DRIVERS FOR STRATEGIC CHOICE OF CLOUD
COMPUTING AS ONLINE SERVICE IN SMES
Research-in-Progress
Min Li
University of Science and Technology
of China-City University of Hong Kong
Joint Advanced Research Center
166 Renai Road, Dushu Lake Higher
Education Town, Suzhou, China
[email protected]
Yan Yu
Key Laboratory of Data Engineering
and Knowledge Engineering, MOE,
Renmin University of China
59 Zhongguancun Street, Haidian
District, Beijing, China
[email protected]
J Leon Zhao
Department of Information Systems,
City University of Hong Kong
Tat Chee Avenue, Kowloon, Hong
Kong SAR
[email protected]
Xin Li
Department of Information Systems,
City University of Hong Kong
Tat Chee Avenue, Kowloon, Hong
Kong SAR
[email protected]
Abstract
Cloud Computing Service (CCS) paradigm is changing IT strategy of organizations in
the digital world. CCS that requires few upfront investments and uses lease-based
pricing is especially relevant to the Small and Medium Enterprises (SMEs), which have
limited resources and may not know their true valuation for the IT prior to adoption.
Thus, this research aims to investigate the influential factors of SMEs’ strategic choice of
CCS as online service. Relying upon Technology-Organization-Environment (TOE)
paradigm, we identify both generic and context-specific factors from the three aspects
and explain how the identified factors affect SMEs’ CCS strategic choices. We hope this
research can make contributions to innovation diffusion theory and IT strategy
literature. We also hope the research with progress going on can generate insights for
the CCS vendors who care about the sector of SME as well as the government
administrators to make appropriate policies or supports for SMEs.
Keywords: cloud computing, online service, TOE paradigm, IT strategy, innovation diffusion
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Introduction
Cloud computing is a model which enables ubiquitous, convenient, and on-demand network access to a
shared pool of computing resources with minimal management effort, provider-user interactions and time
(Mell and Grance 2009). Since some researchers also use cloud service or Cloud Computing Service (CCS)
instead of cloud computing (Anandasivam et al. 2010; Pearson 2009), we choose to use CCS in this paper
as the synonym of cloud computing and cloud service. As a new type of information service, CCS model
has three properties: 1) rapid release capability. A large pool of computing resources allows clients to
access services in a short time; 2) zero or a small scale of upfront investments for clients; 3) pay-for-use
payment mechanism which is flexible for clients (Armbrust et al. 2010). The fast response and flexibility of
CCS may lead to a new round revolution in information technology strategy for organizations, especially
for the Small and Medium Enterprises (SMEs). The CCS market has been estimated promising. In March
2009, Gartner forecasted that the worldwide cloud market was estimated to reach $150.1 billion in 2013
(Ben et al. 2009). Another survey by Gartner in 2012 showed that almost every CIO shows great interest in
cloud computing and have the potential to move into the cloud (Peter 2012).
The CCS is basically a type of information service. Information service vendors provide business services
to customers with the support of different software services and/or IT artifacts (Mathiassen and S rensen
2008). According to the varieties and levels of the end-user usage, CCS can be categorized into three
groups: 1) IaaS (Infrastructure as a Service); 2) SaaS (Software as a Service); 3) PaaS (Platform as a
Service) (Durkee 2010; Mell and Grance 2009). Although CCS may make clients have special concerns on
data security, its pay-for-use pricing gives the clients an opportunity to make sequential adoption
decisions. This is especially relevant when customers, especially in the SME sector, do not know their true
valuation for the infrastructure, software or platform in the cloud prior to adoption, as they can learn their
valuation through an initial usage without making a significant upfront investment and make more
informed adoption decisions later on (Xin 2009).
The emergence and fast dissemination of CCS have salient implications for SMEs’ IT strategy. On the one
side, SMEs are demanding the advanced information services to compete in the global marketplace. SMEs
also need to do business with large companies, thus they have to integrate with sophisticated information
technologies used in those large companies. On the other side, SMEs most often have limited IT resources,
face various uncertainties, and thus may not be willing to invest in the perpetual information technology
infrastructure and systems in one snapshot. As such, CCS provides opportunities for SMEs to make more
proactive IT strategy of adopting advanced information systems while with lower transaction cost and
switching cost. However, seldom has research systematically investigated the influential factors affecting
SMEs’ strategic choice of CCS. Additionally, most relevant research in IS field is about specific IT
implementation (Choi et al. 2010; Tsai et al. 2010; Van Everdingen et al. 2000), with little empirical
research in the strategic choices of different modes of IT enabled services in organizations, especially in
SMEs.
In this research, we aim to investigate the influential factors of SMEs’ strategic choice of CCS. We rely
upon the Technology-Organization-Environment (TOE) paradigm and identify the technological,
organizational, and environmental characteristics that potentially affect SMEs’ strategic choice of IaaS,
SaaS, and PaaS, or maintaining a non-CCS strategy. We include new factors such as data security that are
specific for the new CCS context. We also draw on a spectrum of relevant theories including innovation
diffusion theory, resource-based view (RBV), production cost theory and institutional theory to explain
why the identified factors can affect SMEs’ strategic choice of CCS.
From ASP to CCS
Application Service Provider (ASP) model as an enterprise software business model emerged in the late
1990s. ASP is defined by ASP Consortiums as an organization that “manages and delivers application
capabilities to multiple entities from a data center across a wide-area network (WAN)” (Susarla et al.
2003). ASP model brings user companies a low-costing and more flexible way to obtain information
technology services. However, ASP model has a critical problem with customization (Xin and Levina
2008). The requirement of customization from different clients influences the efficiency of vender’s work
because clients cannot do customization on their own side.
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Thus, the SaaS model emerged and solved the efficiency problem by allowing clients to do certain extent
customization at the meta-data layer (Xin and Levina 2008). SaaS is defined as a new software delivery
model in which firms use applications owned and maintained by software vendors based on pay-for-use
mode, instead of buying a software license (Dubey and Wagle 2007).
Following the SaaS, CCS has appeared. ASP sets the stage for SaaS, and then SaaS sets the stage for CCS. It
is an evolving process. Compared with ASP and SaaS, CCS is a broader conception, including application
services and all infrastructures or platforms that support those application service supplies. The supply
model of information technology services to organizations evolves with the development of information
technology and requirement of a more efficient way to improve information technology capability.
Theoretical Background and Research Framework Development
The TOE paradigm (Mishra et al. 2007; Tornatzky and Fleischer 1990) identifies three broad aspects, i.e.,
technological, organizational and environmental aspects which have impact on IS related decisions and
the uses of technological innovations in a firm. There are some IS studies based on TOE framework. Zhu
and Kraemer (2005) rely on the TOE framework to investigate the post-adoption variation in usage and
value of e-business by organizations (Zhu and Kraemer 2005). The explorative study of organizational
level IS discontinuance by Furneaux and Wade (2011) also conforms to the TOE paradigm (Furneaux and
Wade 2011).
The technological aspect refers to technology related to organizations (Tornatzky and Fleischer 1990; Zhu
and Kraemer 2005). Reliability as one attribute of technology is especially important in the context of
cloud computing where transactions take place in online environment. Additionally, CCS as a new type of
information service can be considered as the type III innovation in Swanson’s (1994) typology (Swanson
1994). Thus, technological factors can be investigated based on innovation diffusion theory. According to
the innovation diffusion theory (Rogers 1995), the CCS complexity, compatibility, trialability, results
observability, and the relative advantages that CCS can bring to organizations will affect the wide
acceptance of CCS in organizations. Meanwhile, the customization ability and data security might be the
major concerns of CCS clients (Marston et al. 2011; Xin and Levina 2008), since CCS vendors are more
likely to provide standardized service packages to the homogeneous clients and the data installed in an
invisible cloud increases the risk for the client firms. Thus, the customization, data security, incorporated
with the support cost constitute to the cost for SMEs to justify the appropriateness of making a CCS
strategy and its depth and width in the firms.
The organizational aspect refers to the organizational readiness and the amount of slack resources
available internally (Zhu and Kraemar 2005; Tornatzky and Fleischer 1990). According to the RBV of
firms, the capabilities of firms vis a vis their transaction partners are important determinants of sourcing
decisions (Barney 1999; Van de Ven 2005). Such capabilities are proximately shaped by descriptive
measures about the organization such as business scope and size. RBV also implies that if the internal IT
competence is strong, then the firm benefits less from accessing vendors’ generalized IT competencies
(Levina and Ross 2003).
The environmental aspect refers to the arena in which business is conducted, such as the industry,
competitors, and government (Zhu and Kraemar 2005; Tornatzky and Fleischer 1990). On the CCS vendor
side, the CCS market maturity that is indicated by the availability of CCS vendors or other support
capabilities is important for client firms to adopt or continue to use the service. On the CCS client side, the
client’s need for organizational legitimacy fosters the emergence of norms and practices that prove the
innovation diffusion. According to the institutional theory (DiMaggio and Powell 1983), the external
influences such as the regulatory support from government and the social influence from the peer
organization’s successful experiences will generate the normative and mimetic pressures for the client
firms to adopt CCS strategy.
Thus, relying on TOE paradigm and the relevant theories, we develop a research framework as shown in
Figure 1. The model links the technological, organizational and environmental factors to SMEs’ Strategic
choice of CCS, based on which a series of hypotheses are proposed in the next section.
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Figure 1. Research Model
Hypotheses
Strategic Choice of CCS
The dependent variable is strategic choice of CCS. Based on prior studies (Davis and Olson 1985; Thong
1999), the strategic choice of CCS in this research is defined as using CCS to support operations,
management, and decision making in the business. The strategic choice of CCS has two steps: (1) the
likelihood of choosing CCS of an SME; and (2) the depth and width of CCS implementation in the SME.
The likelihood of CCS is operationalized in dichotomy: CCS choices versus non-CCS choices. CCS choices
include IaaS, SaaS and PaaS whereas the non-CCS choices refer to all other non-CCS IT utilization modes.
When the SMEs intend to choose CCS strategy, we would further investigate their depth and width of CCS
strategy implementation in the firms.
Technological Factors on the Vendor Side
General Technological Factors
According to innovation diffusion theory, the rate of adoptions is impacted by five factors: relative
advantage, compatibility, trialability, observability, and complexity (Rogers 1995). Moore and Benbasat
(1991) adapted innovation diffusion theory into IS research field by expending the five factors into eight
factors encompassing relative advantage, compatibility, trialability, visibility, result demonstrability,
voluntariness, image, ease of use (Moore and Benbasat 1991). After this early application of innovation
diffusion theory in IS research context, a bunch of IS research have relied on this theory to investigate the
adoption and diffusion of various information systems. Research done by Premkumar et al.(1994)
examines the relationship between innovation characteristics and attributes of diffusion of electronic data
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interchange in organization (Premkumar et al. 1994). An integrative model examining three technology
assimilation stages created by Zhu et al.(2006) also relies on innovation diffusion theory (Zhu et al. 2006).
CCS reliability refers to the extent to which a system, CCS in particular, can be depended on to perform its
intended tasks (Furneaux and Wade 2011). The more reliable the new innovation, the more likely it will be
adopted in the potential companies. CCS relative advantage is the degree to which an innovation, CCS in
particular, is perceived better than the one it supersedes (Rogers 1995). The greater the perceived relative
advantage of an innovation, the quicker it will be accepted (Rogers 1995). The positive perceptions of IS
will prompt SMEs to adopt the related innovation (Thong 1999). CCS complexity is defined as the degree
to which an innovation, CCS in particular, is perceived more difficult to understand and use (Rogers 1995).
The complexity of an innovation is considered to be a hinder to its acceptance (Grover 1993; Teo et al.
2003; Tornatzky and Klein 1982). The higher the CCS complexity, the more effort will be needed for firms
to move traditional business process to the cloud. CCS compatibility is the degree to which an innovation,
CCS in particular, is perceived as being consistent with the existing values, past experiences, and the needs
of potential adopters (Rogers 1995). The more compatibility the new technology with the existing systems
in the potential companies, the more likely the new technology will be adopted. CCS trialability is the
degree to which an innovation, CCS in particular, may be experienced with on a limited basis (Rogers
1995). The triable service allows the clients to pretest whether the new solution fits their special
information needs and thus decide to select it or not. CCS observability is the degree to which the results of
an innovation, CCS in particular, are visible to others (Rogers 1995). As a new information service
solution, it is important for users to become aware of the results of using this new technology.
To conform to prior research mainly based on innovation diffusion theory, we propose the following
hypotheses:
H1: CCS reliability (H1a), relative advantage (H1b), compatibility (H1d), trialability (H1e), observability
(H1f) will have a positive impact on SMEs’ strategic choice of CCS, and complexity (H1c) will have a
negative impact on SMEs’ strategic choice of CCS.
Specific Technological Factors Related to CCS
In addition to the factors identified by prior innovation diffusion research, we also identify several specific
factors that have potential to affect SMEs’ strategic choice of CCS. The standardization, online operation
environment with data stored in invisible cloud, and pay-for-use pricing are the three critical features of
CCS, which differential CCS from traditional information service. Hereinafter, we propose customization,
data security, and the support cost, respectively, will influence SMEs’ strategic choice of CCS.
Customization refers to the extent to which CCS can be specified to fit the individual requirement of the
potential companies. As we illustrated, the low cost of CCS model is based on scale economics. This means
that in house development or traditional IT service outsourcing has much advantage in meeting the higher
customization requirements compared with CCS. However, it is obviously that the higher the level of CCS
customization, the more likely that it will meet clients’ requirement. Therefore, we hypothesize that:
H2: Customization of CCS will have a positive impact on SMEs’ strategic choice of CCS.
Data security refers to the extent to which data confidentiality can be guaranteed. There are researchers
claiming that security and privacy of online transactions tend to be common concerns both for companies
and consumers (Straub et al. 2002; Xu et al. 2004). For CCS, online operation environment makes clients
more concerned about security problem, especially data security. Thus, data security will be more
influential to client behavior in the context of CCS. Besides, resource poverty and fierce competition all
make firms more concern about data security when they consider of online transactions. Therefore, we
hypothesize that:
H3: The assurance of data security by CCS vendors will have a positive impact on SMEs’ strategic choice of
CCS.
The support cost refers to cost an organization incurs to support ongoing operation of an information
system. The higher the support cost, the more unlikely potential companies will adopt a new technology
(Gill 1995). Usage-based pricing model of CCS will reduce the system support cost. For traditional
systems, the ongoing maintenance and update cost are about 70-80 percent of total costs over a system’s
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lifetime (Pressman 1992). Decreasing cost is one critical factor that will make SMEs benefit from advanced
information technology (Thong 1999). Therefore, we hypothesize that:
H4: The support cost of CCS will have a negative impact on SMEs’ strategic choice of CCS.
Organizational Factors on the Client Side
Demand Uncertainty
Research based on RBV and production cost economics argue that organizations operating in the
environment with more uncertainties should increase their flexibility by taking advantage of vendors’
capability (Balakrishnan and Wernerfelt 1986; Levina and Ross 2003; Slaughter and Ang 1996). When
clients’ functionality requirements of information service are generic and their requirement of the service
volume is uncertain, according to the production cost economics, external service providers can be more
efficient in handling these changes (Xin and Levina 2008). SMEs operating in a highly competitive
environment face a bunch of uncertainties for adopting information systems and services in a perpetual
mode. Such demand uncertainties include uncertainty for infrastructure investment and uncertainty for
service functionalities. To reduce the uncertainties, SMEs may make a strategic choice of CCS in a leasebased pricing scheme. Therefore, we hypothesize that:
H5: SMEs’ demand uncertainties for service functionalities and infrastructure volumes will have a positive
impact on SMEs’ strategic choice of CCS.
Organization Size
Organization size is a proximate indicator of the organizational capability to implement information
systems on the one hand, and serves as an indicator of the organizational inertia that impedes the
organization to accept innovation on the other hand (Zhu and Kraemer 2005). Tornatzky and Fleischer
(1990) argue that larger firms generally possess slack resources which facilitate the implementation and
usage of technology (Tornatzky and Fleischer 1990), whereas Thong (1999) claims that structural inertia
associated with large firms may slowdown organizational usage (Thong 1999). For SMEs, resource poverty
including the lack of IT expertise and financial difficulties prevents them from new internal information
technology implementation. Thus, SMEs have to find a more flexible IT solution. Considering structural
inertia, smaller firm size is expected to facilitate innovation usage because of less communication, less
coordination and influence to get support (Nord and Tucker 1987). In conclusion, smaller firms have
higher motivation to select CCS because of resource poverty, and also have more flexibility to adopt CCS
because of less structural inertia. Thus, we hypothesize that:
H6: The organization size of a particular SME will have a negative impact on the SME’ strategic choice of
CCS.
Business Scope
Business scope is defined as the geographical extent of a firm’s business process involved in the global
market based on Zhu and Kraemer (2005)’s definition of international scope which emphasize
globalization instead of product orientation (Zhu and Kraemer 2005). Several research claim that the
greater business scope induces greater demand for IT (Dewan et al. 1998; Hitt 1999), because greater
business scope makes business flexibility more important to facilitate business process. Ernst’s (2003)
research shows that firms need a flexible IT infrastructure and IT management skills to deal with
heterogeneity when they enter a new market segment (Ernst 2003). As we have illustrated above, three
properties of CCS including rapid release capability, zero or a small scale of upfront investments and payfor-use mechanism all make CCS a more flexible way for firms when they are entering a new marketplace.
CCS allows companies to obtain rapid released IT capability with few space and time constraints. Thus,
CCS may be a more flexible way for firms, especially for SMEs who have resource poverty that restricts
them to expand into heterogeneous market segments. Therefore, we hypothesize that:
H7: Business scope of a particular SME will have a positive impact on the SME’s strategic choice of CCS.
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Technology Competence
Based on RBV and production cost economics, organizations with large and well-managed internal IT
department are more likely to operate diverse IT project to obtain technical competence (McFarlan and
Nolan 1995). This means that firms with higher level of technology capability tend to be more count on
their own side instead of taking advantage of outside IT services. This is different for firms without a larger
internal IT department. In order to improve technology competence, firms with less competitive
technology capability may have more motivations to select CCS. This is especially true for SMEs with lower
level of IT capability. Therefore, we hypothesize that:
H8: The technology competence of a particular SME will have a negative impact on the SME’ strategic
choice of CCS.
Environmental Factors
CCS Market Maturity
Market maturity is a complex concept (Keogh and D'Arcy 1994). It is shaped by the number of clients as
well as the system or service supports availability on the CCS market (Furneaux and Wade 2011). Based on
the production cost theory, production cost advantages can be developed through the scale production.
The upfront costs can be lowered through an increasing number of users (Xin and Levina 2008). Also,
with the increasing number of CCS vendors, the industrial competition improves the service quality. The
guaranteed service quality and reduced upfront cost will increase the confidence of SMEs to make a
strategic choice of CCS. Thus, we hypothesize that:
H9: The CCS market maturity on the vendor side will have a positive impact on SMEs’ strategic choice of
CCS.
Regulation Support
Regulation support is defined as the success assurance stemming from government policies, regulations,
rules, laws, et.al. One of the consumer’s primary concerns about cloud computing is security problem
(Marston et al. 2011). As online operation environment is a critical feature of CCS, transaction process
which refers to service delivering process between CCS vendors and service adopters need to be
guaranteed to facilitate CCS acceptance. Meanwhile, the literature shows that government policies
theoretically affect IT diffusion (Umanath and Campbell 1994). CCS as a new type of information
technology service solution, the acceptance of it can be seen as IT diffusion or innovation diffusion
process. Accordingly, we assume that regulation support which provides legal protection or guarantee for
business transactions will for the most part motivate firms to select CCS. Thus, we hypothesize that:
H10: The regulatory support for CCS from the government will have a positive impact on SMEs’ strategic
choice of CCS.
Social Influence
We define social influence as the degree to which the opinion or behavior of one firm is affected by others.
Some researchers claim that successful implementation or acceptance of new information technologies
often depends on various kinds of social influence (Davis et al. 1989; Malhotra and Galletta 1999). Social
influence can be measured from many different forms. Our study mainly based on one identified
categorized social influence: identification. Identification is when people are influenced by someone who is
liked and respected (Kelman 1958). This identification can be transformed into firm context where firm
attitude towards new technology or innovations will be influenced by their customers, suppliers and
competitors who are closely interrelated with them. In our research context, SMEs attitude or behavior
intention towards CCS is accordingly will be influenced by their peers. This is consistent with innovation
diffusion theory which illustrates that peers’ evaluation of an innovation will be more influential than
scientific evaluations in decision stage (Rogers 1995). Thus, we hypothesize that:
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H11: The peer organizations’ strategic choice of CCS will have a positive impact on related SMEs’ strategic
choice of CCS.
Research Methodology
Measurement Development
We have developed the measures for all related constructs in our research framework. The definitions of
constructs were formulated by referring to prior literature review and relating to our special research
context. Most measures used in our research were adapted from the existing scales which fit the construct
definitions of our study. Some modifications were made to ensure that the scales were consistent with our
research context and a few of new measures were self-developed after consulting with several experts and
professionals. Once we finished selection of measurement scales, we carried out a small group interview
with academic and industrial experts to refine our instruments.
Data Collection
Data collection in this study will be conducted in two phases constituted by a pilot study and a large-scale
survey. One questionnaire was designed for data collection. Top managers or people working in the IT
department will be the main informant in this research. Since those people are much familiar with the
information technology applied in their companies. Before the questionnaire survey, we conducted pilot
study. In this phase, online survey website was used to pretest our questionnaire. We sent invite emails to
people working in companies. Ten completed questionnaires were collected. Based on feedback from these
results, some modifications were conducted for next phase of data collection. The response from these ten
businesses will not be included in the final sample.
Conclusions
We have developed the instruments to measure for all the constructs. Interviews and a small-scaled pilot
study involved around 10 firms have been carried out to test the construct validity and reliability. The
preliminary results demonstrated the high reliability and internal consistency and the acceptable
discriminate validity of the constructs. We plan to conduct the large-scaled survey among SMEs in a
science park in Shenzhen, China. We have established the initiated contact with the park and got the
support commitment. For the data analysis, we also have two steps. First, we will use the discrete choice
model to test the predictability of the proposed factors for SMEs’ strategic choices of CCS (IaaS, SaaS, and
PaaS) vs. non-CCS. Second, we’ll investigate how much the proposed factors can affect the depth and
width of CCS adoption for those SMEs that intend to have a CCS strategy, by using structural equation
modeling techniques (e.g., partial least squares, PLS).
Overall, this research will have the following contributions. First, to the best of our knowledge, our
research is on the leading edge to systematically investigate the drivers for SMEs’ Strategic choice of CCS
from a TOE paradigm. Both CCS vendor and client factors are taken into account. We not only consider
the generic technological factors identified in Rogers’ innovation diffusion theory, but also account for CCS
specific technological factors based on our interviews. The potential influential power of the CCS specific
factors will give CCS vendors insights to provide appropriate services to their clients. We also consider the
organizational condition and capability of clients when making the strategic choices. We expect the
findings can generate insights for the CCS potential clients to conduct the internal scanning before the
strategic decision. Second, our research has the potential to help CCS companies fine tune their service to
the niche market of SMEs. Meanwhile, this research also has the potential to provide administrators
guidelines to provide infrastructure and/or public policy support for SMEs’ use of CCS. We believe it is
critical for companies sustain and succeed in the coming cloud computing era.
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Acknowledgements
This research was supported in part by the PPR Grant 9056003 from Hong Kong Research Grant Council
and City University SRG 7002625 from City University of Hong Kong.
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