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JEIM
33,3
What drives organizations to
switch to cloud ERP systems? The
impacts of enablers and inhibitors
600
Received 11 July 2019
Revised 25 September 2019
5 December 2019
Accepted 28 December 2019
Yu-Wei Chang
Department of Business Management,
National Taichung University of Science and Technology, Taichung, Taiwan
Abstract
Purpose – Switching to public cloud enterprise resource planning (ERP) systems not only provides financial
and functional benefits to organizations, but also results in sunk costs of incumbent systems and uncertainty
costs of cloud systems. The purpose of this study is to investigate the enablers and inhibitors concerning
switching to cloud ERP systems at the organizational level.
Design/methodology/approach – Data were collected from 212 top managers and owners of the enterprises
in Taiwan, and 10 hypotheses were examined using structural equation modeling.
Findings – Technological (system quality), organizational (financial advantage), and environmental contexts
(industry pressure) are found to be the antecedents of switching benefits. Perceived risk of cloud ERP systems
and satisfaction with and breadth of use of incumbent ERP systems are found to be the predictors of switching
costs. Switching benefits positively affect switching intention, but switching costs negatively affect switching
intention.
Research limitations/implications – This study develops a theoretical model grounded in a set of
theoretical foundations, including two-factor theory, technology-organization-environment (TOE) framework,
information systems (IS) success model, and expectation confirmation theory (ECT). Two-factor theory is used
to characterize switching benefits and costs that affect switching intention. Technological factors come from IS
success model, and the factors affecting benefits are organized based on TOE framework. Sunk costs of
incumbent ERP systems are developed based on ECT.
Originality/value – Different from previous studies on cloud computing adoption, this study provides
insights into switching intention to cloud computing. The study also proposes an integrated model grounded in
multiple perspectives to explain organizations’ decisions to switch to cloud ERP systems. These findings help
cloud service providers better understand how to promote cloud ERP adoption from technical, organizational,
and environmental perspectives.
Keywords Cloud computing, Two-factor theory, Technology-organization-environment framework,
Information systems success model, Expectation confirmation theory
Paper type Research paper
Journal of Enterprise Information
Management
Vol. 33 No. 3, 2020
pp. 600-626
© Emerald Publishing Limited
1741-0398
DOI 10.1108/JEIM-06-2019-0148
1. Introduction
In recent years, cloud computing has become a common phrase in daily life. Cloud computing
provides a foundation on which to develop new business products, services, and solutions
over the Internet (Gen, 2008). Cloud services developed for organizations can be further
categorized as public, private, and hybrid clouds. According to the 2019 cloud report, 94
percent of organizations use clouds; of these, 91 percent use public clouds, and 72 percent use
private clouds (Flexera, 2019). According to a recent Gartner survey, the public cloud services
market reached 182.4 billion in 2018. Gartner also forecasts that the global public cloud
services market is expected to grow to 214.3 billion in 2019, with a growth rate of 17.5 percent
(Gartner, 2019). This prediction indicates that several organizations are increasing their
investment in public clouds.
Pubic cloud computing has been applied to the development of various business software,
such as enterprise resource planning (ERP) systems, business intelligence, and customer
relationship management (CRM) (Chang et al., 2019). ERP systems are important to most
organizations because the systems are used to manage core business processes, including
financial accounting, management accounting, human resources, manufacturing, and order
processing. Therefore, cloud ERP systems are a common application of public clouds in
organizations (Low et al., 2011). As with the development of public clouds, some of the world’s
leading software suppliers, like SAP, Oracle, and Microsoft, offer cloud ERP systems for their
customers.
Recently, public cloud ERP systems have received increasing attention because of their
apparent financial and functional benefits. Organizations are driven to adopt cloud ERP
systems by the benefits, including low start-up cost, pay only for utilized systems, up-to-date
functionalities, and rapid deployment (Kenyon, 2012). An increasing number of studies on
cloud computing have identified adoption factors of cloud ERP systems, such as the
availability and characteristics of the technologies, financial resources, technical competence,
industry pressure, and government policy (Lee et al., 2013; Low et al., 2011; Paquette
et al., 2010).
However, most existing organizations currently implement on-premise ERP systems. An
investigation of the adoption of new information systems (IS) may not accurately explain the
dilemma faced by organizations, because switching to cloud ERP systems means giving up
incumbent ERP systems. Sunk costs of incumbent ERP systems and uncertainty costs of
cloud ERP systems might tip the balance toward on-premise ERP systems (Lee et al., 2013;
Lian et al., 2014). Thus, switching intention should be considered to be different from adoption
intention, as the factors of adopting new IS and giving up incumbent IS should be accounted
for. Therefore, it is important to gain an understanding of what factors motivate/demotivate
organizations to switch to cloud ERP systems.
Although several studies have identified factors conducive to cloud computing adoption
(Hsu and Lin, 2016; Gangwar et al., 2015; Gutierrez et al., 2015; Lucia-Palacios et al., 2016;
Martins et al., 2019; Safari et al., 2015; Oliveira et al., 2014), a few studies have addressed
switching issues to cloud computing at the organizational level (Fan et al., 2015; LuciaPalacios et al., 2016). Because these studies did not consider switching intention as the
dependent variable and did not highlight the enablers and inhibitors concerning switching to
public clouds, it may be difficult for top managers and owners of enterprises to determine
what factors will benefit organizations and negatively impact organizations. Accordingly, it
is important to understand the factors associated with switching decisions, so that
organizations intending to switch to cloud ERP systems could take appropriate actions.
In order to fill this gap, this study uses two-factor theory to investigate the effects of
switching benefits and costs on switching intention. Certain IS studies applied two-factor
theory to investigate two categories of factors that influence system adoption (Hachicha and
Mezghani, 2018; Kang, 2018; Wu et al., 2019; Park and Ryoo, 2013). Thus, we characterize the
factors as motivators (i.e. switching benefits) and demotivators (i.e. switching costs).
However, switching benefits and costs are too generic, so two-factor theory must be
strengthened with other models or external variables (Hachicha and Mezghani, 2018).
Because technology-organization-environment (TOE) framework is suitable for studying
cloud computing adoption from technological, organizational, and environmental
perspectives (Gangwar et al., 2015; Gutierrez et al., 2015; Senyo and Effah, 2016), this study
integrates two-factor theory with TOE framework. The factors that affect benefits are
organized based on TOE framework, and the factors that affect costs are sunk costs of
incumbent ERP systems and uncertainty costs of cloud ERP systems. Some relevant factors
are developed based on expectation confirmation theory (ECT) and information systems (IS)
success model. Therefore, the research model grounded in multiple theories can help
investigate the factors that influence organizations’ switching to cloud ERP systems.
This study examines the effects of the enablers and inhibitors on switching intention to
cloud ERP systems. We confirm that switching benefits and costs independently and
simultaneously affect switching intention. The results also show that technological,
organizational, and environmental factors can enhance switching benefits, while sunk
Enablers and
inhibitors of
cloud ERP
switching
601
JEIM
33,3
602
costs of incumbent ERP systems and uncertainty costs of cloud ERP systems can intensify
the effects of switching costs. These findings can help cloud service providers better
understand organizations’ decisions regarding platform switching. The understanding can
help improve the functionalities of cloud ERP systems, enhance services, develop effective
marketing agendas, and evaluate potential customers.
2. Literature review
2.1 Cloud computing adoption
Cloud computing has three deployment models: public, private, and hybrid clouds. Public
clouds allow organizations to access on-demand systems and resources without owning core
information technology (IT) infrastructures. Private clouds require organizations to deploy
their own core IT infrastructures to utilize the systems. Hybrid clouds provide systems with a
mixture of public and private resources. The model allows organizations to access critical
services and resources provided internally through an intranet, and noncritical services and
resources provided by cloud service providers over the Internet (Geczy et al., 2012).
According to the NIST (2002), there are three cloud service models: Infrastructure as a
Service (IaaS), Platform as a Service (PaaS), and Service as a Service (SaaS). IaaS provides the
delivery of hardware and network infrastructure as services, and it allows system
administrators to process, store, and manage data on the systems. PaaS provides computing
platforms and solutions as services, and IT developers can design, develop, and test
applications on cloud platforms. SaaS provides the functionalities of software systems as ondemand services. Through Internet connections, end users can access software applications
provided by cloud service providers (Geczy et al., 2012; Low et al., 2011; Park and Ryoo, 2013).
The issue of using emerging cloud technologies is very attractive to researchers and
practitioners. From the perspective of different user adoptions, these studies can be divided into
two main streams. One is related to personal cloud applications, and the other is associated with
enterprise cloud applications. Table I summarizes previous research on personal and enterprise
cloud systems. Personal cloud applications include office systems provided by Google Apps and
Microsoft Office Online, and network storages offered by Apple iCloud. Theoretical models, such
as technology acceptance model (TAM), diffusion of innovation (DOI), and theory of reasoned
action (TRA), are used to investigate the effects of technological and innovative factors on cloud
Application
Research
issue
Personal cloud
applications
Switching
intention
Adoption
Behavioral
intention
Adoption
Table I.
Personal and
enterprise cloud
applications
Enterprise
cloud
applications
Adoption
Adoption
Cloud systems
Subject
Reference
Cloud storage
services/Google
apps
Mobile cloud
services
E-invoice service
Personal
Cheng et al. (2019), Park and
Ryoo (2013)
Personal
Personal
Park and Kim (2014), Schepman
et al. (2012)
Lian (2015)
Cloud
computing
platform
SaaS
Community
colleges and
university
Enterprise
Cloud
computing
Enterprise
Behrend et al. (2011), Sabi et al.
(2016)
Lee et al. (2013), Safari et al.
(2015)
Gangwar et al. (2015), Gupta
et al. (2013), Gutierrez et al.
(2015), Low et al. (2011), Senyo
and Effah (2016)
computing adoption (Park and Ryoo, 2013). Based on TAM, Behrend et al. (2011) and Sabi et al.
(2016) investigated the determinants of cloud computing adoption in community colleges and
universities. The factors influencing the personal cloud adoption of mobile services and e-invoice
are also examined (Park and Kim, 2014; Lian, 2015; Schepman et al., 2012). In the results of their
research, the usability, interfaces, and functionalities of cloud computing are found to influence
users to adopt cloud computing.
Enterprise cloud systems include cloud ERP systems, cloud CRM, and cloud human
capital management (HCM) (Lee et al., 2013; Low et al., 2011). In addition to technological
factors, organizational and environmental factors should be considered to examine cloud
computing adoption by enterprises or small and medium-sized enterprises (SMEs) (Gupta
et al., 2013). Thus, TOE framework is used as the main theoretical foundation of cloud
computing adoption because the framework considers the effects of technological,
organizational, and environmental factors on IS innovation adoption. TOE factors have
been verified to be the determinants of organizations’ adoption of cloud systems (Gangwar
et al., 2015; Gutierrez et al., 2015; Senyo and Effah, 2016).
2.2 Cloud computing enablers and inhibitors
The advantages of adopting cloud computing can be broadly categorized into three
dimensions: deployment, financial, and functional (Geczy et al., 2012). First, the deployment of
cloud systems is similar to outsourcing, and most cloud systems are modularized. Compared
with traditional IT deployment, the deployment of cloud systems for organizations is easy
and fast. Second, cloud systems have the advantages of financial flexibility and cost savings.
Cloud service providers allow customers to pay only for utilized systems on a monthly,
quarterly, or semiannual basis. Organizations might reduce the costs of in-house IT staff and
hardware and software infrastructures by deploying cloud systems (Gen, 2009). Finally,
cloud systems offer the latest functionalities such that cloud service providers will keep their
systems and services up-to-date to remain competitive.
Easy-/fast-to-deploy and up-to-date functionalities are desired attributes of system quality
(Benlian et al., 2011–2012; Ifinedo et al., 2010), which, in general, is measured by adaptability,
availability, reliability, and response time (DeLone and McLean, 2003). Thus, instead of
measuring the effects of the two dimensions, this study investigates the effect of system
quality as a whole. Structured payments, pay for use, and cost savings are financial
advantages of using cloud systems. Financial advantage can stem from organizational
contexts, and system quality can originate from technological contexts. TOE framework is
adopted in this study to apply a unified framework to group-related factors (Tornatzky and
Fleisher, 1990).
Although cloud computing offers many advantages, risks and obstacles inhibit
organizations from adopting cloud systems. According to the cloud IT user survey (Gen,
2009), security is the main concern of organizations with regard to cloud computing. Public
cloud computing poses essential security risks because organizations access control to data
and utilized systems over the Internet (Geczy et al., 2012). Many organizations have concerns
related to hacker attacks and ownership and control of their confidential data (Kenyon, 2012).
The concerns posed by risks can be treated as uncertainty costs that affect the switching
process in IS adoption research (Hong et al., 2008; Kim and Kankanhalli, 2009). Therefore, this
study views perceived risk of cloud systems as switching costs.
However, satisfaction with and breadth of use of incumbent ERP systems are considered
obstacles to adopting cloud systems (Park and Ryoo, 2013; Ye et al., 2008). Both factors can be
viewed as sunk costs, which refer to a previous commitment, that is, economic cost, learning
cost, customer habit, emotional cost, or cognitive effort (Hong et al., 2008; Kim and
Kankanhalli, 2009). Satisfaction is related to emotional cost and customer habit, and breadth
of use is related to learning cost and cognitive effort (Park and Ryoo, 2013; Ye et al., 2008).
Enablers and
inhibitors of
cloud ERP
switching
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2.3 Two-factor theory
Two-factor theory proposed by Herzberg (1959) categorizes the factors that influence
employee job satisfaction into motivators and demotivators (hygiene factors). Motivators are
related to job satisfaction, such as advancement, recognition, responsibility, and
achievement. Employees feel satisfied because of motivators. On the other hand, hygiene
factors are related to job dissatisfaction, such as supervision, pay, company polices, and
working conditions (Robbins and Judge, 2007). The existence of hygiene factors results in
dissatisfaction, while lack of hygiene factors does not lead to job satisfaction. The two factors
are not the opposite of one another. In other words, the two factors could simultaneously
influence employees’ work motivations.
Recently, two-factor theory has been used to investigate the adoption and acceptance of
cloud computing (Hachicha and Mezghani, 2018; Lee et al., 2013; Park and Ryoo, 2013),
knowledge management systems (Kang, 2018), and mobile-based services (Wu et al., 2019;
Liu et al., 2011). Motivators refer to the factors that motivate users to adopt a product and
services, while demotivators refer to the factors that inhibit users from adopting them
(Cenfetelli and Schwarz, 2011). Park and Ryoo (2013) proposed viewing decisions to accept or
reject cloud applications as the evaluation of psychological benefits and costs. Psychological
benefits refer to the perceived utility gained from new IS, while psychological costs refer to
the loss of incumbent IS and the uncertainty of new IS.
Based on the same concept, switching benefits are viewed as motivators since the
presence of switching benefits leads to organizations’ switching intention to cloud ERP
systems. Switching costs are viewed as demotivators since the existence of switching costs
could hinder switching intention, but the absence of switching costs does not necessarily
result in switching intention. Switching benefits and costs are not opposing factors since
organizations can simultaneously hold perceptions of motivators and demotivators.
Therefore, this study uses two-factor theory to investigate the effects of switching benefits
and costs on switching intention to cloud ERP systems.
2.4 Technology-organization-environment framework
Several empirical studies have proposed that individual and organizational benefits are
further influenced by other factors (Seddon and Kiew, 1996; Seddon, 1997). For example,
system quality and data quality factors that influence perceived benefits are considered to be
factors in the success of data warehousing (Wixom and Watson, 2001). According to IS
success model, system quality and information quality also affect organizational benefits
(DeLone and McLean, 2003). Colleague opinion is found to have a positive effect on switching
benefits when investigating user intention to change to new IS (Kim and Kankanhalli, 2009).
Accordingly, factors such as data quality, system quality, information quality, and colleague
opinion are from a variety of contexts and are found to positively affect perceived benefits.
Since perceived benefits are influenced by a variety of factors, the factors can be
categorized as technological, organizational, and environmental (Tornatzky and Fleisher,
1990). TOE framework, including the three contexts, has been used to investigate factors that
drive organizations to adopt and diffuse IS innovation (Gangwar et al., 2015; Gutierrez et al.,
2015; Senyo and Effah, 2016). Thus, this study develops a research model by integrating twofactor theory and TOE framework.
Technological contexts refer to technological characteristics that enhance perceived
benefits of cloud ERP systems. One of the most famous models explaining the technological
factors that influence organizational benefits is DeLone and McLean IS success model, which
posits that system quality and information quality can enhance organizational benefits. The
model has been widely adopted to explain the effects of IS on organizational benefits (Ifinedo
et al., 2010; Lin, 2010; Tsai et al., 2012). Therefore, system quality and information quality are
included in the technological contexts as the predictors of switching benefits.
Organizational contexts refer to the characteristics and resources of the organization. For
SMEs, financial resources are one of the most constrained resources (Kuan and Chau, 2001).
Cloud ERP systems that consume few financial resources have a financial advantage over
more costly ERP systems. Therefore, financial advantage of cloud ERP systems should be a
suitable predictor of switching benefits.
Environmental contexts refer to influences exerted by government and industry (Kuan
and Chau, 2001). Government policies and funding may encourage organizations to adopt IS
innovation. Additionally, organizations may be influenced to adopt IS innovation by their
business partners or competitors. Therefore, government support and industry pressure are
included in the environmental contexts.
Since TOE framework is a theoretical framework to organize the factors that influence
technology adoption and diffusion, the factors affecting cloud computing adoption can be
developed and proposed in technological, organizational, and environmental contexts.
Table II lists the common factors and hypothesized relationship.
3. Research model and hypotheses
3.1 Switching benefits and costs
Based on two-factor theory, we characterize switching benefits and costs as motivators and
demotivators. Kim and Kankanhalli (2009) defined switching benefits as the perceived utility
a user would enjoy in switching from the status quo to the new IS. Switching benefits in this
study refer to the perceived utility an organization would enjoy by switching from incumbent
ERP systems to cloud ERP systems. When switching to cloud ERP systems, organizations
can improve job performance, effectiveness, and efficiency. The potential benefits will
increase organizations’ intention to switch to cloud ERP systems. Previous studies have
revealed that the perceived benefits from using a system positively influence IS adoption and
acceptance (Kuan and Chau, 2001; Liu et al., 2008; Yao et al., 2007). Park and Ryoo (2013) also
found that switching benefits have a positive impact on the intention to switch to cloud
computing. Thus, we expect that higher switching benefits would increase switching
intention to cloud ERP systems.
Kim and Kankanhalli (2009) defined switching costs as the perceived disutility a user
would incur in switching from the status quo to the new IS. Switching costs in this study refer
to the perceived disutility an organization would incur by switching from incumbent ERP
systems to cloud ERP systems. In the process of switching to cloud ERP systems,
organizations might lose their previous investment in incumbent ERP systems and must
invest extra time and costs to implement cloud ERP systems (Kenyon, 2012). Thus, switching
costs might cause organizations to continue to use incumbent IS and refuse to use new IS.
Previous studies have found that switching costs have a negative impact on switching
intention to new IS (Bhattacherjee and Park, 2014; Fan et al., 2015; Lucia-Palacios et al., 2016;
Park and Ryoo, 2013). Thus, we expect that higher switching costs would decrease switching
intention to cloud ERP systems. This study hypothesizes the following:
H1. Switching benefits are positively related to switching intention.
H2. Switching costs are negatively related to switching intention.
3.2 Factors that contribute to switching benefits
In this study, the factors from TOE framework are treated as the external variables that affect
perceived benefits of cloud ERP systems. We hypothesize that technological contexts (system
quality, information quality), organizational contexts (financial advantage), and
environmental contexts (government support, industry pressure) affect switching benefits.
Enablers and
inhibitors of
cloud ERP
switching
605
Bhattacherjee
and Park (2014)
Benefit-related
System
quality
Information
quality
Financial
advantage
Government
support
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606
Table II.
Factors influencing
cloud computing
adoption
Construct
Reference
Cost-related
Industry
pressure
Switching
benefits
Perceived
risk
Satisfaction
with incumbent
systems
−
−
Fan et al. (2015)
þ
þ
−
Hsu and Lin
þ
þ
(2016)
Gen (2008, 2009) þ
þ
−
Gutierrez et al.
þ
þ
(2015)
Lucia-Palacios
þ
−
et al. (2016)
Oliveira et al.
þ
þ
þ
−
(2014)
Note(s): Dependent variable: Cloud computing adoption; þ represents positive relationship; represents negative relationship
Breadth
of use
Switching
costs
−
−
System quality in this study refers to the performance characteristics of cloud ERP systems,
such as adaptability and availability. These items are similar to those that measure the
quality of general systems (DeLone and McLean, 2003). Additionally, the technological
attributes of cloud systems, including easy-/fast-to-deploy and up-to-date functionalities, are
considered in this study (Benlian et al., 2011–2012; Ifinedo et al., 2010). Organizations use ondemand cloud systems without investing in cloud IT infrastructures; thus, they can easily
and rapidly deploy cloud ERP systems. In addition, maintenances and up-grade activities are
managed by cloud service providers. Thus, the abovementioned system quality is perceived
to influence the benefits of using cloud ERP systems. Information quality in this study refers
to the characteristics of the output provided by cloud ERP systems, and measures include
such dimensions as completeness, understandability, relevance, and security (DeLone and
McLean, 2003). With the mobility of cloud computing, organizations can access data on cloud
systems using mobile devices anytime, anywhere. Organizations focus on the accuracy of
information provided by cloud ERP systems because information quality is related to
workforce collaboration, productivity, and efficiency.
According to Kim and Kankanhalli (2009), the construct of switching benefits is adapted
from perceived usefulness. Increases in system quality and information quality drive
increases in usefulness in IS contexts (Seddon and Kiew, 1996). Positive associations have
been applied to ERP success conceptualization (Ifinedo et al., 2010; Lin, 2010; Tsai et al., 2012).
Ifinedo et al. (2010) indicated that the two qualities positively influence individual benefits,
which, in turn, influence overall organizational benefits. Thus, we expect that the system
quality and information quality of cloud ERP systems lead to their perceived utility. This
study hypothesizes the following:
H3. System quality is positively related to switching benefits.
H4. Information quality is positively related to switching benefits.
Financial advantage refers to financial benefits that organizations receive by using cloud ERP
systems, including structured payments, pay for use, and cost savings (Gen, 2009). Cloud ERP
systems offer potential benefits that increase the flexibility of IT investments and decrease costs
in organizations (Geczy et al., 2012). Organizations do not need to invest significant financial
resources in their IT infrastructures because cloud service providers maintain and manage cloud
ERP systems, and this further reduces IT investment costs (Gangwar et al., 2015). The pay-asyou-use mode allows organizations to flexibly purchase their needed cloud systems (Safari et al.,
2015). The financial advantages of adopting cloud systems are the available resources (Wang
et al., 2006) and strengthening of the organizational benefits of switching to cloud ERP systems.
Thus, we expect that financial advantage positively enhances the perception of IS benefits. This
study hypothesizes the following:
H5. Financial advantage is positively related to switching benefits.
Government support is recognized as a critical environmental factor that affects innovation
adoption. Government support in this study refers to the assistance provided by an authority
to encourage the spread of cloud computing in businesses. Governments can provide funding
to encourage organizations to adopt cloud systems. Governments can also help cloud service
providers decrease management costs by improving cloud IT infrastructures or establishing
common data centers and server farms, which further reduce the usage costs of organizations
(Hsu and Lin, 2016). Previous studies have found that government support can encourage
organizations to adopt IS (Hsu and Lin, 2016; Ifinedo, 2011; Lian et al., 2014; Oliveira et al.,
2014). Government policies and support facilitate organizations to increase the benefits of
using cloud ERP systems. Thus, we expect that government support positively enhances the
perception of the cloud ERP utility for organizations.
Enablers and
inhibitors of
cloud ERP
switching
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Industry pressure from business partners and competitors is another important factor in
IS adoption (Kuan and Chou, 2001). Industry pressure in this study refers to the level of cloud
computing capability in the firm’s industry and among its competitors. Trading partners
might request or recommend that organizations use a specific IS to maintain cooperative
relationships. If business competitors use a new IS, the organization will maintain a
competitive advantage in the industry by adopting the new IS (Hsu and Lin, 2016; Maduku
et al., 2016). Organizations under industry pressure hope to increase their competitive
advantage by switching to cloud ERP systems. Industry pressure has been found to
positively influence cloud computing adoption (Hsu et al., 2014; Lian et al., 2014; Low et al.,
2011) and perceived IS performance (Martın et al., 2012). Thus, we expect that the greater the
industry pressure to adopt cloud ERP systems is, the greater the benefits from cloud ERP
systems will be. This study hypothesizes the following:
H6. Government support is positively related to switching benefits.
H7. Industry pressure is positively related to switching benefits.
3.3 Factors that contribute to switching costs
This study suggests perceived risk of cloud ERP systems and satisfaction with and breadth
of use of incumbent ERP systems as the antecedents of switching costs. The former factor is
associated with uncertainty costs (Kim and Kankanhalli, 2009), whereas the latter two factors
are associated with sunk costs (Park and Ryoo, 2013; Ye et al., 2008). Thus, we hypothesize
that perceived risk, satisfaction, and breadth of use affect switching costs.
Perceived risk in this study is related to the disclosure of organizational information
submitted by cloud ERP adopters. Potential risk occurs on the Internet when information is
revealed (Dinev et al., 2006). Because cloud computing is an Internet-based connection,
organizations need to access data and cloud systems over the Internet. When cloud service
providers manage the control and ownership of organizational data, organizations may
worry that their data might be disclosed to certain government agencies and courts (Geczy
et al., 2012). Thus, data security and risks may be viewed as potential concerns for
organizations. Previous studies have found that perceived risk has a negative impact on
cloud computing adoption (Martins et al., 2019; Sabi et al., 2016). We expect that the risks of
using cloud ERP systems create implicit uncertainty and increase the perception of
switching costs.
Satisfaction determines whether users continue to use incumbent IS or switch to new IS.
Based on ECT (Bhattacherjee, 2001), users form an initial expectation of a specific IS and form
perceptions of its performance after using the IS. User satisfaction is determined by whether
the original expectations of the IS are confirmed by perceived performance. Finally, satisfied
users continue to use incumbent IS, and dissatisfied users consider switching to a
replacement IS.
Satisfaction with incumbent IS plays a significant role in switching behavior (Park and
Ryoo, 2013; Ye et al., 2008). Satisfaction in this study refers to users’ feelings about incumbent
ERP system use. Low satisfaction with incumbent ERP systems may drive organizations to
find new ways to improve job performance. However, if organizations feel satisfied with
incumbent ERP systems, they will continue to use the incumbent systems and will be
unwilling to switch to cloud ERP systems. Scholars have empirically verified that satisfaction
with incumbent IS is negatively related to new IS adoption (Chang et al., 2019; Fan et al., 2015;
Hsu and Lin, 2016). Because user satisfaction with incumbent ERP systems might cause
psychological losses, organizations note perceived costs for switching behaviors.
Breadth of use in this study is defined as the degree to which an organization uses the
features and options offered by incumbent ERP systems. Scholars have argued that the effect
of breadth of use can be explained by the concept of sunk costs, that is, time and effort spent in
learning the feature applications (Park and Ryoo, 2013; Ye et al., 2008). When switching to
cloud ERP systems, organizations must make an effort to learn how to use new systems.
Because employees in organizations have invested time and effort in incumbent ERP
systems, they fear losing sunk costs and therefore resist the change to new ERP systems.
Previous studies have found that sunk costs have a positive impact on switching costs
(Burnham et al., 2003; Kim and Kankanhalli, 2009; Park and Ryoo, 2013). This study
hypothesizes the following:
Enablers and
inhibitors of
cloud ERP
switching
609
H8. Perceived risk is positively related to switching costs.
H9. Satisfaction is positively related to switching costs.
H10. Breadth of use is positively related to switching costs.
The research model is shown in Figure 1.
4. Methodology
4.1 Data collection
This study worked with Chunghwa Telecom (CHT) to investigate organizations’ switching
intention to cloud ERP systems because CHT is Taiwan’s largest telecom company and has a
higher market share in cloud computing. The cloud ERP systems are run on the server and
database provided by CHT. The cloud ERP systems include modules for sales and distribution,
material management, product planning, and financial accounting; the systems allow
organizations to pay for module use and are suitable for various industries, that is, trade,
retail trade, wholesale, and manufacture. CHT also emphasized that its cloud ERP systems save
more the costs of IT infrastructures, implementation, and maintenance than on-premise ERP
systems, and provided software upgrades for the cloud ERP systems at any time.
The targeted population was top managers and owners of enterprises because they held
the authority to decide whether to switch to new ERP systems. In reaching out to the targeted
respondents, CHT contacted Taiwan’s top managers and owners of enterprises by phone and
Enablers
Technological Context
System Quality
Information Quality
Organizational Context
Financial Advantage
H3
H4
H5
Switching Benefits
H6
H1
Environmental Context
Government Support
H7
Switching Intention
Industry Pressure
Perceive Risk
Satisfaction
Breadth of Use
H8
H9
H10
Inhibiters
H2
Switching Costs
Figure 1.
Research model
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email and invited them to participate in its marketing activities. The goal of these eight
marketing activities was to promote and introduce the cloud ERP systems and solutions for
enterprise customers, and even encourage these customers to purchase SaaS ERP licenses.
After participating in marketing activities, the participants learned about the advantages and
disadvantages of adopting cloud ERP systems and received enough knowledge to determine
whether to switch from incumbent ERP systems to cloud ERP systems. In these marketing
activities, we also explained the purpose of the study and ensured that the respondents’
questionnaires would be kept confidential. Finally, this study administered questionnaires to
participants and promised to provide a summary of the survey results in exchange for
participation.
This study used a convenience sampling approach to collect data. In order to reduce sample
error, we ensured that the suitable samples should be top managers and owners of enterprises,
and distributed approximately 500 questionnaires externally to respondents who participated in
marketing activities. The data were collected over the period of July to August 2014. A total of
212 questionnaires were returned, with a response rate of 42.4 percent, which was higher than the
average response rate of 10–15 percent of external surveys (Lindemann, 2018). Most respondents
were male (64.2 percent), in the 36–40 age group (36.3 percent), with a university degree (62.7
percent), and worked in a strategy department (26.9 percent). Most organizations were
distributed in the industries of electronics and electrical equipment (39.2 percent). Employees in
the sample primarily ranged from 1 to 10 (34 percent), and the capital of the organizations was
less than NT$ 1 million (39.6 percent). Table AI lists the respondents’ demographics.
4.2 Instrument development
In this study, all the survey items for 11 constructs in the questionnaire were taken from a
prior literature review and modified to fit the context of cloud computing. Items for system
quality and information quality were adapted from DeLone and McLean (2003) and Benlian
et al. (2011–2012). Items for financial advantage were adapted from Yao et al. (2007) and Ma
et al. (2005). Items for government support were adapted from Tan and Teo (2000). Items for
industry pressure were adapted from Kuan and Chau (2001). Items for perceived risk were
adapted from Bhattacherjee (2001). Items for satisfaction were adapted from Dinev and Hart
(2006). Items for breadth of use were adapted from Ye et al. (2008). Items for switching benefits
and switching costs were adapted from Kim and Kankanhalli (2009). Items for switching
intention were adapted from Venkatesh et al. (2003). The 36 items were developed for the
questionnaire (see Table AII) and measured by a seven-point Likert scale ranging from
“strongly disagree” (1) to “strongly agree” (7).
4.3 Data analysis
A partial least squares structural equation modeling (PLS-SEM) approach is suggested for
conducting data analyses when the structural model with many constructs or indicators is
complex, the sample size is small, or the data are nonnormally distributed (Hair et al., 2017,
2019). Since the research model containing 11 constructs is complex, this study used partial
least squares (PLS) analysis to test measurement and structural models. The assessment of
the measurement model includes item reliability, convergent validity, and discriminant
validity, while the assessment of the structural model includes path coefficients and the
statistical significance of hypothesized relationships.
5. Results
5.1 Common method bias
To assess common method bias, Harman’s one-factor test is examined using a principal
component analysis. If a single construct accounts for more than 50 percent of the variance,
common method bias might threaten the validity (Harman, 1976). The results show that the
combined 11 constructs account for 90.41 percent of the total variance. The variance of the 11
constructs ranges from 1.78 percent to 38.85 percent, which is less than 50 percent of the
variance. Therefore, common method bias might be excluded by the items of this study.
5.2 Measurement model
The measurement model is evaluated for convergent validity and discriminant validity,
which are analyzed using confirmatory factor analysis (CFA). Convergent validity is
assessed using factor loading, composite reliability (CR), Cronbach’s alpha, and average
variance extracted (AVE) (Gefen et al., 2000). As shown in Table III, factor loading for all the
items ranges from 0.77 to 0.98, which exceeds the 0.7 recommended level. CR for each
construct ranges from 0.89 to 0.98, which exceeds the 0.7 recommended level. Cronbach’s
alpha for all the constructs ranges from 0.82 to 0.98, which exceeds the 0.5 recommend level.
The AVE for each construct ranges from 0.74 to 0.97, which exceeds the 0.5 recommended
level. Table IV shows that the square root of the AVE for each construct exceeds the
correlations between the construct and other constructs. Thus, convergent validity and
discriminant validity are supported.
5.3 Structural model
This study examines the structural model by testing the hypothesized relationships among
all the constructs. As shown in Figure 2, switching benefits (β 5 0.562, p < 0.001) have a
significantly positive effect on switching intention, and switching costs (β 5 0.127, p < 0.05)
have a significantly negative effect on switching intention, providing support for H1 and H2.
The proposed model explains 32.7 percent of the variance in switching intention.
System quality (β 5 0.209, p < 0.05), financial advantage (β 5 0.560, p < 0.001), and
industry pressure (β 5 0.182, p < 0.001) have significantly positive effects on switching
benefits, thus supporting H3, H5, and H7. Contrary to our expectation, information quality
(β 5 0.035, p > 0.05) and government support (β 5 0.075, p > 0.05) have no direct effect on
switching benefits; thus, H4 and H6 are not supported. These paths account for 67.8 percent of
the variance in switching benefits.
Perceived risk (β 5 0.177, p < 0.05), satisfaction (β 5 0.382, p < 0.001), and breadth of use
(β 5 0.351, p < 0.001) have significantly positive effects on switching costs, thus supporting
H8, H9, and H10. The three paths account for 42.7 percent of the variance in switching costs.
The results of this study are summarized in Table V.
6. Discussion
This study investigates the enablers and inhibitors that influence organizations’ switching
intention from incumbent ERP systems to cloud ERP systems. Table VI summarizes
previous studies in the contexts of cloud computing. As can be seen from the table, most
previous studies have used TOE framework as the main theoretical foundation, because the
framework can take into account technological, organizational, and environmental
perspectives (Hsu and Lin, 2016; Safari et al., 2015). TOE framework can further be
incorporated with other theories, such as TAM, theory of planned behavior, DOI, or ECT
(Gangwar et al., 2015; Lucia-Palacios et al., 2016; Martins et al., 2019; Oliveira et al., 2014).
Different from previous studies, this study is grounded in a set of theoretical foundations,
including two-factor theory, TOE framework, IS success model, and ECT. The two-factor
theory is used to characterize switching benefits and costs, and TOE is used to organize
factors that affect benefits. Information quality and systems quality come from IS success
model, while satisfaction with incumbent ERP systems is derived from ECT. In addition, the
Enablers and
inhibitors of
cloud ERP
switching
611
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Construct
Item
Factor loading
Mean
S.D.
CR
Cronbach’s alpha
AVE
System quality
SQ1
SQ2
SQ3
SQ4
IQ1
IQ2
IQ3
IQ4
FA1
FA2
FA3
GS1
GS2
GS3
ID1
ID2
ID3
PR1
PR2
PR3
US1
US2
US3
BOU1
BOU2
SB1
SB2
SB3
SB4
SC1
SC2
SC3
SI1
SI2
SI3
0.91
0.94
0.92
0.88
0.93
0.95
0.95
0.94
0.94
0.96
0.93
0.96
0.96
0.96
0.94
0.96
0.95
0.96
0.98
0.98
0.97
0.98
0.96
0.98
0.98
0.93
0.96
0.94
0.95
0.90
0.90
0.77
0.93
0.98
0.97
5.42
0.96
0.95
0.94
0.84
5.28
0.98
0.97
0.96
0.89
5.63
1.02
0.96
0.94
0.89
5.04
1.36
0.97
0.96
0.92
4.86
1.21
0.97
0.95
0.91
3.72
1.40
0.98
0.98
0.95
3.11
1.18
0.98
0.97
0.94
3.28
1.48
0.98
0.97
0.97
5.38
1.05
0.97
0.96
0.89
3.30
1.29
0.89
0.82
0.74
4.97
1.18
0.97
0.96
0.92
Information quality
Financial advantage
Government support
Industry pressure
Perceived risk
Satisfaction
Breadth of use
Switching benefits
Switching costs
Switching intention
Table III.
Reliability
SQ
Table IV.
Inter-construct
correlations
IQ
FA
GS
IP
PR
SQ
0.92
IQ
0.78
0.94
FA
0.60
0.52
0.94
GS
0.57
0.47
0.44
0.96
IP
0.48
0.43
0.38
0.45
0.95
PR
0.22
0.15
0.16
0.19
0.16
0.98
SA
0.29
0.29
0.13
0.24
0.31
0.07
BOU 0.20
0.20
0.08
0.27
0.27
0.12
SB
0.65
0.53
0.77
0.50
0.51
0.18
SC
0.07
0.06
0.05
0.17
0.14
0.11
SI
0.50
0.48
0.41
0.35
0.52
0.05
Note(s): The italic values are the squared root of AVE
SA
BOU
SB
SC
SI
0.97
0.56
0.15
0.57
0.29
0.98
0.07
0.54
0.18
0.95
0.04
0.56
0.86
0.10
0.96
Enablers and
inhibitors of
cloud ERP
switching
Technological Context
System Quality
Information Quality
Organizational Context
Financial Advantage
0.209*
– 0.035
0.560***
0.075
R 2 = 67.8%
Environmental Context
Government Support
613
Switching Benefits
0.562***
0.182***
Switching Intention
Industry Pressure
R 2 = 32.7%
Perceive Risk
Satisfaction
Breadth of Use
Hypothesis
– 0.127*
0.177*
0.382***
0.351***
Path
H1
SB – SI
H2
SC – SI
H3
SQ – SB
H4
IQ – SB
H5
FA – SB
H6
GS – SB
H7
IP – SB
H8
PR – SC
H9
SA – SC
H10
BOU – SC
Note(s): *p < 0.05, **p < 0.01, ***p < 0.001
Switching Costs
R 2 = 42.7%
Figure 2.
Results
*p < 0.05, **p < 0.01, ***p < 0.001
Path coefficient
0.562***
0.127*
0.209*
0.035
0.560***
0.075
0.182***
0.177*
0.382***
0.351***
Assessment (p<0.05)
Supported
Supported
Supported
n.s.
Supported
n.s.
Supported
Supported
Supported
Supported
dependent variable of previous research mainly focused on cloud computing adoption rather
than switching intention, so few studies considered switching decisions from incumbent
systems to cloud systems. However, top managers and owners of the enterprises must make
decisions between incumbent and cloud ERP systems based on enablers and inhibitors, such
as benefits of cloud ERP systems, sunk costs of incumbent ERP systems, and uncertainty
costs of cloud ERP systems. The critical factors have been addressed in our developed
research model grounded in multiple theories.
In technological contexts, system quality is an influential factor in switching benefits. This
finding is consistent with that of other studies (Ifinedo et al., 2010; Lin, 2010; Tsai et al., 2012),
which posited that system quality has a positive impact on individual and organizational
benefits. Therefore, good system quality of cloud ERP systems increases the organization’s
expected benefits of switching to new systems. Although previous research has shown that
information quality is another factor in organizational benefits (Ifinedo et al., 2010; Lin, 2010; Tsai
et al., 2012), the results show that information quality is not found to significantly affect switching
benefits. One possible explanation is that ERP systems are known for collecting and
Table V.
Path coefficients and
significance
Cost-benefit analysis and TAM
TOE framework
TAM and TOE framework
TAM, Theory of planned behavior, and ECT
DOI and TOE framework
Chang et al. (2019)
Hsu and Lin (2016)
Gangwar et al. (2015)
Lucia-Palacios et al.
(2016)
Martins et al. (2019)
Table VI.
A comparison of the
current research with
previous studies
Theory
(1) Perceived
usefulness
(2) Perceived ease of
use
(1) Relative advantage
(2) Ease of use
(3) Compatibility
(4) Security
(5) Government
Support
(6) Competition
intensity
(7) Regulatory
environment
(1) Relative advantage
(2) Compatibility
(3) Perceived
usefulness
(4) Perceived ease of
use
(1) Perceived
usefulness
(2) Perceived ease of
use
(3) Subjective norms
(1) Relative advantage
(2) Compatibility
(3) Cost saving
(4) Coercive pressure
(5) Normative pressure
Enabler
(1) Complexity
Security concerns
(1) Switching costs
(2) Satisfaction with prior IT
(1) Complexity
(1) Financial costs
(2) Satisfaction with existing
systems
(1) Perceived risk
(2) Privacy concerns
Inhibitor
SaaS adoption
(continued )
Cloud service adoption
Cloud computing adoption
Cloud service adoption
Switching to cloud computing
Dependent variable
614
Previous studies
JEIM
33,3
Theory
TOE framework
DOI and TOE framework
TOE framework, two-factor theory, IS success
model, and ECT
Previous studies
Safari et al. (2015)
Oliveira et al. (2014)
This study
(1) Relative advantage
(2) Compatibility
(3) Security and
privacy
(4) Competitive
pressure
(5) Social influence
(1) Relative advantage
(2) Compatibility
(3) Cost saving
(4) Competitive
pressure
(5) Regulatory support
(1) Switching benefits
(2) System quality
(3) Information quality
(4) Financial advantage
(5) Government
support
(6) Industry pressure
Enabler
(1) Switching costs
(2) Perceived risk of cloud
systems
(3) Satisfaction with incumbent
systems
(4) Breadth of use of incumbent
systems
(1) Security concerns
(2) Complexity
(1) Complexity
Inhibitor
Switching intention to cloud
computing
Cloud computing adoption
SaaS adoption
Dependent variable
Enablers and
inhibitors of
cloud ERP
switching
615
Table VI.
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616
disseminating integrated data in real time (Wylie, 1990). Both on-premise and on-demand ERP
systems provide high-quality information in this respect. Thus, organizations do not view highquality information as a strong factor that increases switching benefits of cloud ERP systems.
In organizational contexts, the results confirm that financial advantage of using cloud
ERP systems is an important determinant of switching benefits. The significance of financial
advantage is in line with previous research (Gangwar et al., 2015; Safari et al., 2015), which
pointed out that the advantages associated with cloud computing are based on pay-per-use
models. The payment models bring cost savings and flexibility of financial resources, which,
in turn, increase switching benefits.
In environmental contexts, the factor of government support has a high mean score of
4.95, which implies that organizations have perceived government support. Previous studies
proposed that organizations with government support are more likely to increase the benefits
of adopting cloud computing (Hsu and Lin, 2016; Ifinedo, 2011; Lian et al., 2014; Oliveira et al.,
2014). However, contrary to our prediction, the results show that government support is not
found to significantly affect switching benefits. One possible reason for this result is that the
Taiwan government tends to support cloud technology by promoting and building
government clouds and improving the infrastructures for networking. Although the Taiwan
government plans to invest 24 billion in cloud computing over five years (National
Development Council, 2012), these measures might provide long-term benefits rather than
immediately tangible benefits to organizations. As a result, although the score for
government support is high, it does not significantly affect switching benefits of cloud
ERP systems. On the other hand, this study indicates that industry pressure is a significant
determinant of switching benefits. This result is consistent with previous studies (Hsu et al.,
2014; Lian et al., 2014; Low et al., 2011), which supported the effect of industry pressure on
cloud computing adoption. The finding indicates that organizations are forced to adopt cloud
ERP systems by trading partners and business competitors in the competitive environment.
The results show that perceived risk, satisfaction, and breadth of use are important
determinants of switching costs. Prior studies pointed out that sunk costs of giving up
incumbent IS increase switching costs (Bhattacherjee and Park, 2014; Fan et al., 2015; LuciaPalacios et al., 2016), while security risks of using new IS enhance switching costs (Martins
et al., 2019; Sabi et al., 2016). According to our findings, satisfaction with and breadth of use of
incumbent ERP systems cause organizations to lose previous efforts and costs, which further
reduce their switching intention to cloud ERP systems. Cloud ERP systems bring security
risks and uncertainties, which further lead organizations to retain incumbent ERP systems
and hinder their switching intention to new systems.
7. Implications for theory and practice
7.1 Implications for theory
Several studies have investigated cloud computing adoption (Behrend et al., 2011; Lee et al.,
2013; Low et al., 2011; Paquette et al., 2010), but few studies have addressed switching issues
to cloud computing (Fan et al., 2015; Lucia-Palacios et al., 2016). Specifically, no previous work
has empirically examined switching intention as a dependent variable and focused on the
enablers and inhibitors that influence switching intention at the organizational level. Given
the nature of the dependent variable (i.e. switching intention), this study aims to determine the
factors that influence organizations’ switching intention to cloud ERP systems and integrates
multiple theories to address switching issues.
Since two-factor theory is applicable to simultaneously evaluating users’ perceptions of
motivators and demotivators, this study introduces this theory as the main theoretical
foundation for investigating switching benefits and costs. Because organizations can make
comprehensive decisions about IS innovation by considering technological, organizational,
and environmental factors, we use a set of TOE factors to extend two-factor theory as the
factors influencing organizations’ switching benefits. The results show that system quality,
financial advantage, and industry pressure are important determinants of switching benefits
in organizations. Thus, TOE framework can be broken down and investigated to determine
what important factors are with regard to switching benefits. To the best of our knowledge,
this study develops an integrated model based on two-factor theory and TOE framework, and
empirically tests the proposed hypotheses. Future research should validate the model and
findings of this study when investigating IS adoption in organizations.
Perceived risk is known to affect the usage intentions of e-commerce and cloud systems
(Dinev et al., 2006; Geczy et al., 2012; Kenyon, 2012; Martins et al., 2019; Sabi et al., 2016). This
study confirms that perceived risk plays an important role in the model but also shows that it
has a direct effect on switching costs and an indirect effect on switching intention. Future
studies should investigate the effect of perceived risk on costs in other Internet applications.
Satisfaction with and breadth of use of incumbent ERP systems, which contribute to
switching costs, have been investigated in personal applications (Park and Ryoo, 2013; Ye et al.,
2008). However, the subjects in these studies were individuals. The current study confirms that
the same factors can also be applied to enterprise systems. Future research will further
investigate the effects of satisfaction with and breadth of use of incumbent enterprise systems.
7.2 Implications for practice
Our findings show that switching benefits are an important factor for switching intention to
cloud ERP systems. Therefore, cloud service providers should promote the advantages of cloud
ERP systems, that is, improvement in organizational effectiveness, efficiency, productivity, and
product/service quality. Cloud service providers can further attempt to increase switching
benefits by emphasizing system quality, financial advantage, and industry pressure.
The benefits of using cloud ERP systems depend on system quality. This study suggests
that cloud service providers reduce system response time and enhance adaptability,
availability, and reliability. To further improve the system quality of cloud ERP systems,
easy/fast deployments and up-to-date functionalities of systems should be considered. Better
system quality for cloud ERP systems leads to greater organizational performance; thus,
cloud service providers should design systems to be more effective and more relevant to
organizational processes and tasks.
Financial advantage acts as a strong switching incentive. Cloud ERP systems enable
organizations to reduce the costs of IT personnel, hardware and software infrastructures, and
maintenance. Organizations also value the flexibility and convenience of pay-per-use models.
This result suggests that cloud service providers should emphasize the financial flexibility
and cost savings to potential enterprise customers.
Cloud computing adoption may change the rules of competition and provide organizations
with new ways to operate and outperform their business competitors. Pressure from trading
partners may force organizations to maintain partnerships with these partners. Thus, cloud
service providers should make organizations aware of the importance of cloud ERP systems
in maintaining competitive advantages in the industry. Additionally, cloud service providers
can first try to attract more innovative organizations, and these organizations will pressure
their trading partners and business competitors to adopt cloud ERP systems.
However, switching costs represent the major barrier for switching intention to cloud ERP
systems. Cloud service providers can attempt to reduce switching costs by reducing the
perception of risks and sunk costs, which include satisfaction with and breadth of use of
incumbent ERP systems. In these circumstances, cloud service providers should focus on
reducing sunk costs, learning costs, and security risks of using cloud ERP systems.
Cloud service providers should acknowledge that risk presents a tremendous barrier to
switching to cloud ERP systems. It is clear from our sample that organizations are concerned
Enablers and
inhibitors of
cloud ERP
switching
617
JEIM
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that their information might be disclosed to others. Cloud service providers should make
detailed efforts to explain their protection policies and convince customers of their data
security, such as server authentication, database authorization, and data encryption. Cloud
service providers should also invest resources to build better security protocols and
mechanisms (e.g. secure socket layer (SSL) technology) to ensure security and privacy. The
highest possible safety standards and government regulations should be observed to
alleviate customers’ security concerns.
Although satisfaction with and breadth of use of incumbent ERP systems have a larger
effect on switching costs, it is difficult to directly reduce such costs for cloud service
providers. Because cloud service providers cannot directly change organizational
satisfaction with incumbent ERP systems, the only action that they can take is to enhance
customer satisfaction with their cloud systems. Cloud service providers should strive to
improve their products to ensure that organizations are satisfied.
A greater breadth of use increases the barrier to organizational switching and makes it
more difficult for cloud ERP systems to lure away existing organizations. For organizations
that use the features and options of incumbent ERP systems, this result suggests that cloud
service providers should allow organization a trial use, which, in turn, could change
organizational uses, experiences, and habits. Cloud service providers can also provide
effective training courses and customize cloud ERP systems based on organizations’ needs so
that cloud ERP systems are easy to use and effectively implemented in organizations.
8. Limitations and future research
Our research model is grounded in two-factor theory, which characterizes switching benefits
and costs as motivators and demotivators. This theory can model switching benefits and
costs of system usage and open up many research opportunities. First, the model can be
further extended to investigate factors that enhance switching benefits. The factors are
organized based on TOE framework in this study, but other theoretical perspectives, such as
TAM, DOI, and TRA, can also be incorporated in the future. Second, we found direct
associations between the factors of perceived risk, satisfaction, and breadth of use and
switching costs. Perceived risk was originally studied in e-commerce contexts. This study
shows that the factor also increases switching costs in public clouds. Further studies can be
devoted to investigating the role of risks in private clouds, which are accessed through
Internet owned by organizations. The effects of satisfaction with and breadth of use were
originally studied in personal applications. This study shows that the two factors also
increase switching costs of public clouds in enterprise applications. Further studies can
investigate whether the same factors increase the costs of adopting private clouds.
This study contains certain limitations that require further examination and additional
research. First, a bias related to self-reported scales might exist in this study. Second, the
results based on 2014 data may be constrained by the age of the data. Caution must to be
taken when generalizing the results to today’s research. Third, the empirical data for our
study were collected from organizations in Taiwan, and thus, the generalizability of the
results may be limited. The results may differ due to different countries or regions. Fourth, the
cloud systems being investigated in the study were from a single software supplier. The
findings may vary due to different suppliers. Fifth, because the data were cross-sectional and
not longitudinal, the posited causal relationships can only be inferred rather than proven.
Finally, although it has been demonstrated that behavioral intention leads to actual use
behavior, this study stopped at switching intention.
9. Conclusions
This study aims to investigate the enablers and inhibitors that influence organizations’
switching intention from incumbent ERP systems to cloud ERP systems. From an academic
perspective, we provide the empirical evidence regarding switching to public cloud
computing. This study develops a theoretical model by integrating two-factor theory, TOE
framework, IS success model, and ECT. Two-factor theory is used to characterize switching
benefits and costs that affect switching intention as motivators and demotivators. The
factors that influence switching benefits are organized based on TOE framework, and the
factors that influence switching costs are the uncertainties inherent in cloud systems and the
benefits of the deployed on-premise ERP systems. From a practical perspective, our findings
suggest that cloud service providers can enhance switching benefits by enhancing
technological (system quality) and organizational (financial advantage) factors. The
presentation of environmental factors (industry pressure) is also helpful. Cloud service
providers should also try to reduce the perceived risk of using cloud ERP systems because
perceived risk is one of the factors that contributes to switching costs. Satisfaction with
and breadth of use of incumbent ERP systems are also major factors that increase switching
costs. Cloud service providers can target organizations that are not satisfied with
their incumbent ERP systems or deploy a limited number of modules of incumbent ERP
systems.
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Appendix
Measure
Items
Gender
Male
Female
18–25 years
26–35 years
36–45 years
46–55 years
56–65 years
Over 65 years
Senior high school
University
Master’s
Doctor
Administration
Financial accounting
Information technology
Human resources
Manufacturing
Purchase
Research and development
Sales and distribution
Strategy
Others
Education services
Electronic and electrical
equipment
Retail
Household goods and home
construction
Health care
Technology hardware and
equipment
Telecommunications
Food and beverage
Financial and insurance
Oil and gas
Travel and leisure
Media
Software and computer
services
Others
Age
Education
Department
Industry
Frequency
Percent (%)
136
76
9
54
77
1
50
18
21
133
54
4
16
22
48
6
5
9
6
38
57
5
8
83
64.2
35.8
4.2
25.5
36.3
0.5
23.6
8.5
9.9
62.7
25.5
1.9
7.5
10.4
22.6
2.8
2.4
4.2
2.8
17.9
26.9
2.4
3.8
39.2
50
13
23.6
6.1
15
12
7.1
5.7
7
5
3
4
2
2
1
3.3
2.4
1.4
1.9
0.9
0.9
0.5
7
3.3
(continued )
Enablers and
inhibitors of
cloud ERP
switching
623
Table AI.
Profile of respondents
JEIM
33,3
Measure
Items
Incumbent ERP brand
AMIGO
CHING HANG
China study
Data systems consulting
DATAWIN
Ling Yuen
Microsoft
Oracle
SAP
Developed by enterprises
1–10
11–50
51–100
101–150
151–200
201–500
501–1,000
More than 1,000
Less than 10
10–25
25–30
30–35
40–45
45–50
50–55
55–80
More than 80
Missing (unknown)
624
Number of employees
Capital (NT$ million)
Table AI.
Frequency
Percent (%)
4
18
4
58
4
11
15
18
4
76
72
66
27
6
9
18
5
9
84
33
11
4
2
5
3
6
60
4
1.9
8.5
1.9
27.4
1.9
5.2
7.1
8.5
1.9
35.8
34.0
31.1
12.7
2.8
4.2
8.5
2.4
4.2
39.6
15.6
5.2
1.9
0.9
2.4
1.4
2.8
28.3
1.9
Construct
Measurement items
Sources
System quality
(1) The cloud ERP system is adaptable
(2) The cloud ERP system is reliable
(3) The cloud ERP system is fast to deploy
(4) The cloud ERP system provides the most up-to-date
functionalities
(1) Information on the cloud ERP system is complete
(2) Information on the cloud ERP system is understandable
(3) Information on the cloud ERP system is relevant
(4) Information on the cloud ERP system is secured
(1) Using the cloud ERP system can reduce the cost of hiring
information technology personnel, acquiring hardware and
software infrastructures, and system maintenance
(2) Using the cloud ERP system allows customers to pay for use
(e.g. user account, used data storage space, amount of processed
data, or processing time)
(3) Using the cloud ERP system allows customers to utilize
various structured payment (e.g. monthly, quarterly, or
semiannually)
(1) The government endorses cloud technology
(2) The government is active in setting up facilities related to
cloud technology
(3) The government promotes the use of cloud technology
(1) Majority of business partners think that my company
should use cloud technology
(2) Majority of business partners request my company to use
cloud technology
(3) Majority of competitors using or soon to be using cloud
technology
What do you believe is the risk for the cloud ERP adopters due
to the possibility that
(1) Information submitted could be misused?
(2) Information could be made available to unknown
individuals or companies without your knowledge?
(3) Information could be made available to government
agencies?
How do you feel about your overall experience of traditional
ERP system use:
(1) Very displeased/very pleased
(2) Absolutely terrible/absolutely delighted
(3) Very dissatisfied/very satisfied
(1) We take/took advantage of additional features offered by
traditional ERP system
(2) We have/had used a variety of traditional ERP system’s
features
DeLone and McLean
(2003)
Benlian et al. (2011–
2012)
Information
quality
Financial
advantage
Government
support
Industry
pressure
Perceived risk
Satisfaction
Breadth of use
DeLone and McLean
(2003)
Enablers and
inhibitors of
cloud ERP
switching
625
Ma et al. (2005), Yao
et al. (2007)
Tan and Teo (2000)
Kuan and Chau (2001)
Dinev and Hart (2006)
Bhattacherjee et al.
(2001)
Ye et al. (2008)
(continued )
Table AII.
Measurement items of
constructs
JEIM
33,3
Construct
Measurement items
Sources
Switching
benefits
(1) Changing to the new way of working with the cloud ERP
system would enhance my company’s effectiveness than
working in the current way
(2) Changing to the new way of working with the cloud ERP
system would help accomplish relevant tasks more quickly
than working in the current way
(3) Changing to the new way of working with the cloud ERP
system would increase productivity than working in the
current way
(4) Changing to the new way of working with the cloud ERP
system would improve product/service quality than working in
the current way
(1) We have already put a lot of time and effort into mastering
the current way of working
(2) It would take a lot of time and effort to switch to the new way
of working with the cloud ERP system
(3) Switching to the new way of working with the cloud ERP
system could result in unexpected hassles
(1) My company intends to use the cloud ERP system in the
future
(2) I predict my company would use the cloud ERP system in
the future
(3) My company plans to use the cloud ERP system in the future
Kim and Kankanhalli
(2009)
626
Switching costs
Switching
intention
Table AII.
Kim and Kankanhalli
(2009)
Venkatesh et al. (2003)
Corresponding author
Yu-Wei Chang can be contacted at: nickychang@nutc.edu.tw
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