adopting cloud systems: a status quo bias...
TRANSCRIPT
2012 TOPCO崇越論文大賞
論文題目：
Adopting Cloud Systems:
A Status Quo Bias Perspective
報名編號： AC0014
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ABSTRACT
Although a lot of attention is paid in cloud system applications, organizations in
business are still skeptical of adopting cloud framework. The purpose of this study is to
investigate the determinants on firms’ reactions toward cloud systems adoption from
status quo bias perspective. An integrative conceptual framework of system adoption
was proposed and tested by a meta-analytic review with 34 published studies. Based on
the findings of meta-analysis, a quantitative research model was proposed and empirical
tested. By using cloud ERP and cloud CRM as research target, 303 usable responses
were collected from the leading manufacturing and service firms in Taiwan. The results
indicate that institutional pressures, switching benefits, and switching costs have
significant influence on the perceived value of cloud system adoption. Switching
benefits and switching costs are key determinants of perceived risk. Perceived value
contributes to organizational adoption intention, while perceived risk has significant
influence on organizational resistance. The results also reveal the difference among the
type of cloud systems. The findings provide a foundation for understanding the possible
determinants of cloud system adoption and resistance as well as valuable implications to
organizations seeking to utilize cloud-based framework.
Keywords: Cloud Computing, IS Adoption, Status Quo Bias, Meta-Analysis
1. Introduction
With the evolution and development of complementary information and communication
technologies, cloud computing has been regarded as a new paradigm for hosting
information technology (IT) infrastructure (Zhang et al., 2010). Cloud system adoption
infers organizations partially or fully replace their landscapes of incumbent system with
cloud environment (Oracle, 2010). Through leasing outside computing resources,
organizations avoid up-front investment while reducing their business risks (Zhang et
al., 2010). With the elastic provision of computing resources, cloud systems can be
automatically upgraded and can be flexibly scaled upward and downward (Gill, 2011;
Mell and Grance, 2011). However, in spite of the potential advantages of adopting,
firms hesitate to embrace cloud systems and concern about the issues of accessibility,
reliability, security, and vendor lock-in (Benlian et al., 2009; Gill, 2011; Saya et al.,
2010). A worldwide survey of Gartner Executive Programs reveals that only three
percent of CIOs indicate that they have utilized cloud computing resources (Gartner,
2011). This means that many firms are still skeptical of adopting cloud computing.
Adopting cloud systems implies that firms should discontinue current, on-premises
systems and shift to the cloud alternative. Despite the profound impact of adopting
cloud systems, theoretical and empirical research pertaining to the cloud systems
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adoption and the factors which affect a firm’s reaction has been limited and piecemeal
(Khajeh-Hosseini et al., 2010; Saya et al., 2010). In order to avoid possible risks of
system adoption, firm has a tendency to behave conservatively and to maintain the
well-turned structure (Shim et al., 2009). The perception of status quo on existing
systems may reinforce decision maker to remain current status. For instance, the
associated investments on existing system will impede an organization to chase new IT
innovations. Therefore, identifying the determinants of cloud systems adoption and
resistance has substantial merit for helping researchers and managers to have a better
understanding on this growing new IT paradigm.
The main purpose of this research is to understand the determinants of adopting cloud
systems. Although cloud systems demonstrate charming change forces, organizations
may be shackled to existing information systems by the inertia, such as the
embeddedness of existing system (Furneaux and Wade, 2011). Therefore, this study
argues that firms’ perception of current internal, external status will influence their
decision of adopting cloud systems. Drawing on the concepts from the status quo bias
theory and interrelated literature streams, the objectives of this study is to examine the
status quo effects on a firm’s reaction of cloud systems adoption. Specifically, this study
focuses the following research questions: (1) What and how a firm’s perception of
status quo impacts its expectation toward adopting cloud systems? (2) What and how
the expectation of adopting influences the cloud systems adoption and resistance? (3)
Does the expectation of adopting mediate the perception of status quo on a firm’s
adoption and resistance?
The organization of the paper is as follow. The conceptual background and the related
literature reviews for constructing the research framework are presented in session 2.
Subsequently, a conceptual framework is proposed and is examined by conducting a
meta-analytic review. In session 4, a quantitative research model is presented, followed
by research hypotheses, instrument development, data collection, data analysis, and
hypotheses testing results. In session 5, the discussion of research findings is presented.
This study concludes with implications, limitations, and potential future research
directions.
2. Conceptual Background
2.1 Cloud Systems Adoption
Cloud computing provides an on-demand computing model and changes the traditional
allocation of IT resources to a more collaborative framework (Hayes, 2008) National
Institute of Standards and Technology (NIST) has announced the definition of cloud
computing, which has been widely used as a baseline for further discussion (Mell &
Grance, 2011). In general, cloud computing uses virtualization technologies to provide
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on-demand computing resources via networks and has the following characteristics:
on-demand self-service, resource optimization, scalability, flexible pricing model, and
measured service.
The flexibility of a cloud-based framework allows cloud service providers to support
multiple products with shared resources (Anandasivam and Premm, 2009). Cloud
computing basically consists of three service models: (1) Infrastructure as a Service
(IaaS): the provision of storage capabilities and computing power; (2) Platform as a
Service (PaaS): the provision of a programmable environment with needed
programming languages, libraries, services, and tools; (3) Software as a Service (SaaS):
the provision of web-based applications. In addition, there are basically four ways to
deploy cloud computing, including private cloud, public cloud, community cloud, and
hybrid cloud (Mell and Grance, 2011). Each deployment model has its benefits and
drawbacks (Zhang et al., 2010). The decision of choosing a proper cloud computing
deployment model should take technological as well as organizational factors into
consideration.
Adopting cloud services, firms do not have to invest on up-front or other capital
expenditure. For small and medium enterprises (SMEs), cloud computing provides
affordable access to large data centers and unique services (Weinhardt et al., 2009).
Although there are many benefits with adopting cloud computing, Gill (2011) found
that firms still hesitate to embrace cloud computing. Adopting cloud systems is not
merely a technical improvement and can bring about many organizational changes.
Companies concern about the issues of security, customization, reliability, and data
ownership. Saya et al. (2010) found that institutional influences can ease the perceived
lack of security as well as spread potential benefits of adoption (i.e., accessibility,
scalability, and cost effectiveness). Heart (2010) argued that data insecurity and system
unavailability are associated with perceived risks of SaaS, which decrease
organization’s desire to adopt SaaS. An empirical study on the adoption of SaaS-based
applications on German companies revealed a difference between application types
(Benlia et al., 2009). Benlia et al. (2009) found that companies are conservative to adopt
SaaS services with higher strategic relevance, such as CRM and ERP. Accordingly, we
argue that the current internal, external status will affect firms’ decision on cloud
systems adoption. Although cloud-based framework reveals many potential benefits,
firms should address technical and managerial issues to ensure a successful this digital
transformation. We now turn our focus to further developing the theoretical foundations
of system adoption.
2.2 Theoretical Foundations of System Adoption
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Past researches have used various theoretical perspectives to provide complementary
viewpoints on the IT innovation adoption (Lee and Xia, 2006). The diffusion of
innovation (DOI) theory is introduced to explain a new idea or technology spread out in
a social system. An innovation is communicated, over time, among the members of a
social system through particular channels (Rogers, 2003). Innovative attributes play
important roles on firms’ adoption of new ways of developing, implementing, and
maintaining information systems (Mustonen-Ollila and Lyytinen, 2003). Consist with
Rogers’ arguments, Tornatzky and Fleischer (1990) argue that the contextual factors of
adopting an IT innovation is threefold, including technological context, organizational
context, and external environment context.
Institutional theory examines why firms tend to react to comply with institutional
legitimacy. DiMaggio and Powell (1983) propose three isomorphic institutional forces:
(1) coercive pressures, (2) normative pressures, and (3) mimetic pressures. A firm will
perceive mimetic pressures from the structural equivalent competitors with similar goals,
customers, and operating constraints (Teo et al., 2003). Previous IS researches have also
recognized institutional pressures as important external influence on the decision of IT
adoption. For instance, Teo et al. (2003) found that institutional forces enable the
adoption of financial electronic data interchange (FEDI).
The basic argument of resource-based view (RBV) is that the heterogeneous resources
of firms enable them to compete against with others (Barney, 1991). A Firm can sustain
its competitive advantage by possessing resources that are valuable, rare, imperfectly
imitable, and non-substitutable (Barney, 1991). Therefore, the business value of IT rests
on the application of IT and complementary organizational resources by improving
business process and organizational performance (Melville et al., 2004). Through the
theoretical lens of RBV, previous researches focus on the discussion of the IT capability
development (Karimi et al., 2007), the influence on business processes (Ray et al.,
2005), the impacts on firm performance (Hulland et al., 2007), and new system adoption
(Benlian et al., 2009).
The transaction cost economics (TCE) is used to explain needed effort and cost during
an exchange occurs (Williamson, 1981). Several characteristics and determinants have
been introduced, including asset specificity, uncertainty, opportunism, bounded
rationality, and frequency (Miranda and Kim, 2006). The adoption of IT can help to
reduce the transaction costs. For example, with SCM system or EDI, the coordination
between business partners can be enhanced as well as enable the business integration
(Subramani, 2004). The TCE has also been applied to discuss IT outsourcing (Miranda
and Kim, 2006) and outsourcing contract design (Susarla et al., 2009). The following
section will further develop the theoretical concepts of the status quo bias theory.
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2.3 Status Quo Bias
2.3.1 Theory of Status Quo Bias
Every decision has a status quo option, which acts as an anchor for any possible
alternatives and has influence on final decision (Samuelson and Zeckhauser, 1988).
Samuelson and Zechuauser (1988) term the tendency of a decision agent to adhere to
the situation or decision already in place as status quo bias. They propose three main
categories to explain the status quo bias: cognitive misperception, psychological
commitment, and rational decision making.
Firstly, cognitive misperception stems from three propensities of decision agents: loss
aversion, anchoring, and bounded rationality. Loss aversion refers to a phenomenon that
decision agents tend to weigh losses heavier than gains (Kahneman and Tversky, 1979).
In addition, the previous decision (i.e., status quo) is often used as an anchor for
following decision making. With bounded understanding the pros and cons of a new
alternative, decision makers only evaluate the available options. As to new system
adoption, managers will utilize the performance and the deployment of incumbent
systems to evaluate the possible solutions. The existing working practices make firm to
be predisposed to oppose the logics of new systems (Gosain, 2004).
Secondly, psychological commitment may be the results of sunk costs, the efforts of
making consistent decision and to feel in control, and the avoidance of regrettable
decision (Kim and Kankanhalli 2009; Samuelson and Zeckhauser 1988). Firms have
allocated plenty of tangible and intangible resources to leverage the IT investments (Zhu
et al., 2006b). These can be the sunk costs for companies to move to a new system.
Institutional pressures will shape the norms prevailing in the business environment
about the change. In order to maintain competitive advantages or to avoid regrettable
decision, firm’s reaction to a new system will depend on the attitudes and reaction of its
cooperators or competitors.
Finally, rational decision making refers that decision makers will consider the costs and
benefits of switching to a new option with the presence of transition costs and
uncertainty. The initial choice introduces transition costs for the subsequent decision.
Transition costs can be distinguished into transient (e.g., learning costs) and permanent
(e.g., loss of work) (Kim and Kankanhalli, 2009; Samuelson and Zeckhauser, 1988). On
the other hand, adopting new system often accompany uncertainty of the firm’s
adaptation. The limited knowledge and experience of new systems may lead
organizations to stick with the incumbent systems (Polites and Karahanna, 2012).
2.3.2 Status Quo Bias in Cloud Systems Adoption
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The adoption of cloud systems can be regarded as the issues of system adoption or
replacement. The deployment of incumbent system will be an important reference point
for the decision of adopting could-based applications. Furneaux and Wade (2011) argue
that continuance inertia has influence on the firm’s intention to replace existing system.
Unlike poor management, continuance inertia, including structural and network inertia,
often refers to a “well-tuned architecture” and reveals synergy to the focal firm (Kim et
al., 2006). Organizations have to acquire needed managerial skills and technological
know-how to accommodate existing business process to cloud-based environment. In
addition, companies also need to adapt the internal and external entrenched structure
(Zhu et al., 2006b). Therefore, firms either newly adopt or replace will take their current
status into consideration.
Organizational ecologists introduce the concept of “structural inertia” to delineate why
firms tend to react slowly to environmental changes (Hannan and Freeman, 1984). The
implementation of a new system refers needed adaptation among the business logics
embedded in existing systems and those in new alternatives (Gosain, 2004). Previous
researches have proposed corresponding arguments toward the influence of the status
quo on adopting a new system. Van de Ven and Poole (1995) introduce a dialectical
model to describe how “confrontation and conflict between opposing entities” enable
the progression of an organization. As shown in the Figure 1(a), the synthesis will be
produced by a collision between the opposing thesis and antithesis.
Figure 1. Corresponding Arguments of New System Adoption
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Joshi (1991) proposed the equity implementation model to explain the influence of the
changes in inputs and outcomes introduced by the new system adoption on user
resistance. By extending Joshi’s model, Kim (2010) empirically found that changes in
benefits and costs will affect user resistance to the open source software through the
perceived value (see Figure 1(b)). In a similar vein, Gosian (2004) suggests that ERP
implementation often accompany with the misalignment between the existing
institutional logics and those in the new system, which can lead to resistance, selective
appropriation, and unintended side effects (see Figure 1(c)). These arguments provide a
supportive foundation for the anchoring effect of current status on the evaluation of new
alternatives. Accordingly, the status quo of a firm will affect its expectation of adopting
a new system, which in turn has influence on the reaction to the alternative (see Figure
1(d)).
3. Meta-Analytic Review
3.1 Conceptual Framework and Literature Searching
Base on the status quo bias perspective, this study proposes an integrative model of new
system adoption at firm level. The perception of status quo will affect a firm’s
expectations toward adopting a new system, which in turn have influence on the
reaction to the alternative. Specifically, the incumbent system deployment (ISD),
institutional pressures (IP), switching benefits (SB), and switching costs (SC) will
determine the perceived value (PV) and perceived risk (PR) of the alternative.
Subsequently, the adoption (A) or resistance of a new system will depend on the
perception of value and risk. Figure 2 presents the conceptual model of new system
adoption from the status quo bias perspective.
Figure 2. Conceptual Model of System Adoption from the Status Quo Bias Perspective
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Meta-analysis has been widely used to summarize and expand the knowledge on
specific research topics (Kirca et al., 2011). Meta-analytic procedures can help to find
obscure effects and relationships (Lipsey and Wilson, 2001). Researchers can examine a
more comprehensive set of factors for theory-testing purposes through a systematical
review process (Viswesvaran and Ones, 1995). In order to establish the existence and
magnitude of the relationships proposed in the conceptual model, a meta-analytic
review was conducted.
The meta-analytic research dataset was constructed through collecting research
manuscripts by conducting journal-by-journal searching in five electronic databases: (1)
ABI/INFORM Global, (2) Google Scholar, (3) JSTOR, (4) EBSCOhost, and (5)
Elsevier Science Direct. In addition, conference proceedings were also included to
minimize the potential publication bias (Wu and Lederer, 2009). The multiple keywords
were utilized, including “information system adoption,” “IT adoption,” or
“organizational IT diffusion.” When the studies based on same dataset were reported in
different journal articles and proceedings, only one was selected as representative. The
articles would be excluded according to the following criteria: (1) it did not analyze at
firm level, (2) it should be a theoretical or conceptual research, (3) it did not examine
the relationship in the conceptual model, and (4) it did not report required data for
conducting meta-analysis (i.e., correlations).
These efforts yielded a sample of 34 primary studies (indicated with an asterisk in
References section), consisting of 26 journal studies and eight conference proceedings.
The time period from 2006 to 2010 represented a relatively major portion of the total
research sample.
3.2 Coding of Correlations and Moderators
The correlation coefficient was chosen as the effect size metric. The following
information was retrieved from each study to construct dataset for meta-analysis:
sample size, reliability of constructs (i.e., Cronbach’s alpha or inter-item reliability
scores), and correlations for each relationship. Most included studies focus on the
relationships between the determinants and IT adoption. Table 1 reports the distribution
of uncorrected correlations. Four relationships (i.e., ISD-PV, SC-PV, ISD-PR, and
SC-PR) were excluded because of lacking enough studies describing them. Therefore, a
total of 158 effect sizes were identified from the 34 studies. The number of effect sizes
that can be used to test each relationship varies from 4 to 59.
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3.3 Meta-Analytic Procedure
This study used the meta-analytic protocol proposed by Lipsey and Wilson (2001).The
meta-analytic procedure follows the four steps suggested by Hofmann et al. (2005),
including: (1) collection of effect sizes and needed information, (2) combination of
single correlations within studies, (3) correction for measurement error, and (4)
meta-analytic amputations on study correlations. Firstly, the values of sample size,
reliability of constructs, and correlation coefficients for each relationship reported in the
manuscripts were collected. In order to eliminate attenuation due to measurement error,
the correlation of focal effect was corrected by the reliability measure of both
constructs.
3.4 Meta-Analysis Results
The results of Lipsey and Wilson’s protocol are shown in Figure 3. Examining the 95%
CIs of ten relations, the results indicate that all the relationships are significant. Cohen
and Cohen (1983) advocate that the magnitude of correlation can be distinguished into
strong (0.50), moderate (0.30), or weak (0.10). Accordingly, the magnitude of all r for
corresponding relations are weak to moderate, and the absolute values range from 0.11
to 0.44. A ratio of fail-safe N to the number of observations is used and the value should
be greater than 2 (Petter and McLean, 2009). The results suggest that most of
Table 1. Distribution of uncorrected correlations
Relation k
Range Positive
(r ≥ 0.1)
Low
(0.1> r > -0.1)
Negative
(r -0.1) Lower Upper
ISD-PVa 1 0.00 0.00 0 1 0
IP-PV 8 -0.17 0.62 7 0 1
SB-PV 5 -0.14 0.62 4 0 1
SC-PVa 1 -0.37 -0.37 0 0 1
ISD-PRa 2 0.06 0.22 1 1 0
IP-PR 6 -0.50 0.27 3 1 2
SB-PR 8 -0.39 0.23 1 6 1
SC-PRa 2 0.39 0.61 2 0 0
ISD-A 10 0.16 0.44 10 0 0
IP-A 59 -0.09 0.73 53 6 0
SB-A 12 0.12 0.74 12 0 0
SC-A 4 -0.79 0.21 1 1 2
PV-A 24 -0.18 0.68 21 3 1
PR-A 22 -0.62 0.25 2 2 18 Note: a The relationships were excluded.
k = number of correlations; ISD = incumbent system deployment; IP = institutional pressures; SB =
switching benefits; SC = switching costs; PV = perceived value; PR = perceived risk; A = adoption
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meta-analytic findings are robust except SB-PV, IP-PR, and SB-PR. Drawing on these
findings, a publication bias would be minimized in the present meta-analysis.
3.5 Summary of the Meta-Analytic Review
Although previous IS researches have accumulated a considerable body of findings on
IS adoption, there is no systematic review from the status quo bias perspective. After
conducting a meta-analytic review, this study provides three observations. First, most
studies focused on positive reaction toward an IS innovation (both adoption intention
and adoption level), leaving a gap for understanding the negative reaction, such as
organizational resistance. Second, the relationships between status quo factors,
expectations, and systems adoption are indeed related. However, there are still some
relationships under explored, including ISD-PV, ISD-PR, SC-PV, and SC-PR.
4. Quantitative Research
4.1 Research Model and Hypothesis Development
The final phase of this study is to empirically investigate the cloud systems adoption
from the status quo bias perspective. Taken the findings from meta-analytic review
together, the research model is proposed by considering the factors that have been
identified and found to be significant for system adoption in previous IS researches. The
research model is shown as Figure 4. The perceptions of status quo are categorized as
cognitive misperceptions, psychological commitment, and rational decision making.
The main argument of this study is that the perceptions of status quo will affect firms’
expectations on the value and risk of cloud systems adoption. These expectations are in
turn expected to influence organizational resistance and adoption intention. The
research hypotheses are detailed next.
Figure 3. Summary of Meta-Analysis
r = 0.30*
r = 0.29
r = 0.12
r = 0.34 r = 0.24
r = 0.09
r = 0.40
r = -0.21
r = 0.25
r = -0.12
Incumbent System
Deployment
Institutional Pressures
Switching Benefits
Switching Costs
Perceived Value
Perceived Risk
Adoption
Moderate Relationship
Weak Relationship
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4.1.1 Cognitive Misperception
There are three potential sources of cognitive misperception in decision making: loss
aversion, anchoring, and bounded rationality. The status quo bias will exert its influence
by enlarging possible losses (Kim and Kankanhalli, 2009). As to system adoption, the
deployment of incumbent system serves as a reference point in evaluation process. Firm
needs to transform organizational processes to effectively and efficiently use the
existing system, which will bring about a lock-in effect (Kremers and Van Dissel, 2000).
Consequently, the performance of incumbent system might become the anchor for
evaluating possible alternatives. For instance, Chau and Tam (1997) found that
satisfaction with existing systems negatively affect the adoption of an open system.
Companies have higher satisfaction with existing systems will tend to adhere to their
status quo and regard changing to could systems is less valuable and high-risk. Base on
these arguments, the following two hypotheses are suggested:
H1: Satisfaction with existing system is negatively related to perceived value.
H2: Satisfaction with existing system is positively related to perceived risk.
4.1.2 Psychological Commitment
Psychological commitment can also reinforce or weaken the status quo bias of decision
making. In order to avoid regrettable consequences and to keep decision consistency,
organizations are often reluctant to replace existing system immediately (Furneaux and
Wade, 2011). On the other hand, since IT investments will determine organizations’
competitive position, managers need to keep track of external information, especially
competitors’ decisions (Shim et al., 2009). In addition, organization also encounters the
pressure from “interorganizational field” (Kim et al., 2006). Competitive and regulatory
environment positively contribute to the initiation and adoption of an IT innovation
Figure 4. Quantitative Research Model
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(Zhu et al., 2006b). These external pressures may strengthen firms’ propensity to use
incumbent systems or to move toward could-based environment.
The consensus toward the value of using cloud systems can facilitate the adoption
among partners in a network. Institutional pressures provide firms needed legitimacy for
embracing new IT innovation (Teo et al., 2003). To avoid regrettable decision,
organizations may adopt cloud systems because their collaborators and competitors are
doing so. Institutional pressures may exert by the government, business associations,
suppliers, competitors, and customer. For most firms, the paramount concerns are to
retain their customer accounts as well as to follow industrial regulations. Firms will
perceive coercive pressures formally or informally from their institutional environment
(Ke et al., 2009). Ke et al. (2009) distinguished customer pressures from general
coercive pressures. Therefore, we incorporated both customer pressures and generic
coercive pressures into this study.
Prior researches have demonstrated the important roles of environmental pressures in IT
adoption. Kuan and Chau (2001) found that firms receive external pressures from both
industry and government when adopting EDI. Institutional pressures can facilitate
organizational virtualization (Liu et al., 2008) and ERP implementation (Gosain, 2004;
Liang et al., 2007). Shim et al. (2009) also found that the extent of competitors’
adoption will diminish firms’ perceived risk of using open systems. Accordingly, the
following hypotheses are proposed:
H3: Institutional pressures are positively related to perceived value.
H4: Institutional pressures are negatively related to perceived risk.
4.1.3 Rational Decision Making
In order to avert transition costs and uncertainty, firms often evaluate costs and benefits
before switching to new alternatives (Kim and Kankanhalli, 2009). Companies need to
accommodate technical and managerial IT skills to sustain competitive advantages
(Mata et al., 1995). Managerial obstacles, such as the lack of technical knowledge and
managerial experience, will decrease the firm’s propensity to adopt an IT innovation
(Zhu et al., 2006b). Therefore, managers need to acquire needed technical and
managerial expertise for migrating an in-house, on-promised system to cloud-based
environment. The misalignment between the existing institutional logics and those
embedded in cloud systems can produce assimilation gaps (Fichman and Kemerer, 1999;
Gosain, 2004). The lack of know-how toward working under cloud environment might
lead to a biased evaluation of cloud systems adoption.
Prior researches have revealed that perceived benefits and costs have significant
influence on IT adoption (Chau and Tam, 1997; Kuan and Chau, 2001). Switching
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benefits and switching costs respectively refers to the losses and gains of abandoning
the status quo (Kim, 2011). Kim (2011) found that switching costs will decrease the
perceived value of the ERP system, while switching benefits positively contribute to the
ERP implementation. In the same vein, switching benefits and costs will affect
organization’s overall assessment of changing to a cloud environment. Hence, the
following four hypotheses are proposed:
H5: Switching benefits are positively related to perceived value.
H6: Switching benefits are negatively related to perceived risk.
H7: Switching costs are negatively related to perceived value.
H8: Switching costs are positively related to perceived risk.
4.1.4 Perceived Value and Risk
Based on the concept of net equity, Kim (2011) suggests that perceived value is the
perceived net benefits of change. The consequences of current usage and expectations
about future use are two kinds of net benefits toward system adoption (Seddon, 1997).
DeLone and McLean (2003) argue that the expectations about future use will increase
the intention to use an information system. Liu et al. (2008) found that positive net
benefits can facilitate the adoption of organizational virtualization. On the other hand, in
order to avoid regrettable decision, firms tend to remain the status quo when perceived
risk of adoption is high. Perceived risk is defined as “a decision maker’s assessment of
the risk inherent in a situation” (Sitkin and Pablo, 1992, p.12). Perceived higher risk of
system adoption will decrease the propensity of changing status quo (Heart, 2010; Shim
et al., 2009). Moving to the clouds refers to the migration of IS landscape which
essentially is a risky decision. Benlian et al. (2009) found perceived uncertainty will
negatively affect the attitude toward ERP SaaS adoption. Accordingly, the following
hypotheses are proposed:
H9: Perceived value is negatively related to organizational resistance.
H10: Perceived value is positively related to adoption intention.
H11: Perceived risk is positively related to organizational resistance.
H12: Perceived risk is negatively related to adoption intention.
4.1.5 Control Variables
Organizational size and system age were included to rule out possible explanations.
Organizational size has been regarded as an important determinant of IT innovation
adoption (Lee and Xia, 2006). Organizational size represents the resources and
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capabilities of a firm and contributes to the intention toward adopting innovations (Ke et
al., 2009). With accumulated enhancements, older system age forms the inertia for
remaining current status (Furneaux and Wade, 2011). In this study, organizational size
was measured by retrieving firm’s capital and the number of employee from the
database of China Credit Information Service, while system age was indicated by
respondents about the length of time that current system had been gone live.
4.2 Instrument Development
In order to enhance validity, the items were adapted and developed based on previous
studies. Respondents also were asked to indicate the agreement toward the
organizational inclination of adopting cloud systems. All items were anchored on
seven-point Likert scales (1 = strongly disagree/very low, 7 = strongly agree/very high).
The wording of each item was slightly adapted according to fit the context of cloud
systems adoption (i.e., cloud ERP or cloud CRM). In order to make sure the content
validity, the instrument was reviewed IS researchers and practitioners. A pre-test was
conducted with six senior IT executives. According to the results and commentaries, the
wording and sequence of preliminary measurement items were refined.
4.3 Data Collection
This study conducted a two-phase survey which focused on the adoption of cloud ERP
and cloud CRM. Informants should know about the organizational IT utilization and
deployment. Therefore, a copy of the questionnaire with the statement of study
objective, together with a prepaid reply envelope, was sent to senior IT executives. The
leading manufacturing and service firms listed in the Common Wealth and in the China
Credit Information Service were selected as the sample frame for cloud ERP and cloud
CRM respectively. Totally 1,500 and 1,485 copies of the questionnaire were sent by
mail in July 2011 and in September 2011.
There were totally received 347 responses in the two phases, with a compound response
rate of 11.6% (13.5% for cloud ERP and 8.0% for cloud CRM). The 23 responses were
dropped due to incomplete and invalid responses. Following Chau and Tam’s method
(1997), this study also included a binary measure to identify the adopters. Firms are
identified as adopters if they have the migration plan, approval from top management,
or financial budget and timetable of adoption. Among the 324 respondents, only 21
firms (6 for cloud CRM and 15 for cloud ERP) have developed plans for the adoption of
cloud systems. This indicates that the bulk of firms are skeptical of adopting cloud
systems. Since the purpose of this study is focus on cloud systems adoption, therefore,
these 21 cases were also dropped. A total of 303 usable responses were used for further
analyses.
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The respondents reveal a wide spectrum of industry types. Most of their incumbent
systems have gone live more than five years (69%). Most respondents indicated that
they did not have CRM systems (44%), while only five firms did not implement ERP
systems. Most firms use self-developed CRM (33%). Firms have implemented SAP
(14%), Oracle (16%), and Data Systems (i.e., Workflow/Tip-Top, 29%) as their ERP
systems. Forty eight percent of the companies have integrated their existing systems by
integrating with other applications. The position of most informants is senior manager
(30%) or manager (42%) with approximate ten years work experience and five years
experience on current position.
The comparison between early and late respondents is widely used to assess the extent
of non-response bias. The respondents from phase II were used to represent
non-respondents. In order to evaluate the potential non-response bias, we first compared
the difference of demographic data between phase I and phase II. Organizational capital
and the number of employee were retrieved from the database of China Credit
Information Service. There were no significant difference in terms of capital size (t =
0.27, p = 0.79) and number of employee (t = 0.32, p = 0.75). In addition, the differences
among research constructs were compared. No significant difference was found except
satisfaction with existing system (t = 2.95, p < 0.01). Therefore, non-response biases
should not be serious in this study.
4.4 Results of Quantitative Research
The method of partial least squares (PLS) was chosen for performing data analysis and
for testing research model. PLS can be used to simultaneously handle formative and
reflective constructs and has minimal demands on sample size for validating model
(Chin, 1998). The SmartPLS Verson 2.0.M3 was used for data analysis. The
measurement model was used to confirm validity and reliability of our constructs,
whereas the structural model was performed to examine the structural relationships
among latent variables and to test research hypotheses.
4.4.1 Measurement Model
Cronbach’s α and composite reliability (CR) were used to assess reliability and internal
consistence. The former ranges from 0.73 to 0.97, and the latter ranges from 0.83 to
0.98. Convergent validity of each construct was assessed with factor loading of each
indicator and average variance extracted (AVE) of the construct (Hair, 1998). The factor
loading and AVE should be greater than 0.5 respectively (Fornell and Larcker, 1981;
Hair, 1998). All standardized factor loadings are significant. The value of AVE (from
0.56 to 0.94) is greater than acceptable value. It revealed a good reliability and
convergent validity of our measurement.
16
Discriminant validity was evaluated by looking at the square root of the AVE for a
given construct, and it should be greater than the absolute value of the standardized
correlation of the given construct with any other construct in the analysis (Chin, 1998).
As shown in Table 2, the results had shown a substantial discriminant validity of our
instrument. Thus, the multicollinearity was further checked by calculating variance
inflation factor (VIF) and condition index. The VIF values are below the cut-off
threshold of ten suggested by Hair et al. (1998) and ranges from 1.14 to 3.35. The
condition index value does not exceed 30 (i.e., 28.66). The results indicated that
collinearity does not seem to pose serious problem. These results reveal that all
constructs used in this study are acceptable and reliable.
In addition, following the suggestion of Podsakoff et al. (2003), a Harman’s one-factor
test was conducted to assess the severity of common method bias. Night factors were
Table 2. Reliability, AVE, VIF, and Construct Correlations
SA MP CuP CoP NP SB SC PV PR OR AI
SA 0.96
MP 0.00 0.96
CuP -0.05 0.68 0.92
CoP 0.04 0.42 0.59 0.92
NP 0.03 0.43 0.60 0.82 0.96
SB 0.01 0.40 0.40 0.35 0.33 0.84
SC 0.28 0.21 0.18 0.22 0.19 0.38 0.75
PV -0.06 0.38 0.42 0.36 0.33 0.83 0.20 0.97
PR 0.25 0.14 0.14 0.21 0.11 0.16 0.66 0.07 0.75
OR 0.17 0.05 0.11 0.08 0.01 -0.02 0.47 -0.07 0.59 0.85
AI 0.01 0.34 0.53 0.45 0.46 0.35 0.08 0.43 0.03 -0.05 0.97
Mean 4.77 3.28 2.68 3.20 2.96 4.14 4.93 3.75 4.92 4.39 2.85
SD 1.34 1.23 1.20 1.32 1.30 1.17 1.03 1.30 1.08 1.10 1.38
AVE 0.92 0.91 0.86 0.85 0.92 0.70 0.56 0.94 0.57 0.73 0.94
CR 0.96 0.97 0.95 0.95 0.97 0.96 0.83 0.98 0.91 0.91 0.98
Cronbach’s α 0.91 0.95 0.92 0.91 0.96 0.95 0.73 0.97 0.89 0.87 0.97
VIF 1.14 1.95 2.51 3.35 3.30 3.65 2.09 3.34 1.78 - -
Note: SA = satisfaction with existing system; MP = mimetic pressures; CuP = customer pressures; CoP =
coercive pressures; NP = normative pressures; SB = switching benefits; SC = switching costs; PV =
perceived value; PR = perceived risk; OR = organizational resistance; AI = adoption intention.
AVE = average variance extracted; CR = composite reliability; SD = standard deviation; VIF =
variance inflation factor; The shaded numbers in the diagonal are square roots of the average
variance extracted.
17
produced after performing an unrotated factor analysis. No general factor was emerged
on all research items. The first factor accounted for 29.51% of the variance. Therefore,
the results indicated that all constructs used in this study are acceptable and reliable.
4.4.2 Structural Model of Full Sample
In order to test the research model, the bootstrap re-sampling procedure was conducted
to examine the statistical significance of research hypotheses. Figure 4 presents the
analytic results of aggregate data from cloud ERP sample and cloud CRM sample (n =
303). The model explains a substantial amount of variance in perceived value (R2 =
0.71), perceived risk (R2 = 0.46), organizational resistance (R
2 = 0.36), and adoption
intention (R2 = 0.18).
As to the dimensional structure of institutional pressures, mimetic pressures (γ = 0.29, p
< 0.001), customer pressures (γ = 0.30, p < 0.001), coercive pressures (γ = 0.30, p <
0.001), and normative pressures (γ = 0.31, p < 0.001) significantly contribute to the
formation of institutional pressures. The results provide support for the significance of
eight research hypotheses. Institutional pressures (β = 0.11, p < 0.01) and switching
benefits (β = 0.83, p < 0.001) positively contribute to perceived value, while switching
costs (β = -0.14, p < 0.001) will sabotage the perception. Thus, H3, H5, and H7 are
supported. Switching benefits (β = -0.13, p < 0.01) and switching costs (β = 0.68, p <
0.001) are significantly related to perceived risk. Hence, H6 and H8 are supported.
Perceived value has significant effect on adoption intention (β = 0.43 p < 0.001) and has
a slight, negative association with organizational resistance (β = -0.11 p < 0.05).
Therefore, H9 and H10 are supported. Perceived risk (β = 0.59 p < 0.001) is
significantly related to organizational resistance, providing support of H11.
Figure 5. Research Results of Full Sample (n = 303)
18
In order to assess the influence of control variables, this study followed the process
outlined by Teo et al. (2003) to estimate three models: (1) full model, (2) control model,
and (3) theoretical model. In the full model, both research constructs and control
variables are included, while the theoretical model excluded control variables. Then, the
control model only includes control variables and is used as a benchmark for evaluation.
This analysis can provide the assessment of the true impact of research constructs and
can rule out alternative explanations (Teo et al., 2003). Several cases with incomplete
information of control variables were eliminated. Therefore, it remains 261 cases for
further analysis. Figure 6 shows the PLS results of the full model with control variables.
Most original paths reveal the same results expect the relationship between perceived
value and organizational resistance, which becomes insignificant (β = -0.07). As to
control variables, the results show that system age (β = 0.14, p < 0.01) has a significant
influence on organizational resistance.
In comparison with control model, the full model explains substantive incremental
variances of 34% (37.9% - 3.9%) on organizational resistance (f2 = 0.55) and 21%
(22.6% - 1.6%) on adoption intention (f2 = 0.27). Comparatively, including control
variables only explains an additional 2.3% (37.9% - 35.6%) variance on organizational
resistance (f2 = 0.04) and 0.6% (22.6% - 22.0%) on adoption intention (f2 = 0.01).
These results indicate that the theoretical model is substantive enough to explain a large
proportion of the variance in organizational resistance and in adoption intention.
4.4.3 Mediation Analysis
Figure 6. Research Results with Control Variables (n = 261)
19
This study followed two steps suggested by Subramani (2004) for mediation
examination: (1) the comparison between indirect and direct research model and (2) the
calculation of individual mediated paths.
In order to test the existence of mediation, the research model with a direct effect was
compared with the original research model, see Table 3. For example, in order to test
the mediation between switching benefits, perceived value, and adoption intention, a
direct relation between switching benefits and adoption intention was added into the
original research model. Full mediation exists if the independent variable has no effect
on the dependent variable when the mediator is controlled (Baron and Kenny, 1986).
Therefore, the comparison between indirect and direct model can be statistically tested
by using PLS results (Malhotra et al., 2007). A pseudo-F statistic can be calculated for
assessing the significant difference between two models. Two additional direct paths
from institutional pressures (pseudo F = 67.360, p < 0.001) and switching costs (pseudo
F = 9.280, p < 0.001) to organizational resistance explain additional variance and add
significantly to the explanatory power of the model.
The second step is to assess the role of mediator (i.e., perceived value and perceived
risk). The magnitude of mediation is the product of path coefficients between the
independent variable and mediator and between mediator and the dependent variable.
The results indicate that perceived value mediates three relationships, including
institutional pressures to adoption intention (Z = 1.852, p < 0.05), switching benefits to
adoption intention (Z = 5.848, p < 0.001), and switching costs to adoption intention (Z =
-2.246, p < 0.05). In addition, perceived risk plays as mediator in two relations,
including institutional pressures to organizational resistance (Z = 1.383, p < 0.10) and
switching benefits to organizational resistance (Z = -2.223, p < 0.05).
4.4.4 Comparison between Cloud ERP and Cloud CRM
Table 1. Test for Mediation
Direct Path
R2
Indirect
Model
R2
Direct
Model
Pseudo
F(1, 291) Mediator Magnitude Z
SA AI 0.183 0.185 0.450 PV -0.014 -0.548
SA OR 0.355 0.356 0.712 PR 0.035 1.143
IP AI 0.183 0.337 0.450 PV 0.026 1.852*
IP OR 0.355 0.356 67.360*** PR 0.045 1.383†
SB AI 0.183 0.184 1.355 PV 0.371 5.848***
SB OR 0.355 0.358 0.355 PR -0.078 -2.223*
SC AI 0.183 0.183 0.000 PV -0.059 -2.246*
SC OR 0.355 0.375 9.280** PR 0.319 1.255
Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.
20
The subgroup analysis is suggested as the proper methodology for evaluating the
moderating effect with nominal moderating variables (Chai et al., 2011). Table 4
presents the summary of statistical comparison of paths between cloud ERP and cloud
CRM. These results indicate that most relationships reveal statistically difference
according to the type of cloud systems except two relations: satisfaction with existing
system to perceived value and switching cost to perceived risk.
5. Discussion and Conclusions
The adoption of cloud systems is more complex then generic cloud computing adoption.
Through the theoretical lens of the status quo bias, our research provides a different
perspective for investigating why most firms react conservatively on adopting cloud
systems. This study firstly conducted a meta-analytic review to examine the
significance and magnitude of the proposed model. Based on the findings of
meta-analysis results, a two-phase survey was conducted to empirically test the research
model. The findings indicate that perceived value, which facilitate firms’ propensity to
Table 2. Statistical Comparison of Paths between Cloud ERP and Cloud CRM
Model Relationships
Cloud ERP (n = 231)
Cloud CRM (n = 72)
Statistical Comparison Comparison
β t-value β t-value t-value
SA PV -0.03 0.68 -0.03 0.50 0.84
SA PR -0.00 0.07 0.04 0.42 -4.91 *** E < C
MP IP 0.30 *** 21.82 0.27 *** 9.45 11.95 *** E > C
CuP IP 0.29 *** 25.97 0.32 *** 15.47 -14.80 *** E < C
CoP IP 0.29 *** 22.22 0.32 *** 16.71 -12.23 *** E < C
NP IP 0.33 *** 24.00 0.27 *** 9.37 22.04 *** E > C
IP PV 0.09 * 2.05 0.14 † 1.67 -6.51 *** E < C
IP PR 0.03 0.45 0.25 * 2.46 -23.87 *** E < C
SB PV 0.82 *** 24.23 0.84 *** 11.00 -3.27 ** E < C
SB PR -0.14 * 2.31 -0.07 0.74 -6.98 *** E < C
SC PV -0.16 *** 3.55 -0.06 0.60 -11.47 *** E < C
SC PR 0.65 *** 11.23 0.64 *** 6.02 1.00
PV OR -0.07 1.40 -0.04 0.22 -2.40 * E < C
PV AI 0.39 *** 6.43 0.47 *** 3.52 -7.56 *** E < C
PR OR 0.64 *** 15.81 0.42 * 2.22 16.67 *** E > C
PR AI -0.02 0.24 0.12 0.78 -10.71 *** E < C
R2
PV 0.69
0.77
PR 0.39
0.58
OR 0.42
0.16
AI 0.15
0.28
Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001.
21
adopt, is influenced by the institutional pressures and by the cost/benefit evaluation.
Switching costs and benefits are primary determinants of perceived risk, which will in
turn increase organizational resistance. Our findings illustrate how internal and external
structure inertia facilitate or retard firm to change. The higher satisfaction and order
system age refers to the more appropriate deployment of current system, which
introduces internal inertia for firm. By conforming to organizational network,
institutional pressures provide a legitimacy of moving to cloud systems. Therefore, the
propensity of a firm to refuse or adopt cloud systems will base on the evaluation of
internal/external situation and of the incumbent system. We hope this study can
stimulate more works on these important issues.
An interesting finding is that institutional pressure has a positive association with
perceived risk under cloud CRM context. This result is contrary to the H4, which
suggest a negative relationship. The post hoc analysis provides a plausible explanation.
The results indicate that the perception of customer pressures is significant different
between cloud ERP and cloud CRM (F = 7.09, p < 0.001). Specifically, respondents of
cloud CRM perceive higher pressures from their customer. Firms in service industries
often gain the competitive advantage through providing innovated or differentiated
service. As to cloud systems, IS becomes a commodity which make their competitors
easier to duplicate the service process which rely IS to provide value-added benefits
(Jeong and Stylianou, 2010). Therefore, service firms perceive higher institutional
pressures toward moving on to cloud will lead to higher perceived risk of losing the
competitive advantage providing through existing IS.
5.1 Implications for the academic and practitioners
Through a meta-analytic review and an empirical survey, this study proposes an
integrated framework toward new system adoption from the status quo bias theory. This
research applied the status quo bias theory to provide a foundation to understand why
companies still adhere to their in house, on-premise IS architecture. Then, this study
empirically investigated how most firms react conservatively on adopting cloud systems.
The propensity of a firm to refuse or adopt cloud systems will base on the evaluation of
internal/external situation and of the incumbent system.
The findings can provide suggestions for managers who are facing the dilemma of
adopting cloud systems or not. The preliminary findings reveal possible status quo bias
effects on firms’ intention and resistance of adopting cloud systems. On the other hand,
to maintain firms’ competitive advantages, managers also need to consider the extent of
adoption by their competitors and partners. By using the existing system as an anchor,
the biased perception of gains and losses from cloud systems adoption will enlarge risks
22
of adopting. Instead of moving on to cloud systems, managers may choose to remain
current deployment or to upgrades.
The results also provide important implications for cloud service vendors and policy
makers. Service vendors can exert the institutional effects by constructing breakthrough,
successful stories. For example, vendors can promote their applications by cooperating
with some leading firms. In addition, in order to provide an appropriate solution, service
providers should take firm’s status quo of existing systems and the linkages between the
focal firm and its partners when promoting cloud systems. Since the government’s
promotion often plays an important role in the diffusion of an IT innovation,
governments can develop supportive regulations to encourage the adoption and host
some workshops to share experiences with potential adopters. Once a network member
successfully adopts a cloud system, the adoption by other members will unfold through
institutional promotion.
5.2 Limitations and future research directions
The findings of this study should be interpreted according to some limitations. First, the
data was collected from manufacturing and service firms in Taiwan. In addition, we
only focus on two specific types of cloud-based applications. Future research can
replicate and examine this model across different industries and more application types.
Second, our survey is cross-sectional. Therefore, it will limit the ability to draw causal
inferences. Further research can design a delicate even longitudinal study to test the
possible causal relationships. Third, there are a few firms have plans for migrating to
the cloud systems. In the future, with more mature market, future research can compare
the difference according to the categories of adopters. Finally, this study incorporated
some possible status quo bias factors in cloud systems adoption. As an explorative study,
we by no means enumerate all possible factors. Instead of embracing cloud computing,
firms either can choose to remain current deployment or to update their systems. Future
research can extend our model by considering other possible determinants and compare
possible decision.
23
References
Note: references marked with an asterisk indicate studies included in the meta-analyses.
1. Anandasivam, A. and Premm, M., 2009. Bid Price Control and Dynamic Pricing in
Clouds, Proceedings of the Seventeenth European Conference on Information
Systems (ECIS 2009), Verona, Italy.
2. Barney, J.B., 1991. Firm Resources and Sustained Competitive Advantage, Journal
of Management, 17(1), 99-120.
3. Baron, R.M. and Kenny, D.A., 1986. The Moderation-Mediator Variable
Distinction in Social Psychological Research: Conceptual, Strategic, and Statistical
Considerations, Journal of Personality and Social Psychology, 51(6), 1173-1182.
4. *Benlian, A. and Hess, T., 2011. Opportunities and Risks of Software-as-a-Service:
Findings from a Survey of IT Executives, Decision Support Systems, 52(1),
232-246.
5. Benlian, A., Hess, T., and Buxmann, P., 2009. Drivers of SaaS-Adoption: An
Empirical Study of Different Application Types, Business & Information Systems
Engineering, 1(5), 359-369.
6. *Benlian, A., Koufaris, M., and Hess, T., 2011. Service Quality in
Software-as-a-Service: Developing the SaaS-Qual Measure and Examining Its
Role in Usage Continuance, Journal of Management Information Systems, 28(3),
85-126.
7. Chai, S., Das, S., and Rao, H.R., 2011. Factors Affecting Bloggers’ Knowledge
Sharing: An Investigation across Gender, Journal of Management Information
Systems, 28(3), 309-341.
8. Chau, P.Y.K. and Tam, K.Y., 1997. Factors Affecting the Adoption of Open
Systems: An Exploratory Study, MIS Quarterly, 21(1), 1-24.
9. *Chen, A.J., Watson, R.T., Boudreau, M.C., and Karahanna, E., 2009.
“Organizational Adoption of Green IS & IT: An Institutional Perspective,”
Proceedings of the Thirtieth International Conference on Information Systems
(ICIS 2009), Phoenix, Arizona.
10. Chin, W., 1998. The Partial Least Squares Approach to Structural Equation
Modeling, In: G.A. Mrcoulides. ed. Modern Methods for Business Research,
Hillsdale, NJ: Lawrence Erlbaum Associates, 295-336.
24
11. *Chwelos, P., Benbasat, I., and Dexter, A., 2001. Research Report: Empirical Test
of an EDI Adoption Model, Information Systems Research, 12(3), 304-321.
12. Cohen, J. and Cohen, P., 1983. Applied Multiple Regression/Correlation Analysis
for the Behavioral Sciences, NJ: Erlbaum.
13. DeLone, W.H. and McLean, E.R., 2003. The DeLone and McLean Model of
Information Systems Success: A Ten-Year Update, Journal of Management
Information Systems, 19(4), 9-30.
14. DiMaggio, P.J. and Powell, W.W., 1983. The Iron Cage Revisited: Institutional
Isomorphism and Collective Rationality in Organizational Fields, American
Sociological Review, 48(2), 147-160.
15. Fornell, C. and Larcker, D.F., 1981. Structural Equation Models with Unobservable
Variables and Measurement Errors, Journal of Marketing Research, 18(2), 39-50.
16. Furneaux, B. and Wade, M., 2011. An Exploration of Organizational Level
Information Systems Discontinuance Intentions, MIS Quarterly, 35(3), 573-598.
17. Gartner, 2011. Gartner Executive Programs Worldwide Survey of More Than 2,000
CIOs Identifies Cloud Computing as Top Technology Priority for CIOs in 2011,
Retrieved March 13, 2012, from: http://www.gartner.com/it/page.jsp?id=1526414.
18. Gill, R., 2011. Why Cloud Computing Matters to Finance, Strategic Finance, 92(7),
43-47.
19. *Goebel, C., Tribowski, C., and Gunther, O., 2009. Adoption of Cross-Company
RFID: An Empirical Analysis of Perceived Influence Factors, Proceedings of the
Seventeenth European Conference on Information Systems (ECIS 2009), Verona,
Italy.
20. Gosain, S., 2004. Enterprise Information Systems as Objects and Carriers of
Institutional Forces: The New Iron Cage? Journal of the Association for
Information Systems, 5(4), 151-182.
21. *Goswami, S., Teo, H.H., and Chan, H.C., 2008. Real Options from RFID
Adoption: The Role of Institutions and Managerial Mindfulness, Proceedings of
the Twenty Ninth International Conference on Information Systems (ICIS 2008),
Paris, France.
22. Hair, J.F., Anderson, R.E., Tatham, R.L. and Black, W.C., 1998. Multivariate Data
Analysis, 5th ed. NJ: Prentice Hall.
25
23. Hannan, M.T. and Freeman, J., 1984. Structural Inertia and Organizational Change,
American Sociological Review, 49(2), 149-164.
24. Hayes, B., 2008. Cloud Computing, Communications of the ACM, 51(7), 9-11.
25. *Heart, T., 2010. Who Is Out There? Exploring the Effects of Trust and Perceived
Risk on SaaS Adoption Intentions, DATA BASE for Advances in Information
Systems, 41(3), 49-68.
26. Hofmann, W., Gawronski, B., Gschwendner, T., Le, H., and Schmitt, M., 2005. A
Meta-Analysis on the Correlation between the Implicit Association Test and
Explicit Self-Report Measures, Personality and Social Psychology Bulletin, 31(10),
1369-1385.
27. Hulland, J., Wade, M.R., and Antia, K.D., 2007. The Impact of Capabilities and
Prior Investments on Online Channel Commitment and Performance, Journal of
Management Information Systems, 23(4), 109-142.
28. *Iskandar, B.Y., Kurokawa, S., and LeBlanc, L.J., 2001. Adoption of Electronic
Data Interchange: The Role of Buyer-Supplier Relationships, IEEE Transactions
on Engineering Management, 48(4), 505-517.
29. Jeong, B.K. and Stylianou, A.C., 2010. Market Reaction to Application Service
Provider (ASP) Adoption: An Empirical Investigation, Information & Management,
47(3), 176-187
30. Joshi, K., 1991. A Model of Users’ Perspective on Change: The Case of
Information Systems Technology Implementation, MIS Quarterly, 15(2), 229-242.
31. Kahneman, D. and Tversky, A., 1979. Prospect Theory: An Analysis of Decisions
under Risk, Econometrica, 47(2), 263-291.
32. *Ke, W., Liu, H., Wei, K.K., Gu, J., and Chen, H., 2009. How Do Mediated and
Non-Mediated Power Affect Electronic Supply Chain Management System
Adoption? The Mediating Effects of Trust and Institutional Pressures, Decision
Support Systems, 46(4), 839-851.
33. Khajeh-Hosseini, A., Sommerville, I., and Sriram, I., 2010. Research Challenges
for Enterprise Cloud Computing, Proceeding of the First ACM Symposium on
Cloud Computing (SOCC 2010), Indianapolis, Indiana.
34. *Khalifa, M. and Davison, R.M., 2006. SME Adoption of IT: The Case of
Electronic Trading Systems, IEEE Transactions on Engineering Management,
53(2), 275-284.
26
35. *Kim, C., Oh, E., Shin, N., and Chae, M., 2009. An Empirical Investigation of
Factors Affecting Ubiquitous Computing Use and U-Business Value, International
Journal of Information Management, 29(6), 436-448.
36. Kim, H.W., 2010. Managing User Resistance to Open Source Migration,
Proceedings of the Thirty First International Conference on Information Systems
(ICIS 2010), St. Louis, USA.
37. Kim, H.W., 2011. The Effects of Switching Costs on User Resistance to Enterprise
Systems Implementation, IEEE Transactions on Engineering Management, 58(3),
471-482.
38. Kim, H.W. and Kankanhalli, A., 2009. Investigating User Resistance to
Information Systems Implementation: A Status Quo Bias Perspective, MIS
Quarterly, 33(3), 567-582.
39. Kim, T.Y., Oh, H., and Swaminathan, A., 2006. Framing Interorganizational
Network Change: A Network Inertia Perspective, Academy of Management
Review, 31(3), 704-720.
40. Kirca, A.H., Hult, G.T.M., Roth, K., Cavusgil S.T., Perryy, M.Z., Akdeniz, M.B.,
Deligonul, S.Z., Mena, J.A., Pollitte, W.A., Hoppner, J.J., Miller, J.C., and White,
R.C., 2011. Firm-Specific Assets, Multinationality, and Financial Performance: A
Meta-Analytic Review and Theoretical Integration, Academy of Management
Journal, 54(1), pp. 47-72.
41. Kremers, M. and Van Dissel, H., 2000. ERP System Migrations: A Provider’s
versus a Customer’s Perspective, Communications of the ACM, 43(4), 53-56.
42. Kuan, K.K.Y. and Chau, P.Y.K., 2001. A Perception-Based Model for EDI
Adoption in Small Businesses Using a Technology-Organization-Environment
Framework, Information & Management, 38(8), 507-521.
43. *Lai, V.S., Liu, C.K.W., Lai, F., and Wang, J., 2010. What Influences ERP Beliefs:
Logical Evaluation or Imitation? Decision Support Systems, 50(1), 203-212.
44. *Lee, S. and Lim, G.G., 2003. The Impact of Partnership Attributes on EDI
Implementation Success, Information & Management, 41(2), 135-148.
45. Lee, G. and Xia, W., 2006. Organizational Size and IT Innovation Adoption: A
Meta-Analysis, Information & Management, 43(8), 975-985.
46. *Li, Y., Tan, C.H., Teo, H.H., and Siow, A., 2005. A Human Capital Perspective of
Organizational Intention to Adopt Open Source Software, Proceedings of the
27
Twenty Sixth International Conference on Information Systems (ICIS 2005),
Milwaukee, Wisconsin.
47. *Liang, H., Saraf, N., Hu, Q., and Xue, Y., 2007. Assimilation of Enterprise
Systems: The Effect of Institutional Pressures and the Mediating Role of Top
Management, MIS Quarterly, 31(1), 59-87.
48. *Lin, H.F. and Lin, S.M., 2008. Determinants of E-Business Diffusion: A Test of
the Technology Diffusion Perspective, Technovation, 28(3), 135-145.
49. Lipsey, M.W. and Wilson, D.B., 2001. Practical Meta-Analysis, Thousand Oaks,
London: Sage.
50. *Liu, C., Sia, C.-L., and Wei, K.K., 2008. Adopting Organization Virtualization in
B2B Firms: An Empirical Study in Singapore, Information & Management, 45(7),
429-437.
51. *Malhotra, A., Gosain, S., and El Sawy, O.A., 2007. Leveraging Standard
Electronic Business Interfaces to Enable Adaptive Supply Chain Partnerships,
Information Systems Research, 18(3), 260-279.
52. Mata, F.J., Fuerst, W.L., and Barney, J.B., 1995. Information Technology and
Sustained Competitive Advantage: A Resource-Based Analysis, MIS Quarterly,
19(4), 487-505.
53. Mell, P. and Grance, T,. 2011. The NIST Definition of Cloud Computing (draft),”
Retrieved March 13, 2012, from:http://csrc.nist.gov/publications/nistpubs/
800-145/SP800-145.pdf.
54. Melville, N., Kraemer, K., and Gurbaxani, V., 2004. Review: Information
Technology and Organizational Performance: An Integrative Model of IT Business
Value, MIS Quarterly, 28(2), 283-322.
55. Miranda, S.M. and Kim, Y.M., 2006. Professional Versus Political Contexts:
Institutional Mitigation and the Transaction Cost Heuristic in Information Systems
Outsourcing, MIS Quarterly, 30(3), 725-753.
56. *Mishra, A.N. and Agarwal, R., 2010. Technological Frames, Organizational
Capabilities, and IT Use: An Empirical Investigation of Electronic Procurement,
Information Systems Research, 21(2), 249-270.
57. *Mishra, A.N., Konana, P., and Barua, A., 2007. Antecedents and Consequences of
Internet Use in Procurement: An Empirical Investigation of U.S. Manufacturing
Firms, Information Systems Research, 18(1), 103-120.
28
58. *Molla, A. and Abareshi, A., 2011. Green IT Adoption: A Motivational Perspective,
Proceedings of the Fifteenth Pacific Asia Conference on Information Systems
(PACIS 2011), Brisbane, Queensland.
59. Mustonen-Ollia, E. and Lyytinen, K., 2003. Why Organizations Adopt Information
Systems Process Innovations: A Longitudinal Study Using Diffusion of Innovation
Theory, Information Systems Journal, 13(3), 275-297.
60. *Nakayama, M. and Sutcliffe, N.G., 2005 Exploratory Analysis on the Halo Effect
of Strategic Goals on IOS Effectiveness Evaluation, Information & Management,
42(2), 275-288.
61. Oracle, 2010. Oracle While Paper: SAP ERP in the Cloud, Retrieved March 13,
2012, from: http://www.oracle.com/us/solutions/sap/database/
sap-erp-cloud-352626.pdf.
62. Petter, S. and McLean, E.R., 2009. A Meta-Analytic Assessment of the DeLone
and McLean IS Success Model: An Examination of IS Success at the Individual
Level, Information & Management, 46(3), 159-166.
63. Podsakoff, P.M., MacKenzie, S.B., Lee, J.Y., and Podsakoff, N.P., 2003. Common
Method Biases in Behavioral Research: A Critical Review of the Literature and
Recommended, Journal of Applied Psychology, 88(5), 879-903.
64. Polites, G.L. and Karahanna, E., 2012. Shackled to the Status Quo: The Inhibiting
Effects of Incumbent System Habit, Switching Costs, and Inertia on New System
Acceptance, MIS Quarterly, 36(1), 21-42.
65. *Ranganathan, C., Teo, T.S.H., and Dhaliwal, J., 2011. Web-Enabled Supply Chain
Management: Key Antecedents and Performance Impacts, International Journal of
Management, 31(6), 533-545.
66. Ray, G., Muhanna, W.A., and Barney, J.B., 2005. Information Technology and the
Performance of the Customer Service Process: A Resource-Based Analysis, MIS
Quarterly, 29(4), 625-652.
67. *Riemenschneider, C.K., Harrison, D.A., and Mykytyn Jr., P.P., 2003.
Understanding IT Adoption Decisions in Small Business: Integrating Current
Theories, Information & Management, 40(4), 269-285.
68. Rogers, E.M., 2003. Diffusion of Innovations fifth ed., New York: Free Press.
69. Samuelson, W. and Zeckhauser, R., 1988. Status Quo Bias in Decision Making,
Journal of Risk and Uncertainty, 1(1), 7-59.
29
70. *Saya, S., Pee, L.G., and Kankanhalli, A., 2010. The Impact of Institutional
Influences on Perceived Technological Characteristics and Real Options in Cloud
Computing Adoption, Proceedings of the Thirty First International Conference on
Information Systems (ICIS 2010), Saint Louis, Missouri.
71. Seddon, P.B., 1997. A Respecification and Extension of the Development of the
DeLone and McLean Model of IS Success, Information Systems Research, 8(3),
240-253.
72. *Shim, S., Chae, M., and Lee, B., 2009. Empirical Analysis of Risk-Taking
Behavior in IT Platform Migration Decisions, Computers in Human Behavior,
25(6), 1290-1305.
73. Sitkin, S.B. and Pablo, A.L., 1992. Reconceptualizing the Determinants of Risk
Behavior, Academy of Management Review, 17(1), 9-38.
74. *Son, J.Y. and Benbasat, I., 2007. Organizational Buyers’ Adoption and Use of
B2B Electronic Marketplaces: Efficiency- and Legitimacy-Oriented Perspectives,
Journal of Management Information Systems, 24(1), 55-99.
75. Subramani, M., 2004. How Do Suppliers Benefit from Information Technology
Use in Supply Chain Relationships? MIS Quarterly, 28(1), 45-73.
76. Susarla, A., Barua, A., and Whinston, A.B., 2009. A Transaction Cost Perspective
of the “Software as a Service” Business Model, Journal of Management
Information Systems, 26(2), 205-240.
77. *Teo, H.H., Wei, K.K., and Benbasat, I., 2003. Predicting Intention to Adopt
Interorganizational Linkages: An Institutional Perspective, MIS Quarterly, 27(1),
19-49.
78. *Thong, J.Y.L., 1999. An Integrated Model of Information Systems Adoption in
Small Business, Journal of Management Information Systems, 15(4), 187-214.
79. Tornatzky, L.G. and Fleischer, M., 1990. The Processes of Technological
Innovation, Lexington Books, Lexington, MA.
80. Van de Ven, A.H. and Poole, M.S., 1995. Explaining Development and Change in
Organizations, Academy of Management Review, 20(3), 510-540.
81. Viswesvaran, C. and Ones, D.S., 1995. Theory Testing: Combining Psychometric
Meta-Analysis and Structural Equations Modeling, Personnel Psychology, 48(4),
865-885.
30
82. Weinhardt, C., Anandasivam, A., Blau, B., Borissov, N., Meinl, T., Michalk, W.,
and Stöβer, J., 2009. Cloud Computing: A Classification, Business Models, and
Research directions, Business & Information Systems Engineering, 1(5), 391-399.
83. Williamson, O.E., 1981. The Economics of Organization: The Transaction Cost
Approach, American Journal of Sociology, 87(3), 548-577.
84. Wu, J. and Lederer, A., 2009. A Meta-Analysis of the Role of Environment-Based
Voluntariness in Information Technology Acceptance, MIS Quarterly, 33(2),
419-432.
85. *Wu, F., Zsidisin, G.A., and Ross, A.D., 2007. Antecedents and Outcomes of
E-Procurement Adoption: An Integrative Model, IEEE Transactions on
Engineering Management, 54(3), 576-587.
86. *Xu, J. and Quaddus, M., 2006. Antecedents of Knowledge Management Systems
Adoption and Diffusion in Australia: A Partial Least Square Approach, Proceedings
of the Tenth Pacific Asia Conference on Information Systems (PACIS 2006), Kuala
Lumpur, Malaysia.
87. *Xue, L., Zhang, C., Ling, H., and Zhao, X., 2011. Impact of IT Unit’s Decision
Right on Organizational Risk Taking in IT, Proceedings of the Thirty Second
International Conference on Information Systems (ICIS 2011), Shanghai, China.
88. Zhang, Q., Cheng, L., and Boutaba, R., 2010. Cloud Computing: State-of-the-Art
and Research Challenges, Journal of Internet Services and Applications, 1(1), 7-18.
89. *Zhu, K., Kraemer, K.L., Gurbaxani, V., and Xu, S.X., 2006a. Migration to
Open-Standard Interorganizational Systems: Network Effects, Switching Costs,
and Path Dependency, MIS Quarterly, 30(Special Issue on Standard Making),
515-539.
90. *Zhu, K., Kraemer, K.L., and Xu, S., 2006b. The Process of Innovation
Assimilation by Firms in Different Countries: A Technology Diffusion Perspective
on E-Business, Management Science, 52(10), 1557-1576.