sustainability
Article
Factors Affecting Intention to Adopt Cloud-BasedSample Page
ERP from a Comprehensive Approach
Byungchan Ahn and Hyunchul Ahn *
Graduate School of Business IT, Kookmin University, 77 Jeongneung-ro, Seongbuk-gu, Seoul 02707, Korea;
[email protected]
* Correspondence: [email protected]; Tel.: +822-910-4577
Received: 14 July 2020; Accepted: 3 August 2020; Published: 10 August 2020






Abstract: To enhance the sustainability of business operations, enterprises have interests in enterprise
resource planning (ERP) transitions from an existing on-premise method to a cloud-based system.
This study conducts a comprehensive analysis using the technology-organization-environment, diffusion
of innovation, and the model of innovation resistance frameworks. The empirical analysis shows that
the factors of organizational culture, regulatory environment, relative advantage, trialability, and vendor
lock-in all had a significant influence on the intention to adopt cloud-based ERP, while information and
communications technology skill, complexity, observability, data security, and customization had no
significant influence on the intention to adopt cloud-based ERP. This study’s findings provide meaningful
guidance for companies that want to adopt cloud-based ERP, governments that support enterprise
digitalization, and vendors who sell cloud-based ERP systems.
Keywords: cloud ERP; technology-organization-environment; innovation; resistance; adoption
1. Introduction
Most large enterprises stopped introducing new enterprise resource planning (ERP) systems in
around 2010. Usually, companies decide every 10 years whether or not to rebuild their ERP. Considering
this cycle, the market demand for advanced ERP is expected to erupt, either later this year or early next
year. The most important factor to consider in advanced ERP is the response to the advent of cloud
technology. Existing ERP established itself as a system that integrates and manages a company’s overall
business processes. However, recently, since it is combined with the latest technologies such as machine
learning, analysis, and the cloud, advanced ERP is garnering attention as a core system that can help
executives make more informed and rapid decisions. In addition, Systems, Applications & Products in
Data Processing (SAP), a major German ERP package provider, said it would discontinue technical
support for existing ECC 6.0 products in 2025 and is strongly driving the transition to the S/4 HANA
cloud. Oracle and Microsoft are also insisting on a cloud-first policy. So, existing on-premise ERP
users are being forced to switch to cloud-based ERP. However, no matter how good a cloud-based ERP
system is, if an organization’s decision-makers and stakeholders do not intend to adopt it, then doing
so would not contribute to improving the organization’s productivity or maintaining its sustainability.
According to Gartner, the ERP market has been undergoing a generational technology shift, due
to the advent of cloud computing technology [1]. Due to the benefits of moving away from on-premise
ERPs, especially in managing upgrades and maintenance processes, cloud-based ERP emerged in the
mid-2000s [2]. While most cloud-based ERPs are provided to customers as software as a service (SaaS),
a number of ERP platforms as a service (PaaS) also exist [3,4]. Gartner predicts that almost 32% of
large enterprises with ERP systems up for replacement might replace their on-premise ERP with the
SaaS service model by 2021 [1]. It is clear that the cloud-based ERP market is growing.
Sustainability 2020, 12, 6426; doi:10.3390/su12166426 www.mdpi.com/journal/sustainability
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In particular, as coronavirus disease 2019 (COVID-19) spread in early 2020, the sustainability of
corporate information systems of companies became a very important topic of corporate management.
Interlocked with the topic of sustainability, cloud-based ERP is becoming increasingly important.
Companies that have adopted cloud-based ERP are much better at working from home, and therefore
ahead in maintaining continuity during the COVID-19 pandemic crisis. Therefore, interest in the
adoption or acceptance of cloud-based ERP has become important at this time.
As a result, research on cloud-based ERP has been rapidly increasing over recent years. In particular,
studies on affecting the adoption of cloud-based ERP have been recently released, and many such studies
are based on the technology-organization-environment (TOE) framework, Ddiffusion of innovation (DOI)
theory, or the model of innovation resistance (MIR). So far, cloud-based ERP adoption has been analyzed
in terms of TOE [5–15], DOI [16–20], and MIR [21–23]. TOE-centered research has the advantage of
approaching ERP from a comprehensive point of view, but it cannot reflect the innovation characteristics
of new technology, like cloud-based ERP. The introduction of the DOI perspective might reflect the
innovation characteristics of the technology itself, as indicated above, but this only considers the new
technology as a positive facilitator, and does not reflect its resistance factors. Therefore, only by adding
the MIR perspective to the above two perspectives can a truly comprehensive view be achieved.
With this background, this study intends to provide insights into what factors should be considered
regardingthe adoption of cloud-based ERP. Based onthe TOE,DOI, andMIRframeworks, a researchmodel
was developed to identify influential factors on intention to adopt cloud-based ERP. Specifically, a total of
10 characteristics are analyzed by classifying cloud-based ERP preference characteristics (i.e., information
and communications technology (ICT) skill, organizational culture, regulatory environment, relative
advantage, complexity, trialability, and observability) and resistance characteristics (i.e., data security,
vendor lock-in, and customization) for this study.
2. Theoretical Background
2.1. Cloud-Based ERP and On-Premise ERP
A cloud-based ERP is a cloud computing environment. It is invisible like a cloud, but the ERP
package is run under a computing resource that exists in something on the Internet [24–26]. On the
other hand, an on-premise ERP is directly installed on to one or more physical servers. Therefore,
it requires hardware, a software environment, and personnel [26]. ERP packages as infrastructure as a
service (IaaS) are also used, as well as PaaS or SaaS ERP packages, which are run in large part or entirely
by the ERP package provider. The main benefit of a cloud-based ERP as opposed to an on-premise
ERP is that it minimizes the cost of access and requires less information technology (IT) support and
maintenance. Access to reliable information, avoiding the duplication of data in the database, reduction
of adoption and cycle time, cost savings, improved scalability, and less maintenance are some of the
other benefits of implementing a cloud-based ERP [27]. The advantages of a cloud-based ERP include
cost savings, initial introduction cost, manpower cost, usability, efficiency, scalability, and flexibility;
whereas ease of control is the main advantage of on-premise ERP [26].
With the continuous development of cloud computing technology, cloud-based ERP has been
developed as an alternative to the on-premise solution. According to Grabski et al. [28], cloud computing
can fundamentally change the ERP environment. The data and the application are no longer on the
premises of a company. Rather, a provider offers access to the application that can be adapted to the
needs of the user and hosts the data securely on the Internet. Many research questions are related to
this evolutionary method of ERP system. Arnesen ([29], p. 47) added that “As the market shifts to cloud
environments, ERP vendors are developing hosting or cloud solutions”. According to Mezghani ([30],
p. 47), “Cloud-based ERP appears to have become a real alternative to on-premises ERP, and companies
are likely to push for cloud solutions”. A recent study examined the hidden link between one of the key
pillars of Industry 4.0 (e.g., cloud-based ERP) and the attributes of sustainable corporate performance,
Sustainability 2020, 12, 6426 3 of 26
considering the impact of variables such as company size, cloud service type and possible offers used as
control variables and achieve sustainable performance at the same time [31].
2.2. Cloud-Based ERP Adoption
From a comprehensive review of the literature, Ngai et al. ([32], p. 1) identified 18 critical success
factors (CSF), including more than 80 sub-elements, of ERP adoption by an organization. Among the
18 CSFs, the two most frequently mentioned in reference to ERP system adoption are “training and
education” and “top management support”. According to Bhaaradwaj and Lal [33], several factors,
such as organizational attitude, credibility, apparent ease of use, and relative advantage towards
technology influence an organization’s decision to adopt cloud computing. Bellamy [34] discovered that
lack of skilled labor, data security risks, and high cost are the main reasons why organizations hesitate
to adopt cloud-based services. Kinuthia [5] believed that compatibility, competitive pressure, employee
IT expertise, business size, and cost are the key factors to consider when adopting a cloud-based ERP.
In Table 1, which summarizes the research on cloud-based ERP adoption, it is evident that a
number of studies have been done on the benefits and challenges of cloud-based ERP, framework
development for cloud-based ERP, and the factors affecting providers’ cloud-based ERP implementation
perspective. However, from the perspective of an enterprise or organization, only limited research has
been conducted that has played an important role in cloud-based ERP adoption by organizations. Most
recent studies have been conducted only in the context of developed countries. Peng and Gala [21]
identified the benefits of, and barriers to, cloud-based ERP adoption, articulating system speed and
performance, ERP cost and support, ERP mobility, and system upgrade and enhancement as benefits,
while highlighting organizational challenges, vendor lock-in, data security, transparency and data
privacy, and integration difficulties as barriers.
Recently, Adnan AlBar and Md. Hoque [6] studied the factors affecting cloud-based ERP adoption
in Saudi Arabia, and Moh’d Anwer Al-Shboul [7] sought a better understanding of the logistical factors
of cloud-based ERP adoption by small and medium enterprises (SMEs) in developing economies.
There have been studies conducted in Europe, South Africa, Egypt, and Taiwan. Yu-Wei Chang [35]
explored the enablers and inhibitors that drive organizations to switch to cloud-based ERP systems.
EPR packages are mainly used in Korea and Japan in Asia, while few studies have been conducted in
Korea or Japan.
Table 1. Literature review on cloud-based enterprise resource planning (ERP) adoption.
Framework/Sample Findings Reference
Semi-Structured Interviews/Six companies
This study identified the potential security issues caused
by the deployment of cloud-based ERP systems from the
perspective of the provider.
[36]
Interviews/16 IT consultants
The study identified cloud-based ERP benefits and barriers
to cloud-based ERP adoption, highlighting vendor lock-in,
data security, and integration difficulties as barriers.
[21]
Survey/637 American IT security
practitioners
This study found that cloud-based ERPs ensure data
security, Internet accessibility, and business profit at a
minimum cost in business organizations.
[37]
Interviews/Three companies
This study developed a framework that explains how small
and large businesses are associated with the opportunities
and concerns identified in adopting cloud-based ERP.
[38]
Online survey/136
organizations/participants in Saudi Arabia,
TOE, DOI
This study found that the TOE and DOI factors were
strongly related to cloud-based ERP adoption. [6]
Quantitative method with UTAUT2
model/Google Form from 30 companies
in Indonesia
Habits and behavioral intentions have a significant impact
on users of systems that adopt and use cloud-based ERP in
large Indonesian companies.
[39]
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Table 1. Cont.
Framework/Sample Findings Reference
TOE framework/131 respondents in SMEs
in developing economies (Bahrain, Egypt,
Emirates, Jordan, Kuwait, Lebanon, Oman,
Qatar, Saudi Arabia, and Turkey)
Competitive pressure, high-level management support,
enterprise readiness, enterprise size, technological
readiness, and technical barriers have a major impact on
the adoption of cloud-based ERP.
[7]
Survey/212 participants in Taiwan, TOE,
ECT (Expectation confirmation theory),
Two-factor theory
Technology (system quality), organization (financial
advantage), and environment (industry pressure) contexts
are identified to be the prerequisites of switching benefits.
The perceived risk of a cloud-based ERP system, as well as
the satisfaction and use scope of an existing ERP system, can
be regarded as a predictive indicator of conversion costs.
[35]
2.3. Technology-Organization-Environment (TOE) Framework
The TOE framework was developed for exploring the adoption of technology by Tornatzky and
Fleischer [40]. The framework examines three important factors that influence the adoption of new
technologies: technology, organization, and environment [41]. The TOE framework has been widely
utilized to research the adoption of cloud-based ERP [5–15] and cloud computing [42]. Juiz et al. [43]
used the TOE framework for SaaS ERP adoption by SMEs, and explored perceived value, security
concerns, configurability and customization, organizational readiness, top management, competitive
pressure, and vendor qualities. Qian et al. [12] collected 102 valid data records from the manufacturing
and service sector and developed a theoretical model using the TOE framework, to discover the fact
that the top management support factor significantly and positively correlated with the intention to
adopt cloud-based ERP systems by manufacturing SMEs in Malaysia.
AL-Shboul [7] examined cloud-based ERP adoption based on the TOE framework and identified
competitive pressure, top management support, enterprise readiness, enterprise size, technological
readiness, and technical barriers as factors that exerted a significant influence on cloud-based ERP
adoption. AlBar and Hoque [6] also investigated cloud-based ERP adoption intention based on the
TOE framework, and found that ICT skill, ICT infrastructure, top management support, regulatory
environment, and competitive environment were significantly related to cloud-based ERP adoption,
while organizational culture was not significantly related. Yu-Wei Chang [35] studied cloud-based
ERP switching intention using the TOE framework, and discovered that financial advantage, system
quality, and industry pressure were significantly related to cloud-based ERP switching intention, while
information quality and government support were not significantly related.
2.4. The Diffusion of Innovation (DOI) Theory
Innovation was defined by Rogers as “an idea, practice, or object that is perceived as new by
an individual or other unit of adoption” ([44], p. 11). The DOI theory proposes that five innovative
features affect the IT adoption intention. These five characteristics are believed to play an important
role in helping businesses embrace new innovative technologies (e.g., cloud-based ERP) [6].
Tornatzky and Kelin [45] revealed that one of the most extensive research and innovation features
that determines the IT adoption rate—including the enterprise-level cloud-based ERP adoption rate—
is a relative advantage. Most companies believe that cloud-based ERP saves time and money, promotes
communication, and leads to more efficient collaboration of new business applications, as compared
with traditional ERP systems, so that they are interested in adopting cloud-based ERP [16]. According
to Premkumar [17], compatibility is identified as one of the most important components of innovation
adoption using information systems. However, more complicated company innovation processes are
controversial. Complexity is the main barrier to a rapid pace of technology adoption [18].
Meanwhile, observability and trialability are important factors that influence the implementation
of innovative IT, such as cloud-based ERP [19]. Jeyaraj et al. [20] revealed that these two innovation
characteristics significantly impact ICT adoption such as cloud-based ERP. Lim [24] studied characteristics
Sustainability 2020, 12, 6426 5 of 26
of innovation as the introductory characteristics of cloud computing services with relative advantage,
complexity, suitability, cost reduction, attemptability, and observability. Shin [46] defined relative
advantage, suitability, and complexity as the innovation characteristics of cloud computing services,
and explored various factors, such as psychological factors, innovation resistance, and management
will, in three dimensions.
2.5. The Model of Innovation Resistance (MIR)
The model of innovation resistance (MIR) was introduced by S. Ram [47]. He stated that innovation
resistance is not the obverse of innovation adoption. Instead, for innovation to be adopted, it must
overcome some initial resistance. Thus, the length of time of resistance demonstrates whether or not
the innovation will be well-received. If resistance is too high, the innovation dies, and adoption will fail.
Furthermore, resistance and adoption can coexist during the stages of innovation [47]. Ram identified
two main causes of innovation resistance: perceived risk and cognitive resistance [48].
Yoo and Lee [49], who used MIR as the main acceptance theory, shows that with anxiousness and
risk-awareness about wrong purchases, consumers feel that they are losing money due to innovation,
or that their confidence is threatened. When the information or belief about innovation is unclear and
confusing, the consumer becomes psychologically defensive and can resist. Benlian and Hess [50] identified
that security threats are the dominant factors influencing IT executives’ overall risk perceptions.
Faasen et al. [22] found that customization limitations, lack of vendor trust, loss of control,
data security risk, and functionality fit were the main factors causing resistance in cloud-based
ERP adoption. Kim [51] proved that three factors are highly correlated with innovation resistance
among consumers—relative advantage, complexity, and suitability. The higher the relative advantage,
the lower the innovation resistance. The higher the complexity, the higher the innovation resistance and
the higher the suitability, but the lower the innovation resistance. It has been verified that innovation
resistance has a negative effect on the intention to recommend.
Peng and Gala [21] identified data security and vendor lock-in as barriers to cloud-based ERP adoption.
Demi and Haddara [23] posited that barriers in organizations to cloud-based ERP adoption include
privacy and security issues, cloud-based ERP system configuration and customization capability level,
and vendor lock-in. Abd Elmonem et al. [3] identified the major identified challenges as customization
and integration limitations, data ownership, functionality limitations, performance risk, security risk,
and service-level agreement (SLA) issues.
3. Research Model
The research model illustrated in Figure 1 empirically examines the impact of TOE, innovation,
and resistance characteristics on cloud-based ERP adoption intention. Sustainability 2020, 12, x FOR PEER REVIEW 6 of 27
Figure 1. Research model.
3.1. Technological Context: Cloud-Based ERP Skill (ICT Skill)
The technological context includes employees’ ICT skills and the ICT infrastructure. Although a
cloud-based ERP is undoubtedly a labor-saving and innovative technology, adoption is challenging
and confusing due to the fact that it requires essential ICT skills. [52]. Lutovac and Manojlov [53]
found that if a company’s employees lacked certain ICT skills, they would be upset and eventually
lose motivation, investing more time and energy in participating in adopting ERP solutions. SME
o T a
Figure 1. Research model.
Sustainability 2020, 12, 6426 6 of 26
3.1. Technological Context: Cloud-Based ERP Skill (ICT Skill)
The technological context includes employees’ ICT skills and the ICT infrastructure. Although a
cloud-based ERP is undoubtedly a labor-saving and innovative technology, adoption is challenging
and confusing due to the fact that it requires essential ICT skills. [52]. Lutovac and Manojlov [53]
found that if a company’s employees lacked certain ICT skills, they would be upset and eventually
lose motivation, investing more time and energy in participating in adopting ERP solutions. SME
owners that have poor ICT skills might not be inclined to adopt ICT, and thus perceive IT adoption as
difficult [54].
Deficiencies in or a lack of ICT skills and knowledge is a critical challenge affecting most of
SMEs [55]. The rapid development of ICT has brought about tremendous business opportunities,
as well as challenges. One of these challenges is the need for greater ICT skills and expertise in adopting
and implementing emerging technologies [53]. Based on the aforementioned previous studies, it is
hypothesized that cloud-based ERP skill positively impacts cloud-based ERP adoption intention.
Hypothesis 1 (H1). Cloud-based ERP skill (IS) is positively (+) related to the intention to adopt cloud-based
ERP (IA).
3.2. Organizational Context: Organizational Culture
Early research on ERP adoption identified a set of organizational characteristics that might explain why
organizations accepted or rejected certain innovations [56]. The most frequently mentioned organizational
characteristics influencing cloud-based ERP adoption are organizational culture and high-level management
support [57].
Romm et al. [58] showed that the relationship between information systems and organizational
culture is critical for companies realizing the potential benefits promised by the system. Organizational
culture is both the main driving factor and the impeding factor for cloud-based ERPs in promoting the
adoption of innovative technologies [59]. Jone et al. [60] found that organizational culture influences
employee attitudes towards ERP adoption, and eventually contributes to the successful implementation
of cloud-based ERP. Ke and Wei [61] also noted that cloud-based ERP implementation is related to
organizational culture.
When adopting cloud-based ERP, senior management support is considered to be the most important
CSF. Senior management in an organization determines the resource allocation required to successfully
adopt cloud-based ERP, and approves the project before execution [62,63]. Low et al. [16] revealed that
the level of support provided by senior management influenced cloud-based ERP adoption. Based on
these findings, we developed the following hypothesis:
Hypothesis 2 (H2). Organizational culture (OC) is positively (+) related to the intention to adopt cloud-based
ERP (IA).
3.3. Environmental Context: Regulatory Environment
Environmental context canbedividedintothe regulatory environment andthe competitive environment.
The support of the regulatory environment is an important factor in innovation adoption [64–66]. Previous
research has discovered that government regulations and policies are critical drivers that might affect the
adoption of innovative technologies such as cloud-based ERP, especially in developing countries [67].
Li [68] believes that an organization is likely to adopt a new technology if the government has a clear
obligation to the new technology.
Complying with data, energy, and environmental standards are other difficulties which are
faced by cloud-based ERPs, and there are not enough regulations to handle them [3]. Thus, friendly
regulatory environments positively contribute to the initiation and adoption of IT [69]. The following
hypothesis is based on the above findings:
Sustainability 2020, 12, 6426 7 of 26
Hypothesis 3 (H3). Regulatory environment (RE) is positively (+) related to the intention to adopt cloud-based
ERP (IA).
3.4. Innovation Characteristics
3.4.1. Innovation Characteristic: Relative Advantage
Tornatzky and Kelin [45] described relative advantage as one of the most widely researched
innovative features of enterprise IT applications at the firm level. Rogers ([70], p. 229) defined it as
“the degree to which an innovation is perceived as being better than the idea it supersedes”. Most
enterprises adopt cloud-based ERPs, because they understand that doing so will more significantly
accelerate communications, save money and time, and lead to the efficient synchronization of new
applications of business ideas than traditional ERP systems [16].
Premkumar and Roberts [71] stated that the results of the discriminant analysis of data demonstrated
that relative advantage, organizational size, top management support, competitive pressure, and external
pressure are they key determinants of information systems adoption. Low et al. [16] discovered that
relative advantage, competitive pressure, firm size, top management support, and trading partner
pressure characteristics have a significant effect on the adoption of cloud computing. In light of these
findings, this study hypothesizes the following:
Hypothesis 4 (H4). Relative advantage (RA) is positively (+) related to the intention to adopt cloud-based
ERP (IA).
3.4.2. Innovation Characteristic: Complexity
Higher complexity is the main reason leading to slower technology adoption [18]. It is defined as
“the degree to which an innovation is perceived as relatively difficult to understand and use” ([70],
p. 257). Larger organizations feature various complexities such as restrictions in data access; when
a high volume of data is handled by a cloud vendor, an organization cannot monitor the software
complexity. SMEs have a less complex structure and remain unimpacted by this challenge [72].
Large companies use their ERP, from the standpoint of complexity, to support industry-specific
functionality and achieve real-time integration with machinery and other complex legacy systems [38].
The characteristics of SMEs (e.g., smaller size, less complexity) could make the implementation of a
cloud-based ERP less complex, thereby encouraging the company to continue with their intention to
adopt such a system [73]. Based on the above findings, the following hypothesis is posited:
Hypothesis 5 (H5). Complexity (CO) is negatively (–) related to the intention to adopt cloud-based ERP (IA).
3.4.3. Innovation Characteristic: Trialability
Trialability is an important factor that supports the implementation of innovative IT [19,74,75].
Jeyaraj et al. [20] found that trialability has a great influence on the adoption of ICT, such as cloud-based
ERP. It is defined as “the degree to which an innovation may be experimented with on a limited
basis” ([70], p. 258). Thus, the following hypothesis is put forth:
Hypothesis 6 (H6). Trialability (TR) is positively (+) related to the intention to adopt cloud-based ERP (IA).
3.4.4. Innovation Characteristic: Observability
Observability is also an important factor that supports the implementation of innovative IT [19,74,75].
Jeyaraj et al. [20] found observability to have a great influence on the adoption of ICT such as cloudbased ERP. It is defined as “the degree to which the results of an innovation are visible to others” ([70],
p. 258). Based on these findings, the following hypothesis is put forth:
Sustainability 2020, 12, 6426 8 of 26
Hypothesis 7 (H7). Observability (OB) is positively (+) related to the intention to adopt cloud-based ERP (IA).
3.5. Resistance Characteristics
3.5.1. Resistance Characteristic: Data Security
It is often said in the industry that cloud vendors are able to provide better IT infrastructure
and more effectively protect data security. However, reviewing the literature demonstrates that data
leakage and loss is more likely attributable to human causes than technical failures [76].
Specifically, the integrated nature of ERP determines that the data stored in the system can be
shared and used by different organizational units. Therefore, managers have access to data in other
business areas as well as in their own department. With conventional ERP systems, administrators
often save multiple copies of important company data on personal computers (PCs), laptops, hard
disk drives and memory sticks. However, if one of these hardware devices is damaged or lost, risk of
unauthorized access to the data stored on the devices increases. Moreover, internal employees can
download confidential company data from the system and illegally pass it on to competitors for higher
profits [76].
If ERP data is hosted by a third-party cloud provider, the client company has less control over
who accesses its confidential data. Such a lack of control in the cloud environment inevitably leads to
additional threats to customer companies’ data security [21]. To ensure confidentiality, a company
should establish data security guidelines, and negotiate with its cloud providers [77]. With these
investigations, the following hypothesis regarding data security is developed.
Hypothesis 8 (H8). Data security (DS) is negatively (–) related to the intention to adopt cloud-based ERP (IA).
3.5.2. Resistance Characteristic: Vendor Lock-in
Overall, the market for cloud-based ERP and cloud services in general is still relatively emerging
and immature. Therefore, the quality of cloud-based ERP applications and services provided by
different vendors may vary significantly [21]. If a company is not satisfied with their current cloud
services, it might inevitably want to switch to another service provider. However, changing to a new
cloud-based ERP provider might not be that easy, for several reasons. First, due to the complexity
of cloud-based infrastructure, migrating ERP data from one provider to another can be significantly
expensive and time-consuming. Second, certain legal restrictions imposed by the current cloud
provider might make it difficult for a user company to retrieve and relocate their ERP data to another
cloud provider’s servers during, or at the end of, an existing service contract. Moreover, the new
ERP package is likely to reshape and change business processes, structures, distributions of power,
and organizational culture [78].
Therefore, changing an existing ERP package requires making changes in many other organizations,
management, and operations. Due to these potential challenges and difficulties, even in the case of
unsatisfactory service, a user company might not be able to switch their cloud-based ERP provider.
A further review of the literature identified that this issue, often known as the vendor lock-in scenario,
occurs very commonly in the cloud environment [79,80]. Opara-Martins [81] studied a decision-making
framework to mitigate vendor lock-in risks in (SaaS) cloud migration. Vendor lock-in and switching
costs have become two significant obstacles to the retirement of cloud-based ERP systems. Using the
existing studies, the following hypothesis is developed.
Hypothesis 9 (H9). Vendor lock-in (VL) is negatively (–) related to the intention to adopt cloud-based ERP (IA).
3.5.3. Resistance Characteristics: Customization
It is considered that cloud-based ERP is standardized, as everybody uses the same software. It is
difficult to customize because the environment is stricter and users have less control, as they do not
Sustainability 2020, 12, 6426 9 of 26
own the system [64]. This is one of the challenges faced by cloud computing in general [82]. System
customization is defined as “the degree to which an ERP system was altered to meet the needs of a
business unit” ([83], p. 1753).
Johansson et al. [38] was able to develop a framework that shows how SMEs and large enterprises
are associated with opportunities and concerns regarding cloud-based ERP adoption. The framework
identified the limited customization capabilities of cloud-based ERP and limited integration capabilities,
with complex legacy systems as a major concern, especially for large organizations. Abd Elmonem et al. [3]
mentioned that customization and integration limitations are some of the key challenges of cloud-based
ERP. These limitations do not exist in traditional ERP systems.
Juiz et al. [43] noted that cloud-based ERP systems are less flexible than on-premise systems and
offer minimal customization options. So, the lack of configurability and customization negatively
affects the adoption of cloud-based ERP systems [13]. Thus, the previous empirical findings lead us to
hypothesize the following:
Hypothesis 10 (H10). Customization (CU) is negatively (–) related to the intention to adopt cloud-based ERP (IA).
Table 2 presents the operational definitions of the variables with references.
Table 2. Operational definition of research variables.
Variable Definition References
ICT Skill The degree to which the skill level of employees utilizing
IT technologies, such as computer, network, and software [52–55]
Organizational Culture The degree to which level of an organization is
responsive and flexible [16,56–63]
Regulatory Environment The degree to which less stringent
regulatory environment [3,56–63]
Relative Advantage The degree to which an innovation is perceived as being
better than the idea it supersedes [45,70,71]
Complexity The degree to which an innovation is perceived as
relatively difficult to understand and use [18,38,72,73]
Trialability The degree to which an innovation may be experimented with on a limited basis [19,20,74,75]
Observability The degree to which the results of an innovation are visible to others [19,20,74,75]
Data Security The degree to which poor data protection practices
adopted and unauthorized data access occurred [21,76,77]
Vendor Lock-in The degree to which legal and contract restrictions on
vendor change [21,23,78–81]
Customization The degree to which the level of customization limitation [3,13,38,43,64,82,83]
Cloud-based ERP
Adoption Intention
The degree to which the level of cloud-based ERP
adoption intention [5–7,21,32–35]
4. Research Methodology
4.1. Measurements
A survey was conducted to verify the research model. The measured items regarding TOE
and innovation characteristics were selected from prior studies [5–20,24,35,40–46,52–75]. In addition,
the measured items regarding resistance characteristics were selected from revisions of existing items
to better fit the research context [3,13,21–23,38,47–51,76–83]. Using a five-point Likert scale (1: very
strongly disagree to 5: very strongly agree), all the measurements were scored. In addition, two
Sustainability 2020, 12, 6426 10 of 26
professional information system researchers reviewed the survey to confirm its face validity. Therefore,
their inputs were reflected in the final list of items measured as a part of the survey. The detailed items
of the survey questionnaire are presented in Appendix A.
4.2. Data Collection
Data were collected from chief executive officers (CxOs) (e.g., CEO, CFO, and CIO) or key
stakeholders such as IT directors who represented each Korean company adopting and operating ERP
systems. The population targeted was CxOs and key stakeholders of enterprises, because they have
the authority make cloud-based ERP system adoption decisions. To generate a list of target companies,
we first identified the contact information of those who used an ERP package from an ERP-related
company such as SAP, Oracle, Rimini Street, or an IT consulting firm.
Among 500 companies, 159 companies participated in the survey. These companies were SMEs
to large enterprises. Survey questionnaires were distributed by phone and e-mail to invite them to
participate in the survey. The survey was conducted between February and April 2020. Overall, 159
out of about 500 survey questionnaires were received. Eleven out of the 159 survey questionnaires
received were excluded, due to missing answers identified from some part of the responses.
As a result, 148 received survey questionnaires from 148 companies were utilized for the analysis.
Most enterprises used SAP or Oracle (62.2%). The manufacturing portion was 44.6% and the aboveexecutive portion was 35.8%. If we count above the senior manager level, it was 81.8%. The samples’
enterprises and demographics are described in Tables 3 and 4.
Table 3. Descriptive features of respondent enterprises.
Respondents (n = 148) Frequency Percentage (%)
Annual Revenue Size
Less than 50M USD 43 29.1
50M~300M USD 26 17.6
300M~1B USD 23 15.5
1B~5B USD 32 21.6
More than 5B USD 24 16.2
Number of Employees
Less than 200 41 27.7
200~499 28 18.9
500~999 14 9.5
1000~4999 37 25.0
5000~9999 10 6.8
More than 10,000 18 12.1
ERP Package
SAP 72 48.7
Oracle 20 13.5
MS Dynamics 2 1.4
Douzone,
Younglimwon(Korean ERP) 36 24.3
Self-Developed 18 12.1
Industry
Manufacturing 66 44.6
IT/Communications/Services 38 25.7
Finance 14 9.5
Distribution 14 9.5
Construction 9 6.0
Others 7 4.7
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Table 4. Demographic features of respondents.
Respondents (n = 148) Frequency Percentage (%)
Gender Male 127 85.8
Female 21 14.2
Age
30s 29 19.6
40s 58 39.2
Over 50s 61 41.2
Title
Manager 27 18.2
Senior Manager 68 46.0
Executive 30 20.3
CIO/CFO/CEO 23 15.5
Department
IT Planning 64 43.2
IT Operations 23 15.5
Finance/Procurement 37 25.1
Others 24 16.2
5. Analysis and Results
Statistical Package for Social Science (SPSS) Version 21 was used to analyze the collected data.
The sequence and method of analysis were applied as follows. First, to determine the distribution of
enterprises and respondents, frequency and percentages were calculated. Second, validity and reliability
tests were conducted. Third, multiple regression analysis was performed to verify the research hypotheses.
Lastly, the results and findings are summarized.
5.1. Measurement Model
To analyze the validity and reliability of the measurement tools, factor analysis and reliability
analysis were performed, respectively. The measurement variables of this study were partially removed
through the scale refinement process.
First, exploratory factor analysis (EFA) was conducted to verify validity. All component variables
used principal component analysis (PCA) to extract constituent factors, and Varimax was adopted to
simplify factor loading. The selection criteria for items in this study were based on an eigenvalue of 1.0
or more and a factor loading of 0.40 or more. Out of 55 question items, two items were removed to fit
the theoretical structure and 53 items were used for analysis.
In the reliability analysis, it is considered reliable if the Cronbach’s alpha value is 0.6 or higher; all
factors are 0.8 or higher in this study. The KMO sample fit is for determining the relevance between
variables. Generally, if the KMO value is 0.9 or higher, it is very high, and if it is 0.8 ~ 0.89, it is
rather high. If it is less than 0.5, it is judged to be unacceptable [84]. The study shows 0.86, which is
moderately acceptable. The Bartlett sphericity test indicates the suitability of the factor analysis model,
and is judged as a significant probability. If the significance probability is less than 0.05, it is possible to
facilitate the factor analysis model. In other words, it can be concluded that the use of factor analysis is
appropriate, and that common factors exist [85]. The study shows 0.00, so it can be judged that all of
the factor analysis models used in this study are suitable.
Tables 5 and 6 present descriptive statistics of the measurement instruments, and Table 7 shows
the results of validity and reliability analyses.
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Table 5. Descriptive statistics of the survey items.
Variable Item Avg. S.D.
Cloud ERP Skill (ICT Skill)
IS1 3.277 1.105
IS2 2.986 1.184
IS3 3.122 1.298
IS4 2.932 1.182
IS5 3.061 1.162
Organizational Culture
OC1 3.169 1.145
OC2 3.459 0.950
OC4 3.378 1.103
OC5 3.203 1.088
Regulatory Environment
RE1 2.899 1.002
RE2 2.655 1.022
RE3 3.054 1.009
RE4 2.905 0.957
RE5 3.054 0.909
Relative Advantage
RA1 3.534 0.972
RA2 3.358 1.024
RA3 3.493 0.986
RA4 3.203 1.195
RA5 3.480 1.020
Complexity
CO1 2.757 0.994
CO2 3.000 1.043
CO3 3.020 1.066
CO4 2.608 0.945
CO5 3.257 1.057
Trialability
TR1 3.541 1.033
TR2 3.622 1.006
TR3 3.601 0.974
TR4 3.547 0.950
TR5 3.439 1.045
Observability
OB2 3.209 0.949
OB3 3.068 1.021
OB4 2.912 1.112
OB5 2.973 1.125
Data Security
DS1 3.439 1.191
DS2 3.061 1.051
DS3 2.676 1.096
DS4 2.973 1.240
DS5 3.270 1.216
Vendor Lock-In
VL1 3.831 0.936
VL2 4.000 0.911
VL3 3.669 1.033
VL4 3.845 0.967
VL5 3.946 0.924
Customization
CU1 3.676 1.018
CU2 3.527 0.965
CU3 3.736 0.928
CU4 3.824 0.967
CU5 3.669 0.986
Cloud ERP Adoption Intention
IA1 2.993 1.301
IA2 3.128 1.185
IA3 3.162 1.184
IA4 2.845 1.249
IA5 2.689 1.344
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Table 6. Correlation of the research variables.
Variable Avg. S.D. 1 2 3 4 5 6 7 8 9
1 Cloud ERP Skill (ICT Skill) 3.076 1.058
2 Organizational Culture 3.302 0.963 0.652 **
3 Regulatory Environment 2.914 0.818 0.401 ** 0.381 **
4 Relative Advantage 3.414 0.874 0.345 ** 0.443 ** 0.460 **
5 Complexity 2.928 0.846 –0.205 * –0.310 ** 0.073 –0.181 *
6 Trialability 3.550 0.848 0.499 ** 0.590 ** 0.434 ** 0.467 ** –0.246 **
7 Observability 3.041 0.926 0.482 ** 0.506 ** 0.564 ** 0.503 ** –0.060 0.571 **
8 Data Security 3.084 0.978 0.070 –0.006 0.086 0.033 0.425 ** 0.060 0.149
9 Vendor Lock-In 3.858 0.737 0.025 0.091 –0.052 –0.011 0.292 ** 0.136 0.041 0.351 **
10 Customization 3.686 0.846 0.035 0.022 0.087 –0.057 0.390 ** 0.040 –0.008 0.279 ** 0.499 **
* Significant at the 0.05 level (two-tailed), ** Significant at the 0.01 level (two-tailed).
Sustainability 2020, 12, 6426 14 of 26
Table 7. Factor analysis and reliability.
Variable Item Factor Loading Communality Eigenvalue Total Variance
Explained
Cronbach’s
α
Cloud ERP
Skill (ICT Skill)
IS1 0.788 0.744
4.756 8.973 0.932
IS2 0.871 0.855
IS3 0.777 0.718
IS4 0.861 0.836
IS5 0.804 0.831
Organizational
Culture
OC1 0.581 0.786
OC2 0.716 0.719 2.722 5.136 0.915
OC4 0.679 0.836
OC5 0.728 0.851
Regulatory
Environment
RE1 0.761 0.713
3.535 6.670 0.881
RE2 0.723 0.764
RE3 0.667 0.653
RE4 0.749 0.742
RE5 0.796 0.713
Relative
Advantage
RA1 0.789 0.801
3.848 7.261 0.894
RA2 0.768 0.790
RA3 0.831 0.789
RA4 0.710 0.726
RA5 0.717 0.694
Complexity
CO1 0.811 0.756
3.437 6.485 0.878
CO2 0.772 0.742
CO3 0.745 0.754
CO4 0.793 0.719
CO5 0.649 0.638
Trialability
TR1 0.750 0.744
4.089 7.716 0.902
TR2 0.827 0.805
TR3 0.762 0.766
TR4 0.716 0.671
TR5 0.692 0.713
Observability
OB2 0.593 0.652
OB3 0.734 0.817 2.989 5.640 0.897
OB4 0.781 0.804
OB5 0.779 0.797
Data Security
DS1 0.782 0.726
3.822 7.212 0.902
DS2 0.849 0.795
DS3 0.755 0.755
DS4 0.850 0.814
DS5 0.807 0.795
Vendor Lock-In
VL1 0.659 0.585
3.134 5.913 0.835
VL2 0.801 0.737
VL3 0.621 0.677
VL4 0.771 0.726
VL5 0.715 0.708
Customization
CU1 0.842 0.786
3.961 7.473 0.917
CU2 0.854 0.804
CU3 0.764 0.715
CU4 0.796 0.734
CU5 0.881 0.843
Cloud ERP
Adoption
Intention
IA1 0.791 0.867
4.082 7.703 0.952
IA2 0.703 0.827
IA3 0.736 0.861
IA4 0.777 0.833
IA5 0.836 0.862
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5.2. Hypothesis Test
Multiple regression analysis was performed to verify the hypothesis. The multiple regression
analysis is used to verify the causal relationship between two or more independent variables and
one dependent variable. Since there are two or more independent variables, multicollinearity may
occur. Multicollinearity means the possibility of a high correlation between independent variables.
The basic assumption of the regression model is that there is no correlation between the independent
variables. However, the occurrence of multicollinearity results in ignoring the basic assumption of the
regression model. To diagnose multicollinearity, we examined tolerance and variance inflation factor
(VIF). As shown in Table 8, the VIFs of the independent variables are generally low (all of them are
much less than 10). All of the tolerance values of the independent variables are also greater than 0.1.
So, multicollinearity is not present in our model [84].
Table 8. Regression test results.
Variable
Unstandardized
Coefficients 1
Standardized
Coefficients 1
t-Value Sig.
Collinearity
Statistics
B Std. Error Beta Tolerance VIF
(Constant) –0.624 0.433 –1.439 0.152
ICT Skill (IS) 0.048 0.072 0.044 0.672 0.503 0.521 1.920
Organizational Culture (OC) 0.385 0.087 0.322 4.451 0.000 0.432 2.316
Regulatory Environment (RE) 0.205 0.090 0.145 2.283 0.024 0.556 1.798
Relative Advantage (RA) 0.318 0.079 0.241 4.048 0.000 0.636 1.573
Complexity (CO) 0.038 0.086 0.028 0.447 0.656 0.568 1.760
Trialability (TR) 0.405 0.092 0.298 4.410 0.000 0.493 2.026
Observability (OB) 0.027 0.085 0.022 0.322 0.748 0.484 2.066
Data Security (DS) –0.050 0.066 –0.042 –0.754 0.452 0.727 1.376
Vendor lock-in (VL) –0.254 0.093 –0.162 –2.736 0.007 0.641 1.561
Customization (CU) –0.005 0.079 –0.003 –0.057 0.955 0.662 1.510
1 Dependent variable: Cloud ERP Adoption Intention (IA).
The correlation between the independent variable and the dependent variable showed a high
correlation of 0.831. The R-squared value was found to be 0.691, which means that independent variables
account for 69.1% of the dependent variable, cloud-based ERP adoption intention, IA. The adjusted
R-squared value was 0.669. Durbin–Watson has a value of 2.041, which is close to 2; since it is not close to
0 or 4, there is no correlation between the residuals, so it can be interpreted that the regression model is
suitable. Since the F-value was 30.676 and the probability of significance was 0.000 (p < 0.05), the regression
line was found to fit the model.
As a result of examining the relationship between cloud-based ERP skill (IS), which is the technological
context variable, and cloud-based ERP adoption intention (IA), Hypothesis 1 was rejected, with a t-value
of 0.672 and a p-value of 0.503. On the other hand, as a result of grasping the impact relationship between
organizational culture (OC), which is the organizational context, and IA, the t-value was 4.451 and the
p-value was 0.000, so Hypothesis 2 was adopted with statistical significance. In addition, as a result of
understanding the impact relations between the regulatory environment (RE), which is the environmental
context, and IA, the t-value was 2.283 and the p-value was 0.024, so Hypothesis 3 was adopted with
p < 0.05 statistical significance. Among the innovation characteristics, relative advantage (RA) and
trialability (TR) are related to IA, respectively, where the t-value is 4.048, the p-value is 0.000, the t-value
is 4.410, the p-value is 0.000, and Hypotheses 4 and 6 are p < 0.05. These were adopted by securing
statistical significance. However, complexity (CO) and observability (OB) were rejected with t-values
of 0.447 and 0.322, respectively, in relation to IA. Among resistance characteristics, vendor lock-in (VL)
is related to IA, t-value is –2.736, p-value is 0.007, and statistical significance is secured. Hypothesis 9
was adopted. Data security (DS) and customization (CU), Hypotheses 8 and 10, were rejected because
they were not statistically significant (p > 0.05) while their t-values were –0.754 and –0.057, respectively.
Summarizing the above hypotheses verification analysis, the figure (Figure 2) and table (Table 9) are
as follows.
Sustainability 2020, 12, 6426 16 of 26
observability (OB) were rejected with t-values of 0.447 and 0.322, respectively, in relation to IA.
Among resistance characteristics, vendor lock-in (VL) is related to IA, t-value is -2.736, p-value is
0.007, and statistical significance is secured. Hypothesis 9 was adopted. Data security (DS) and
customization (CU), Hypotheses 8 and 10, were rejected because they were not statistically significant
(p > 0.05) while their t-values were -0.754 and -0.057, respectively. Summarizing the above
hypotheses verification analysis, the figure (Figure 2) and table (Table 9) are as follows.
Figure 2. Results of the regression model.
Table 9. Hypothesis testing.
Hypothesis Path t-Value Sig. Assessment (p < 0.05)
H1 IS→IA 0.672 0.503 Not Supported
H2 OC→IA 4.451 0.000 Supported
H3 RE→IA 2.283 0.024 Supported
H4 RA→IA 4.048 0.000 Supported
H5 CO→IA 0.447 0.656 Not Supported
H6 TR→IA 4.410 0.000 Supported
H7 OB→IA 0.322 0.748 Not Supported
H8 DS→IA -0.754 0.452 Not Supported
H9 VL→IA -2.736 0.007 Supported
H10 CU→IA -0.057 0.955 Not Supported
6. Discussion and Conclusions
6.1. Discussion of Findings
The study focuses on the intention to adopt cloud-based ERP using the TOE framework,
innovation characteristics, and resistance characteristics. There have been prior studies in which TOE
or DOI was used in the analysis of factors affecting cloud-based ERP adoption, but there were very
few cases of comprehensive analysis using the TOE and DOI frameworks, and as far as we know,
this is the first comprehensive empirical study that integrates the TOE, DOI, and MRI frameworks.
This study comprehensively examined the significant relationship between technology,
organizational and environmental context; innovation characteristics; resistance characteristics; and
the intention to adopt cloud-based ERP. This study has identified the factors affecting the intention
to successfully adopt cloud-based ERP.
The empirical analysis results showed that organization culture, regulatory environment,
relative advantage, trialability, and vendor lock-in each had a significant influence (p < 0.05) on the
Figure 2. Results of the regression model.
Table 9. Hypothesis testing.
Hypothesis Path t-Value Sig. Assessment (p < 0.05)
H1 IS!IA 0.672 0.503 Not Supported
H2 OC!IA 4.451 0.000 Supported
H3 RE!IA 2.283 0.024 Supported
H4 RA!IA 4.048 0.000 Supported
H5 CO!IA 0.447 0.656 Not Supported
H6 TR!IA 4.410 0.000 Supported
H7 OB!IA 0.322 0.748 Not Supported
H8 DS!IA –0.754 0.452 Not Supported
H9 VL!IA –2.736 0.007 Supported
H10 CU!IA –0.057 0.955 Not Supported
6. Discussion and Conclusions
6.1. Discussion of Findings
The study focuses on the intention to adopt cloud-based ERP using the TOE framework, innovation
characteristics, and resistance characteristics. There have been prior studies in which TOE or DOI
was used in the analysis of factors affecting cloud-based ERP adoption, but there were very few
cases of comprehensive analysis using the TOE and DOI frameworks, and as far as we know, this
is the first comprehensive empirical study that integrates the TOE, DOI, and MRI frameworks.
This study comprehensively examined the significant relationship between technology, organizational
and environmental context; innovation characteristics; resistance characteristics; and the intention to
adopt cloud-based ERP. This study has identified the factors affecting the intention to successfully
adopt cloud-based ERP.
The empirical analysis results showed that organization culture, regulatory environment, relative
advantage, trialability, and vendor lock-in each had a significant influence (p < 0.05) on the intention to
adopt cloud-based ERP, while ICT skill, complexity, observability, data security and customization had
no significant influence (p > 0.05) on the intention to adopt cloud-based ERP.
Although ICT skill and complexity were considered to be important variables on the intention
to adopt cloud-based ERP, the result of the empirical analysis was not statistically significant in this
study. As for resistance characteristics, only vendor lock-in is statistically valid, and data security and
customization limitations, which are generally in question, are insignificant. It seems that cloud-based
ERP providers have achieved more results than in the past and trust from future prospects.
According to this study, organizational culture was identified to be important. To adopt cloud-based
ERP, it is necessary to revitalize the organizational culture. Organizations should be responsive and
flexible in adopting cloud-based ERP. In addition, it should be an organizational culture that is shared,
Sustainability 2020, 12, 6426 17 of 26
open, and easy to accept, regarding the direction of company operations. It would be desirable if the
learning organization or community of practice (CoP) was prepared to create a free forum for discussion
regarding cloud-based ERP adoption.
Second, since the regulatory environment is identified as an important factor in the adoption of
cloud-based ERP, it is important to relax laws and regulations for the activation of cloud-based ERP by
government agencies. This study reveals that the role of the government is important. The role of the
company is also important, but the government will need to loosen regulations. In the case of Korea,
the so-called “Three Data Acts” have recently been passed to remove the obstacles to cloud-based ERP
adoption. If this regulation creates an increasingly favorable environment, this study suggests that
cloud-based ERP adoption can be expanded even further. The easing of these government regulations
and policies will enhance national competitiveness as well as corporate competitiveness, through the
fourth industrial development, including cloud-based ERP.
Third, relative advantage proved to be a very important factor, and the efficiency and effectiveness
of an organization will be improved through the adoption of cloud-based ERP. It is also expected and
confirmed to be provided with timely information for decision-making. In addition to the simple cost
reduction effect, this study suggests that the messages that cloud-based ERP vendors emphasize are
receiving the relative advantage of being able to respond quickly and flexibly, as businesses expand
and pay as much as they use.
Fourth, since cloud-based ERP is a relatively new concept, it is important to try and experience
it. Before the actual adoption of cloud-based ERP, it is necessary to demonstrate how it fits in with an
organization and make sure that the requirements can be reflected to minimize trial and error before
actual use. It is important for cloud-based ERP suppliers to be open to the possibility of trial before
adopting cloud-based ERP. It should be possible to meet needs, by developing a formal demo scenario
that is simple but sufficiently experienced and capable of helping adoption decisions. This study suggests
that implementing a trial and buy program, or a limited experience to get you started to make sure
that the project is right for your organization, before the adoption decision can contribute to maximum
performance under the appropriate investment, that is, the expansion of the cloud-based ERP market.
Fifth, regarding vendor lock-in, which was verified as a resistance factor for cloud-based ERP
adoption, it was verified that this was the most worrying factor among the various resistance factors.
It is recognized that the quality of service is different depending on the cloud-based ERP vendor, and it
is vendor-dependent, because it can be contracted, technically, or on the vendor product roadmap,
because it is very difficult to move to another solution vendor after using a specific cloud-based ERP
vendor solution. It is understood in this study that there is a reluctance to adopt cloud-based ERP.
6.2. Theoretical Implications
The study has three academic implications. First, factors influencing the adoption of cloud-based
ERP were identified from the comprehensive perspective. To answer what leads to cloud-based
ERP adoption, an integrated research model was presented and analyzed through empirical analysis.
Specifically, the TOE framework was introduced for analyzing factors from the comprehensive
viewpoint. Applying the TOE framework to cloud-based ERP adoption was not only done in our
research, but also in Saudi Arabia and in Taiwan. This study comprehensively analyzed the effects
by bringing the TOE framework, ICT skills from the T perspective, organizational culture from the O
perspective, and regulatory environment variables from the E perspective.
Second, it is said that the TOE framework was introduced earlier. The TOE framework has the
limitation that it cannot consider the characteristics of an innovative technology such as cloud-based ERP.
Therefore, because cloud-based ERP is an innovative technology that is very different from traditional
ERP, it is a research subject that has characteristics that make it very important to reflect such innovation
characteristics in the model. Therefore, to reflect this in the model, the main factors of innovation
characteristics were used as variables, in accordance with DOI theory. However, in recent studies,
many studies have reported that resistance in an organization becomes difficult when new innovative
Sustainability 2020, 12, 6426 18 of 26
technologies are introduced. According to studies that researched the user’s perception of cloud-based
ERP, there are many positive views of cloud-based ERP, but there are also many negative views of
cloud-based ERP as well. In a paper recently published in Taiwan [35], both motivational factors and
risk factors were considered simultaneously. Accepting that view, we also considered factors, not only
in terms of innovation characteristics but also in terms of innovation resistance. In particular, data
security concerns, vendor lock-in concerns, and customization concerns were discovered as variables for
specialized innovation resistance factors affecting cloud-based ERP adoption. Based on these discovered
variables, this is the second theoretical implication of this study.
Third, this study targeted and investigated companies of a wide variety of industries and sizes.
In addition, in the research of these companies, it was very important to know who from the company
received the questionnaire from. This determines the quality of the analysis, which was emphasized
previously, but it is also a theoretical implication that the participation of individuals from the CxO
level in our study is very high.
6.3. Practical Implications
The study has three practical implications. First, the study provides implications for companies
seeking to introduce cloud-based ERP under the COVID-19 environment. Companies may have increased
interest in cloud-based ERP because of COVID-19, because it increases the sustainability of business
operations. However, even if it is a meaningful tool or infrastructure, it is difficult for IT to succeed if
the acceptance level of the entire organization is low. However, it is necessary to find and supplement
factors to promote or inhibit this, and this study provides a way to do this. Specifically, according to this
study, organizational culture and relative advantage are important. In particular, it is emphasized that
organizational culture is important, so it is necessary to have a good cultural background.
Second, it suggests that the role of the company is important, but the role of the government is also
important to the expansion of cloud-based ERP adoption. We considered the variable for regulation as
an environmental variable, but it turned out that there are many companies that take the regulations
seriously. This study suggests that the role of government is significant in expanding cloud-based
ERP. In particular, when introducing cloud-based ERP, there may be cases where it is not possible
to introduce cloud-based ERP, including information that is the ankle caught, such as information
protection, personal information protection, or security. In particular, there are many organizations
that are reluctant to use the public cloud, and if such institutional supplementation is not made in
these places, it may become difficult to activate.
Third, it offers strategic direction to ERP vendors. As a result of this study, since relative advantage
and trialability were important, this may be a good way to emphasize the relative advantages in this
case study, for example, and induce them to try using them when formulating sales strategies. However,
the point to be aware of is that ERP companies are very worried that customer companies are locked
into certain vendors. So, for example, when persuading a client, it suggests that it is a necessary sales
strategy to instill the perception that we are not targeting to control you, but that we are partners that
share success with each other.
Based on the results, it seems necessary to target companies with flexible organizational cultures.
Another suggestion that can be derived from the study could be cooperating more with the government
and related organizations, so that regulatory factors such as government regulations can be promptly
resolved. Moreover, it is necessary to emphasize the relative advantage, which could make it possible
to experience cloud-based ERP on a trial basis and develop successful cloud-based ERP customer cases
for marketing, so that customers can easily observe them. These findings will give practical insights
and guidelines to key decision-makers for cloud-based ERP adoption.
6.4. Limitations and Future Research Directions
There are three research limitations and future research directions of this study. First, it was good
to introduce the TOE viewpoint, but because there are too many variables in the model, only one factor
Sustainability 2020, 12, 6426 19 of 26
per T, O, and E, respectively, was considered. However, according to other papers that introduced the
TOE framework [5–15,35,42], in addition to ICT skill, ICT infrastructure, information quality, etc., were
also considered as the technology context. In addition to organizational culture, we considered, top
management support and enterprise size are considerable. Furthermore, in the environmental context,
in addition to the regulatory environment, we can also consider competitive environment, industry
pressure, etc. In future studies, these factors should be considered more comprehensively.
Second, since the results of this study were analyzed by questionnaires collected in Korea, the results
might be limited to Korea. In fact, there are some mismatched results with previous studies conducted
overseas. For example, although previous research in Saudi Arabia has shown that ICT skill and complexity
were strongly related to cloud-based ERP adoption, while organizational culture and trialability had
no significant influence [6], this study demonstrates that organizational culture and trialability have a
significant influence on cloud-based ERP adoption, while ICT skill and complexity have no significant
impact. The results from AlBar and Hoque’s [6] study were directly opposite of those from Korean
companies. According to a previous study in South Africa, customization negatively influenced the
decision to adopt [60]. From another study in the UK, data security and limited customization were
identified as major concerns, particularly relevant to large organizations [38]. Because each country’s
environment is different, the findings show that these factors’ impacts on adoptability can vary from
country to country. Therefore, it is necessary to check whether the country context has a moderating
effect on the effects of factors, through comparative studies between countries in the future.
Third, COVID-19 events continue to have a significant negative impact on business operations
globally. Thus, it is also necessary to examine how the COVID-19 pandemic affects the intention to
adopt cloud-based ERP. The greater the impact of COVID-19, the greater demand for non-contact
operations, which may increase the demand for cloud-based ERP in countries suffering from COVID-19.
Thus, we have a future plan to expand our study, to see if COVID-19 patient ratios or the degree of
containment caused by COVIDs will impact adoption of cloud-based ERP.
In addition, as a new research topic, vendor lock-in is disliked and cloud-based ERP is expensive
at the beginning, so companies are starting to choose a completely new third-party alternative.
The alternative is to choose companies called third-party maintenance (3PM). If you choose 3PM, you
can continue to use the existing on-premises ERP. This is a third alternative to avoid vendor lock-in.
Recently, sales of 3PM companies such as Rimini Street have greatly expanded. In this study, we
analyzed whether to adopt cloud-based ERP, but in the future, among the two alternatives, the adoption
of cloud-based ERP and the transition to 3PM, what will companies decide? It seems to be necessary
to conduct such a comparative study in the future.
Author Contributions: Conceptualization, B.A. and H.A.; methodology, B.A. and H.A.; software, B.A. and H.A.;
validation, B.A. and H.A.; formal analysis, B.A. and H.A.; investigation, B.A.; resources, B.A.; data curation,
B.A.; writing—original draft preparation, B.A.; writing—review and editing, B.A. and H.A.; visualization, B.A.;
supervision, H.A.; project administration, B.A.; funding acquisition, B.A. and H.A. All authors have read and
agreed to the published version of the manuscript.
Funding: This research received no external funding.
Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the
study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to
publish the results.
Abbreviations

Sustainability 2020, 12, 6426 20 of 26
SaaS Software as a Service
PaaS Platform as a Service
COVID-19 Coronavirus disease 2019

Appendix A
Table A1. Measurement Items.
Variable Item Measurement (Five-Point Likert Scale) References
Cloud ERP Skill
(ICT Skill)
IS1 Employees in our company are generally aware of the functions
of cloud-based ERP.
[52–55]
IS2 Employees in our company are well trained in cloud-based ERP.
IS3 Our company is supported by specialized or knowledgeable
personal for cloud-based ERP.
IS4 Employees in our company have enough opportunity to train
new technologies including cloud-based ERP.
IS5 Employees in our company have a high level of understanding
of new technologies including cloud-based ERP.
Organizational
Culture
OC1 Our company is responsive and flexible in adopting
cloud-based ERP.
[16,56–63]
OC2 There is a high level of agreement about how we operate in
this company.
OC4 Our company has an open and receptive organizational culture
in adopting cloud-based ERP.
OC5 Our company has an organizational culture suitable for
cloud-based ERP adoption.
Sustainability 2020, 12, 6426 21 of 26
Table A1. Cont.
Variable Item Measurement (Five-Point Likert Scale) References
Regulatory
Environment
RE1 The laws and regulations of the government support
cloud-based ERP initiatives and implementation.
[3,64–69]
RE2 The government drives the use of cloud ERP through
incentive programs.
RE3 The company requires maintaining the regulatory environment
in the use of cloud-based ERP.
RE4 The government policy has a positive impact on cloud-based
ERP adoption.
RE5 Various government regulations that are hindering cloud-based
ERP adoption are being relaxed.
Relative
Advantage
RA1 Cloud-based ERP will enhance the efficiency of our company.
[45,70,71]
RA2 Cloud-based ERP will improve the performance of
our company.
RA3 Cloud-based ERP will provide timely information
for decision making.
RA4 With cloud-based ERP adoption, we expect to see cost
savings effect.
RA5
With cloud-based ERP adoption, we will be able to respond
quickly and flexibly to our business expansion and pay only for
what we use.
Complexity
CO1 We believe that cloud-based ERP is difficult to use.
[18,38,72,73]
CO2 Integrating cloud-based ERP in our work practices will
be difficult.
CO3 Our company may encounter some difficulties in maintaining
the cloud-based ERP platform.
CO4 Cloud-based ERP is complex to use.
CO5 Our company is expected to have a long stabilization period for
stable use after cloud-based ERP adoption.
Trialability
TR1 The company experiments on cloud service applications before
deciding whether to use it.
[19,20,74,75]
TR2
We were allowed to use cloud-based ERP services on an
experimental basis long enough to understand how it fits into
the company.
TR3 It is easy to correct mistakes when using cloud-based ERP.
TR4 Before cloud-based ERP adoption, it is possible to confirm
whether the requirements of the company can be reflected.
TR5 Before cloud-based ERP adoption, the company’s additional
requirements can be reflected.
Observability
OB2 It is easy to observe the benefits of partner cloud-based
ERP usage.
[19,20,74,75]
OB3 We have seen many partners use cloud-based ERP.
OB4 It is observed that companies in the same industry are using
cloud-based ERP.
OB5 It is observed that companies of the same size use
cloud-based ERP.
Sustainability 2020, 12, 6426 22 of 26
Table A1. Cont.
Variable Item Measurement (Five-Point Likert Scale) References
Data Security
DS1 There are poor data protection practices adopted by companies
and cloud vendors.
[21,76,77]
DS2 We have seen unauthorized data access occurred in
client companies.
DS3 We have seen unauthorized data access occurred in
cloud providers.
DS4 Cloud-based ERP’s data security is a concern because it is
unclear where the data storage is located.
DS5 We are concerned about the privacy of sensitive data held by
our company due to inconsistent data protection laws.
Vendor Lock-In
VL1 We can experience variance in the service quality of different
cloud vendors.
[21,23,78–81]
VL2 We can face the high cost of ERP re-migration after using
cloud-based ERP.
VL3 It would be very difficult to change a cloud vendor due to legal
and contractual restrictions.
VL4
When adopting a cloud-based ERP of a specific vendor, it is
unavoidable to accept the guidelines of the vendor even if it
is unreasonable.
VL5 After adopting a cloud-based ERP of a specific vendor, there is
no choice but to follow the product roadmap of the vendor.
Customization
CU1 We have customization limitations on cloud-based ERP.
[3,13,38,43,64,82–84]
CU2 The customization ability of cloud-based ERP is very limited.
CU3 We believe it is not easy to migrate to cloud-based ERP due to
heavy customization.
CU4 Developing custom code in cloud-based ERP is expensive.
CU5 It is not easy to develop with custom code after cloud-based
ERP adoption.
Cloud-based
ERP Adoption
Intention
IA1 We strongly intend to use cloud-based ERP in our company.
[5–7,21,32–35]
IA2 We like the idea of using cloud-based ERP systems.
IA3 Overall, we have a favorable attitude toward cloud-based
ERP implementation.
IA4 Our company is deeply discussing the adoption of
cloud-based ERP.
IA5 Our company is preparing for the adoption of cloud-based ERP.
References
1. Magic Quadrant for Cloud Core Financial Management Suites for Midsize, Large and Global Enterprises.
Available online: https://www.gartner.com/en/documents/3913508/magic-quadrant-for-cloud-core-financialmanagement-suite (accessed on 18 June 2020).
2. Bjelland, E.; Haddara, M. Evolution of ERP systems in the cloud: A study on system updates. Systems 2018, 6, 22.
[CrossRef]
3. Elmonem, M.A.A.; Nasr, E.S.; Geith, M.H.; Ali, M. Benefits and challenges of cloud ERP systems—A systematic
literature review. Futur. Comput. Inform. J. 2016, 1, 1–9. [CrossRef]
4. Van der Borg, F.F.; Eshuis, H.; Kusters, R.J. Customization in Cloud-Based Enterprise Resource Planning
Systems. Master’s Thesis, Eindhoven University of Technology, Eindhoven, The Netherlands, 2017.
5. Kinuthia, J.N. Technological, Organizational, and Environmental Factors Affecting the Adoption of Cloud
Enterprise Resource Planning (ERP) Systems. Ph.D. Thesis, Eastern Michigan University, Ypsilanti, MI,
USA, 2014.
Sustainability 2020, 12, 6426 23 of 26
6. Albar, A.M.; Hoque, R. Factors affecting cloud ERP adoption in Saudi Arabia: An empirical study. Inf. Dev.
2017, 35, 150–164. [CrossRef]
7. Al-Shboul, M.A. Towards better understanding of determinants logistical factors in SMEs for cloud ERP
adoption in developing economies. Bus. Process. Manag. J. 2019, 25, 887–907. [CrossRef]
8. Gangwar, H.; Date, H.; Ramaswamy, R. Understanding determinants of cloud computing adoption using an
integrated TAM-TOE model. J. Enterp. Inf. Manag. 2015, 28, 107–130. [CrossRef]
9. Albar, A.M.; Hoque, R. Determinants of Cloud ERP Adoption in Saudi Arabia: An Empirical Study. In Proceedings
of the 2015 International Conference on Cloud Computing (ICCC), Riyadh, Saudi Arabia, 26–29 April 2015;
Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2015; pp. 1–4.
10. Lee, Y.-H.; Hsu, P.; Chang, Y.-W.; Cheng, Y.-S. Integrating TRA and toe Frameworks for Cloud ERP Switching
Intention by Taiwanese Company. In Proceedings of the PACIS, Chiayi, Taiwan, 27 June–1 July 2016; PACIS:
Budapest, Hungary, 2016; pp. 139–148.
11. Usman, U.M.Z.; Ahmad, M.N.; Zakaria, N.H. The determinants of adoption of cloud-based erp of Nigerian’s
SMES manufacturing sector using toe framework and doi theory. Int. J. Enterp. Inf. Syst. 2019, 15, 27–43.
[CrossRef]
12. Qian, L.Y.; Baharudin, A.S.; Kanaan-Jebna, A. Factors affecting the adoption of enterprise resource planning
(ERP) on cloud among small and medium enterprises (SMES) in Penang, Malaysia. J. Appl. Inf. Technol. 2016,
88, 398–409.
13. Valdebenito, J.; Quelopana, A. Conceptual Model for Software as a Service (SaaS) Enterprise Resource Planning
(ERP) Systems Adoption in Small and Medium Sized Enterprises (SMEs) Using the Technology-OrganizationEnvironment (T-O-E) Framework. In Proceedings of the Advances in Intelligent Systems and Computing; Springer
Science and Business Media LLC: Cham, Switzerland, 2019; pp. 143–152.
14. Oliveira, T.; Thomas, M.; Espadanal, M. Assessing the determinants of cloud computing adoption: An analysis
of the manufacturing and services sectors. Inf. Manag. 2014, 51, 497–510. [CrossRef]
15. Salum, K.H.; Rozan, M.Z.A. Exploring the challenge impacted SMES to adopt cloud ERP. Indian J. Sci. Technol.
2016, 9, 1–9. [CrossRef]
16. Low, C.; Chen, Y.; Wu, M. Understanding the determinants of cloud computing adoption. Ind. Manag. Data
Syst. 2011, 111, 1006–1023. [CrossRef]
17. Premkumar, G. A meta-analysis of research on information technology implementation in small business.
J. Organ. Comput. Electron. Commer. 2003, 13, 91–121. [CrossRef]
18. Premkumar, G.; Ramamurthy, K. The role of interorganizational and organizational factors on the decision
mode for adoption of interorganizational systems. Decis. Sci. 1995, 26, 303–336. [CrossRef]
19. Elbeltagi, I.; Al Sharji, Y.; Hardaker, G.; Elsetouhi, A. The role of the owner-manager in SMEs’ adoption of
information and communication technology in the United Arab Emirates. J. Glob. Inf. Manag. 2013, 21, 23–50.
[CrossRef]
20. Jeyaraj, A.; Rottman, J.W.; Lacity, M.C. A review of the predictors, linkages, and biases in IT innovation
adoption research. J. Inf. Technol. 2006, 21, 1–23. [CrossRef]
21. Peng, G.C.A.; Gala, C. Cloud erp: A new dilemma to modern organisations? J. Comput. Inf. Syst. 2014, 54,
22–30. [CrossRef]
22. Faasen, J.; Seymour, L.F.; Schuler, J. SaaS ERP Adoption Intent: Explaining the South African SME Perspective.
In Lecture Notes in Business Information Processing; Springer Science and Business Media LLC: Berlin/Heidelberg,
Germany, 2013; Volume 139, pp. 35–47.
23. Demi, S.; Haddara, M. Do cloud ERP systems retire? An ERP lifecycle perspective. Procedia Comput. Sci.
2018, 138, 587–594. [CrossRef]
24. Lim, J.S.; Oh, J.I. A study on the effect of the introduction characteristics of cloud computing services on the
performance expectancy and the intention to use: From the perspective of the innovation diffusion theory.
Asia Pac. J. Inf. Syst. 2012, 22, 100–124.
25. Mijaˇc, M.; Picek, R.; Stapi´c, Z. Cloud ERP System Customization Challenges. In Proceedings of the 24th
Central European Conference on Information and Intelligent Systems, Varazdin, Croatia, 18–20 September
2013; pp. 132–140.
26. Lee, K.-P. Where would you build your big data analytics platform?—Comparative analysis between cloud
and on-premise. J. Res. Methodol. 2018, 3, 101–117. [CrossRef]
Sustainability 2020, 12, 6426 24 of 26
27. Elbahri, F.M.; Al-Sanjary, O.I.; Ali, M.A.M.; Naif, Z.A.; Ibrahim, O.A.; Mohammed, M.N. Difference
comparison of SAP, Oracle, and Microsoft solutions based on cloud ERP systems: A review. In Proceedings
of the 2019 IEEE 15th International Colloquium on Signal Processing & Its Applications (CSPA), Penang,
Malaysia, Malaysia, 8–9 March 2019; pp. 65–70. [CrossRef]
28. Grabski, S.V.; Leech, S.A.; Schmidt, P.J. A review of ERP research: A future agenda for accounting information
systems. J. Inf. Syst. 2011, 25, 37–78. [CrossRef]
29. Arnesen, S. Is a cloud ERP solution right for you? Strateg. Finance 2013, 94, 45–50.
30. Mezghani, K. Switching toward cloud ERP. Int. J. Enterp. Inf. Syst. 2014, 10, 46–61. [CrossRef]
31. Gupta, S.; Meissonier, R.; Drave, V.A.; Roubaud, D. Examining the impact of cloud ERP on sustainable
performance: A dynamic capability view. Int. J. Inf. Manag. 2020, 51, 102028. [CrossRef]
32. Ngai, E.W.; Law, C.; Wat, F. Examining the critical success factors in the adoption of enterprise resource
planning. Comput. Ind. 2008, 59, 548–564. [CrossRef]
33. Bharadwaj, S.S.; Lal, P.; Lal, P. Exploring the Impact of Cloud Computing Adoption on Organizational
Flexibility: A Client Perspective. In Proceedings of the 2012 International Conference on Cloud Computing
Technologies, Applications and Management (ICCCTAM), Dubai, UAE, 8–10 December 2012; Institute of
Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA; pp. 121–131.
34. Bellamy, M.C. Cloud Computing in the Large Scale Organisation Potential Benefits and Overcoming Barriers
to Deployment. Ph.D. Thesis, Imperial College London, London, UK, 2014.
35. Chang, Y.-W. What drives organizations to switch to cloud ERP systems? The impacts of enablers and
inhibitors. J. Enterp. Inf. Manag. 2020, 33, 600–626. [CrossRef]
36. Sahin, N.Y. Cloud ERP Security: Guidelines for Evaluation. Master’s Thesis, Stockholm University, Stockholm,
Sweden, 2013.
37. Weng, F. Competition and challenge on adopting cloud ERP. Int. J. Innov. Manag. Technol. 2014, 5, 309–313.
[CrossRef]
38. Johansson, B.; Alajbegovic, A.; Alexopoulo, V.; Desalermos, A. Cloud ERP Adoption Opportunities and
Concerns: The Role of Organizational Size. In Proceedings of the 2015 48th Hawaii International Conference
on System Sciences, Kauai, HI, USA, 5–8 January 2015; Institute of Electrical and Electronics Engineers
(IEEE): Piscataway, NJ, USA, 2015; pp. 4211–4219.
39. Ongowarsito, H.; Ekawati, A.D. Cloud EPR Adoption Factors in Large Companies. In Proceedings of
the 2019 International Conference on Information Management and Technology (ICIMTech), Jakarta/Bali,
Indonesia, 19–20 August 2019; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA,
2019; Volume 1, pp. 230–233.
40. Tornatzky, L.G.; Fleischer, M.; Chakrabarti, A.K. Processes of Technological Innovation; Lexington Books:
Lanham, MD, USA, 1990.
41. Priyadarshinee, P.; Raut, R.D.; Jha, M.K.; Gardas, B.B. Understanding and predicting the determinants of
cloud computing adoption: A two staged hybrid SEM—Neural networks approach. Comput. Hum. Behav.
2017, 76, 341–362. [CrossRef]
42. Sohaib, O.; Naderpour, M. Decision Making on Adoption of Cloud Computing in E-commerce Using Fuzzy
Topsis. In Proceedings of the 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), Naples,
Italy, 9–12 July 2017; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2017;
pp. 1–6.
43. Juiz, C.; Gómez, B.; Bermejo, B.; Cordero, D.; Mory, A. A Guide for Cascading and Scaling up Green IT Governance
Indicators through Balanced Scorecards: The Case of Data center Consolidation; Springer Science and Business
Media LLC: Berlin/Heidelberg, Germany, 2019; pp. 133–142.
44. Rogers, E.M. Innovation in organizations. In Diffusion of Inovations; The Free Press: New York, NY, USA,
1995; Volume 4.
45. Tornatzky, L.G.; Klein, K.J. Innovation characteristics and innovation adoption-implementation: A metaanalysis of findings. IEEE Trans. Eng. Manag. 1982, 28–45. [CrossRef]
46. Shin, W.; Ahn, H. Effects of innovation characteristics of cloud computing services, technostress on innovation
resistance and acceptance intention: Focused on public sector. Knowl. Manag. Res. 2019, 20, 59–86. [CrossRef]
47. Ram, S. A model of Innovation Resistance. In NA—Advances in Consumer Research; Melanie, W., Paul, A., Eds.;
Association for Consumer Research: Provo, UT, USA, 1987; Volume 14, pp. 208–212.
Sustainability 2020, 12, 6426 25 of 26
48. Ram, S. Successful innovation using strategies to reduce consumer resistance an empirical test. J. Prod. Innov.
Manag. 1989, 6, 20–34. [CrossRef]
49. Yoo, P.-H.; Lee, S.-H. A study on the innovation resistance of consumers in adoption process of new
product-concentrated on innovation resistance model. Korean Manag. Rev. 1994, 23, 32.
50. Benlian, A.; Hess, T. Opportunities and risks of software-as-a-service: Findings from a survey of IT executives.
Decis. Support Syst. 2011, 52, 232–246. [CrossRef]
51. Kim, S. The Effect of Consumers’ Innovation Resistance to FinTech Service on Intention to Recommend.
Ph.D. Thesis, Kangwon National University, Gangwon-do, Korea, 2018.
52. Marston, S.; Li, Z.; Bandyopadhyay, S.; Zhang, J.; Ghalsasi, A. Cloud computing—The business perspective.
Decis. Support Syst. 2011, 51, 176–189. [CrossRef]
53. Lutovac, M.; Manojlov, D. The successful methodology for enterprise resource planning (ERP) implementation.
J. Mod. Acc. Audit. 2012, 8, 1838–1847.
54. Abdullah, N.H.; Shamsuddin, A.; Wahab, E.; Hamid, N.A. Preliminary qualitative findings on technology
adoption of Malaysian SMEs. In Proceedings of the 2012 IEEE Colloquium on Humanities, Science and
Engineering (CHUSER), Kota Kinabalu, Sabah, Malaysia, 3–4 December 2012; Institute of Electrical and
Electronics Engineers (IEEE): Piscataway, NJ, USA, 2012; pp. 15–20.
55. Duan, Y.; Mullins, R.; Hamblin, D.; Stanek, S.; Sroka, H.; Cruz-Machado, V.; Araújo, J.P. Addressing ICTs
skill challenges in SMEs: Insights from three country investigations. J. Eur. Ind. Train. 2002, 26, 430–441.
[CrossRef]
56. Al-Jabri, I.; Roztocki, N. Adoption of ERP systems: Does information transparency matter? Telemat. Inform.
2015, 32, 300–310. [CrossRef]
57. Ram, J.; Corkindale, D.; Wu, M.-L. Implementation critical success factors (CSFs) for ERP: Do they contribute
to implementation success and post-implementation performance? Int. J. Prod. Econ. 2013, 144, 157–174.
[CrossRef]
58. Romm, T.; Pliskin, N.; Weber, Y.; Lee, A.S. Identifying organizational culture clash in MIS implementation.
Inf. Manag. 1991, 21, 99–109. [CrossRef]
59. Son, I.; Lee, D. Assessing A New IT Service Model, Cloud Computing. In Proceedings of the PACIS, Brisbane,
Australia, 7–11 July 2011; PACIS: Budapest, Hungary, 2011; pp. 179–188.
60. Jones, M.C.; Cline, M.; Ryan, S.D. Exploring knowledge sharing in ERP implementation: An organizational
culture framework. Decis. Support Syst. 2006, 41, 411–434. [CrossRef]
61. Ke, W.; Wei, K.K. Organizational culture and leadership in ERP implementation. Decis. Support Syst. 2008,
45, 208–218. [CrossRef]
62. Nah, F.F.-H.; Zuckweiler, K.M.; Lau, J.L.-S. ERP implementation: Chief information officers’ perceptions of
critical success factors. Int. J. Hum. Comput. Interact. 2003, 16, 5–22. [CrossRef]
63. Holland, C.P.; Light, B. A critical success factors model for ERP implementation. IEEE Softw. 1999, 16, 30–36.
[CrossRef]
64. Saeed, I.; Juell-Skielse, G.; Uppström, E. Cloud enterprise resource planning adoption. Adv. Enterp. Inf. Syst.
2012, 429–434. [CrossRef]
65. Lechesa, M.; Seymour, L.F.; Schuler, J. ERP Software as Service (SaaS): Factors Affecting Adoption in
South Africa. In Lecture Notes in Business Information Processing; Springer Science and Business Media LLC:
Berlin/Heidelberg, Germany, 2012; Volume 105, pp. 152–167.
66. Appandairajan, P.; Khan, N.Z.A.; Madiajagan, M. ERP on Cloud: Implementation Strategies and Challenges.
In Proceedings of the 2012 International Conference on Cloud Computing Technologies, Applications and
Management (ICCCTAM), Dubai, UAE, 8–10 December 2012; Institute of Electrical and Electronics Engineers
(IEEE): Piscataway, NJ, USA, 2012; pp. 56–59.
67. Amini, M.; Sadat Safavi, N.; Mirzaeyan Bahnamiri, R.; Mirzaei Omran, M.; Amini, M. Development of an
instrument for assessing the impact of environmental context on adoption of cloud computing for small and
medium enterprises. Aust. J. Basic Appl. Sci. 2014, 8, 129–135.
68. Li, Y.-H. An empirical investigation on the determinants of e-procurement adoption in Chinese manufacturing
enterprises. In Proceedings of the 2008 International Conference on Management Science and Engineering
15th Annual Conference Proceedings, Long Beach, CA, USA, 10–12 September 2008; Institute of Electrical
and Electronics Engineers (IEEE): Piscataway, NJ, USA, 2008; pp. 32–37.
Sustainability 2020, 12, 6426 26 of 26
69. Fan, Y.-W.; Chen, C.-D.; Wu, C.-C.; Fang, Y.-H. The effect of status quo bias on cloud system adoption.
J. Comput. Inf. Syst. 2015, 55, 55–64. [CrossRef]
70. Rogers, E.M. Elements of diffusion. In Diffusion of Innovations; The Free Press: New York, NY, USA, 2003; p. 5.
71. Premkumar, G.; Roberts, M. Adoption of new information technologies in rural small businesses. Omega
1999, 27, 467–484. [CrossRef]
72. Gupta, S.; Misra, S.C.; Singh, A.; Kumar, V.; Kumar, U. Identification of challenges and their ranking in the
implementation of cloud ERP. Int. J. Qual. Reliab. Manag. 2017, 34, 1056–1072. [CrossRef]
73. Salim, S.A.; Sedera, D.; Sawang, S.E.; Alarifi, A.H.; Atapattu, M. Moving from evaluation to trial: How do
SMEs start adopting cloud ERP? Australas. J. Inf. Syst. 2015, 19. [CrossRef]
74. Martins, C.B.; Steil, A.V.; Todesco, J.L. Factors influencing the adoption of the internet as a teaching tool at
foreign language schools. Comput. Educ. 2004, 42, 353–374. [CrossRef]
75. Fichman, R.G.; Kemerer, C.F. Adoption of software engineering process innovations: The case of object
orientation. Sloan Manag. Rev. 1993, 34, 7.
76. Peng, G.C.; Nunes, M.B. Surfacing ERP exploitation risks through a risk ontology. Ind. Manag. Data Syst.
2009, 109, 926–942. [CrossRef]
77. Saa, P.; Moscoso-Zea,O.; Costales,A.C.; Luján-Mora, S.Data Security Issues in Cloud-Based Software-as-a-Service
ERP. In Proceedings of the 2017 12th Iberian Conference on Information Systems and Technologies (CISTI),
Lisbon, Portugal, 14–17 June 2017; Institute of Electrical and Electronics Engineers (IEEE): Piscataway, NJ,
USA, 2017; pp. 1–7.
78. Pan, K.; Nunes, M.B.; Peng, G.C. Risks affecting ERP post-implementation. J. Manuf. Technol. Manag. 2011,
22, 107–130. [CrossRef]
79. Dutta, A.; Peng, G.C.A.; Choudhary, A. risks in enterprise cloud computing: The perspective of it experts.
J. Comput. Inf. Syst. 2013, 53, 39–48. [CrossRef]
80. Eliasson, L.; Berg, S.; Sandberg, D. Some aspects on the more efficient use of wood in the industrial
manufacture of single-fammily timber houses. Pro Ligno 2015, 11, 418–425.
81. Opara-Martins, J. A Decision Framework to Mitigate Vendor Lock-in Risks in Cloud (SaaS Category)
Migration. Ph.D. Thesis, Bournemouth University, Poole, UK, 2017.
82. Kim, W.; Kim, S.D.; Lee, E.; Lee, S. Adoption Issues for Cloud Computing. In Proceedings of the 7th International
Conference on Advanced in Mobile Computing and Multimedia, Kuala Lumpur, Malaysia, 14–16 December
2009; Association for Computing Machinery (ACM): New York, NY, USA, 2009; pp. 2–5.
83. Ali, A.Q.; Sultan, A.B.M.; Ghani, A.A.A.; Zulzalil, H. Empirical studies on the impact of software customization
on quality attributes: A systematic review. J. Theor. Appl. Inf. Technol. 2019, 97, 1747–1763.
84. Kwahk, K.Y. Statistical Data Analysis with SPSS; Chungram: Seoul, Korea, 2019.
85. Song, J. SPSS/AMOS Statistical Analysis Method Required for Thesis Preparation; 21c Book: Paju, Korea, 2015.
‘ 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
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