
(Datta 2011) are generated as a result of associations between different actors in a network (Linderoth
2010). Literature on CC shows that theory which aims to explain the CC adoption decisions of
organizations needs to consider a complex network and relationships among owner-managers,
employees, and external parties which may influence the decision of organization owner-managers
(Saya et al. 2010). The decisions made in the adoption of CC in organizations such as SMEs are very
complex and involve many actors, both human and non-human. In other words, rather than
characteristics of technology itself (non-human actors), human issues also determine how
organizations may adopt and migrate to CC (Datta 2011; Low et al. 2011). This means that complex
networks in organizations benefit from being informed by ANT perspectives (Cresswell et al. 2010)
and ANT offers a suitable framework for analyzing CC adoption by organizations including SMEs.
3 METHODOLOGY
Technology adoption and diffusion research within the IS domains is usually studied at two different
levels, these being organizational level and user level (Chan & Ngai 2007; Choudrie & Dwivedi
2005). Moreover, technology and innovation adoption studies must consider the context of the study,
stage of adoption, and the feasibility of deploying specific methods in their research design (Choudrie
& Dwivedi 2005). Since SMEs were targeted in this study, the researcher chooses organizational level
for analysis. The normal methods used in ANT involve qualitative data collection techniques such as
interviews, document analysis and other methods used in ethnography (Deering et al. 2010; Kennan et
al. 2010; Tatnall & Burgess 2002). Therefore, in this research, the qualitative method has been used to
1) uncover the concepts, constructs, and a set of human and non-human actors and 2) generate the
hypothesis and as a result, (3) propose a theoretical framework for adoption of CC by SMEs. In terms
of data collection activity, in-depth interviews (open-ended questions) have been conducted on a
group of three Malaysian SMEs selected as the case study across ICT sector that were willing to adopt
or have already adopted CC. According to Benbasat et al. (1987), a case study is a feasible IS research
strategy when (1) the area of the research is in its infancy and minimal previous studies have been
carried out, (2) the aim of the research is to explore, classify and develop hypothesis, (3) a set of
independent and dependent variables may not have been stipulated by the researcher in advanced, (4)
the researcher intends to generate and develop theories, (5) answers to “how” and “why” questions are
sought which shows the complexity of the process, and (6) the research focuses on contemporary
events.
After collecting data from the interview phase, the researcher then analyzed the data. There are two
different types of data analysis in the case study strategy and these are within-case analysis and cross-
case analysis (Yin 2009). A within-case analysis is suitable when a single-case is involved in the
research and/or the researcher intends to compare different theories used and the data gathered from
the single-case (Yin 2009). However, a cross-case analysis occurs between multiple-case designs to
find out the similarities and differences and where the aim of the study is to enhance generalizability
and to enrich understanding (Yin 2009). Therefore, due to the number of SMEs involved in this study,
the researcher performed a cross-case analysis (using Nvivo Software) to analyse the qualitative data
and to find the similarities and differences between these three cases. The findings of the interview
sessions and literature review were then used to develop an initial theoretical framework for CC
adoption by SMEs.
4 RESEARCH THEORETICAL FRAMEWORK
Figure 1 illustrates an initial integrated theoretical framework for adoption of CC by SMEs. As
discussed earlier, the theoretical framework is proposed through cross-case analysis of three SMEs
selected as the case study and enfolding literature. Moreover, the framework is founded by two
theories, including TOE framework and ANT. In the TOE framework, the factors of and the barriers
to CC adoption are categorized into three contexts such as technology, organization and environment.
In the ANT, the actors first are uncovered and then classified as human and non-human. Later, the
factors of and the barriers to CC adoption are shown as some properties of the actor relating to them.
According to Tatnall (2012), ANT is quite flexible in what is called an actor and what might be a
property of an actor; there are no set views in ANT on diagrammatic representations. Therefore,
researchers really cannot be wrong about how they do this as long as they do not contradict ANT
principals. As shown in figure 1, cost-savings, relative advantages, compatibility and accessibility as
the technological context, SMEs’ size and top manager’s intentions as the organizational context and
service-level agreement, suppliers’ competences, government supports, competitors’ pressures,
friends and family members’ advises, IT specialists and consultants’ advises and business network’s
advises as the environmental context, accelerate the adoption of CC by SMEs. However, lack of data
security, lack of data privacy and size of IT resources hindrance CC adoption.
* Barriers to CC adoption
Technology Organization Environment
Cloud Computing Adoption
Cloud Computing
Cost-savings Relative advantages Compatibility Accessibility Lack of Data Security* Lack of Data Privacy*
SME Size Size of IT Resources*
Top Manager Intentions
Supplier Service-Level agreement Competencies
Government Supports
Competitor Pressures
Friends and Family Members Advises
IT Specialist and Consultants Advises
Business Network Advises
Actor
Properties
Non-Human Actors
Human Actors
Figure 1. An initial integrated theoretical framework for adoption of CC by SMEs
5 DISCUSSION AND CONCLUSION
The term CC is referred to as “a fundamental change in the way IT services are invented, developed,
deployed, scaled, updated, maintained and paid for” (Marston et al. 2011, p. 176). Based on the
research questions and objectives, this study offers a new CC adoption framework to both SMEs and
CC service providers. For SMEs, similar to any new technology, early adopters are able to gain more
advantages compared to late adopters (Benton 2010a). In terms of CC service providers, it is also
crucial to determine how to influence organizations’ adoption decision and consequently, understand
how to convince them to migrate to cloud solutions (Saya et al. 2010). According to Benlian & Hess
(2011), CC service providers must consider factors that should be prioritized or avoided when
offering CC services to firms at different stages of their technology adoption lifecycle.
By conducting in-depth interviews with the three Malaysian SMEs selected as the case study, and
through cross-case analysis and enfolding literature, the researcher uncovered initial concepts,
constructs, a set of preliminary detriments influencing CC adoption and a set of barriers to CC
adoption. As a result, an initial integrated theoretical framework for adoption of CC by SMEs was
founded by TOE framework and ANT. While the research almost has reached its aims, there are some
unavoidable limitations. First, in-depth interviews was conducted only on a small sample size.
Therefore, to generalize the results for larger groups and to generate research hypothesis, the study
must involve more participants. Second, the initial research framework is still untested. Thus,
developing an instrument for survey and testing of the research theoretical framework is crucial for
future research.
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