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What drives organizations to

switch to cloud ERP systems? The

impacts of enablers and inhibitors

Yu-Wei ChangDepartment of Business Management,National Taichung University of Science and Technology, Taichung, Taiwan

AbstractPurpose – Switching to public cloud enterprise resource planning (ERP) systems not only provides financialand functional benefits to organizations, but also results in sunk costs of incumbent systems and uncertaintycosts of cloud systems. The purpose of this study is to investigate the enablers and inhibitors concerningswitching to cloud ERP systems at the organizational level.Design/methodology/approach – Data were collected from 212 top managers and owners of the enterprisesin 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 systemsand satisfaction with and breadth of use of incumbent ERP systems are found to be the predictors of switchingcosts. Switching benefits positively affect switching intention, but switching costs negatively affect switchingintention.Research limitations/implications – This study develops a theoretical model grounded in a set oftheoretical foundations, including two-factor theory, technology-organization-environment (TOE) framework,information systems (IS) success model, and expectation confirmation theory (ECT). Two-factor theory is usedto characterize switching benefits and costs that affect switching intention. Technological factors come from ISsuccess model, and the factors affecting benefits are organized based on TOE framework. Sunk costs ofincumbent ERP systems are developed based on ECT.Originality/value – Different from previous studies on cloud computing adoption, this study providesinsights into switching intention to cloud computing. The study also proposes an integrated model grounded inmultiple perspectives to explain organizations’ decisions to switch to cloud ERP systems. These findings helpcloud 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 theoryPaper type Research paper

1. IntroductionIn recent years, cloud computing has become a common phrase in daily life. Cloud computingprovides a foundation on which to develop new business products, services, and solutionsover the Internet (Gen, 2008). Cloud services developed for organizations can be furthercategorized as public, private, and hybrid clouds. According to the 2019 cloud report, 94percent of organizations use clouds; of these, 91 percent use public clouds, and 72 percent useprivate clouds (Flexera, 2019). According to a recent Gartner survey, the public cloud servicesmarket reached 182 billion in 2018. Gartner also forecasts that the global public cloudservices market is expected to grow to 214 billion in 2019, with a growth rate of 17 percent(Gartner, 2019). This prediction indicates that several organizations are increasing theirinvestment 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 customerrelationship management (CRM) (Chang et al., 2019). ERP systems are important to mostorganizations because the systems are used to manage core business processes, includingfinancial accounting, management accounting, human resources, manufacturing, and order

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The current issue and full text archive of this journal is available on Emerald Insight at:emerald/insight/1741-0398.htm

Received 11 July 2019Revised 25 September 20195 December 2019Accepted 28 December 2019

Journal of Enterprise InformationManagementVol. 33 No. 3, 2020pp. 600-© Emerald Publishing Limited1741-DOI 10/JEIM-06-2019-

processing. Therefore, cloud ERP systems are a common application of public clouds inorganizations (Low et al., 2011). As with the development of public clouds, some of the world’sleading software suppliers, like SAP, Oracle, and Microsoft, offer cloud ERP systems for theircustomers. Recently, public cloud ERP systems have received increasing attention because of theirapparent financial and functional benefits. Organizations are driven to adopt cloud ERPsystems by the benefits, including low start-up cost, pay only for utilized systems, up-to-datefunctionalities, and rapid deployment (Kenyon, 2012). An increasing number of studies oncloud computing have identified adoption factors of cloud ERP systems, such as theavailability and characteristics of the technologies, financial resources, technical competence,industry pressure, and government policy (Lee et al., 2013; Low et al., 2011; Paquetteet al., 2010). However, most existing organizations currently implement on-premise ERP systems. Aninvestigation of the adoption of new information systems (IS) may not accurately explain thedilemma faced by organizations, because switching to cloud ERP systems means giving upincumbent ERP systems. Sunk costs of incumbent ERP systems and uncertainty costs ofcloud 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 adoptionintention, as the factors of adopting new IS and giving up incumbent IS should be accountedfor. Therefore, it is important to gain an understanding of what factors motivate/demotivateorganizations 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 addressedswitching issues to cloud computing at the organizational level (Fan et al., 2015; Lucia-Palacios et al., 2016). Because these studies did not consider switching intention as thedependent variable and did not highlight the enablers and inhibitors concerning switching topublic clouds, it may be difficult for top managers and owners of enterprises to determinewhat factors will benefit organizations and negatively impact organizations. Accordingly, itis important to understand the factors associated with switching decisions, so thatorganizations 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 ofswitching benefits and costs on switching intention. Certain IS studies applied two-factortheory to investigate two categories of factors that influence system adoption (Hachicha andMezghani, 2018; Kang, 2018; Wu et al., 2019; Park and Ryoo, 2013). Thus, we characterize thefactors as motivators (i. switching benefits) and demotivators (i. switching costs).However, switching benefits and costs are too generic, so two-factor theory must bestrengthened with other models or external variables (Hachicha and Mezghani, 2018).Because technology-organization-environment (TOE) framework is suitable for studyingcloud computing adoption from technological, organizational, and environmentalperspectives (Gangwar et al., 2015; Gutierrez et al., 2015; Senyo and Effah, 2016), this studyintegrates two-factor theory with TOE framework. The factors that affect benefits areorganized based on TOE framework, and the factors that affect costs are sunk costs ofincumbent ERP systems and uncertainty costs of cloud ERP systems. Some relevant factorsare developed based on expectation confirmation theory (ECT) and information systems (IS)success model. Therefore, the research model grounded in multiple theories can helpinvestigate the factors that influence organizations’ switching to cloud ERP systems. This study examines the effects of the enablers and inhibitors on switching intention tocloud ERP systems. We confirm that switching benefits and costs independently andsimultaneously 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

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 anduniversities. The factors influencing the personal cloud adoption of mobile services and e-invoiceare also examined (Park and Kim, 2014; Lian, 2015; Schepman et al., 2012). In the results of theirresearch, the usability, interfaces, and functionalities of cloud computing are found to influenceusers to adopt cloud computing. Enterprise cloud systems include cloud ERP systems, cloud CRM, and cloud humancapital management (HCM) (Lee et al., 2013; Low et al., 2011). In addition to technologicalfactors, organizational and environmental factors should be considered to examine cloudcomputing adoption by enterprises or small and medium-sized enterprises (SMEs) (Guptaet al., 2013). Thus, TOE framework is used as the main theoretical foundation of cloudcomputing adoption because the framework considers the effects of technological,organizational, and environmental factors on IS innovation adoption. TOE factors havebeen verified to be the determinants of organizations’ adoption of cloud systems (Gangwaret al., 2015; Gutierrez et al., 2015; Senyo and Effah, 2016).

2 Cloud computing enablers and inhibitorsThe advantages of adopting cloud computing can be broadly categorized into threedimensions: deployment, financial, and functional (Geczy et al., 2012). First, the deployment ofcloud systems is similar to outsourcing, and most cloud systems are modularized. Comparedwith traditional IT deployment, the deployment of cloud systems for organizations is easyand 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 andhardware and software infrastructures by deploying cloud systems (Gen, 2009). Finally,cloud systems offer the latest functionalities such that cloud service providers will keep theirsystems 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 ofmeasuring the effects of the two dimensions, this study investigates the effect of systemquality as a whole. Structured payments, pay for use, and cost savings are financialadvantages of using cloud systems. Financial advantage can stem from organizationalcontexts, and system quality can originate from technological contexts. TOE framework isadopted in this study to apply a unified framework to group-related factors (Tornatzky andFleisher, 1990). Although cloud computing offers many advantages, risks and obstacles inhibitorganizations 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. Publiccloud computing poses essential security risks because organizations access control to dataand utilized systems over the Internet (Geczy et al., 2012). Many organizations have concernsrelated 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 switchingprocess in IS adoption research (Hong et al., 2008; Kim and Kankanhalli, 2009). Therefore, thisstudy views perceived risk of cloud systems as switching costs. However, satisfaction with and breadth of use of incumbent ERP systems are consideredobstacles to adopting cloud systems (Park and Ryoo, 2013; Ye et al., 2008). Both factors can beviewed as sunk costs, which refer to a previous commitment, that is, economic cost, learningcost, customer habit, emotional cost, or cognitive effort (Hong et al., 2008; Kim andKankanhalli, 2009). Satisfaction is related to emotional cost and customer habit, and breadthof use is related to learning cost and cognitive effort (Park and Ryoo, 2013; Ye et al., 2008).

Enablers and inhibitors of cloud ERP switching

603

2 Two-factor theoryTwo-factor theory proposed by Herzberg (1959) categorizes the factors that influenceemployee job satisfaction into motivators and demotivators (hygiene factors). Motivators arerelated to job satisfaction, such as advancement, recognition, responsibility, andachievement. Employees feel satisfied because of motivators. On the other hand, hygienefactors are related to job dissatisfaction, such as supervision, pay, company polices, andworking conditions (Robbins and Judge, 2007). The existence of hygiene factors results indissatisfaction, while lack of hygiene factors does not lead to job satisfaction. The two factorsare not the opposite of one another. In other words, the two factors could simultaneouslyinfluence employees’ work motivations. Recently, two-factor theory has been used to investigate the adoption and acceptance ofcloud 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 andservices, 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 orreject cloud applications as the evaluation of psychological benefits and costs. Psychologicalbenefits refer to the perceived utility gained from new IS, while psychological costs refer tothe loss of incumbent IS and the uncertainty of new IS. Based on the same concept, switching benefits are viewed as motivators since thepresence of switching benefits leads to organizations’ switching intention to cloud ERPsystems. Switching costs are viewed as demotivators since the existence of switching costscould hinder switching intention, but the absence of switching costs does not necessarilyresult in switching intention. Switching benefits and costs are not opposing factors sinceorganizations can simultaneously hold perceptions of motivators and demotivators.Therefore, this study uses two-factor theory to investigate the effects of switching benefitsand costs on switching intention to cloud ERP systems.

2 Technology-organization-environment frameworkSeveral empirical studies have proposed that individual and organizational benefits arefurther 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 befactors in the success of data warehousing (Wixom and Watson, 2001). According to ISsuccess model, system quality and information quality also affect organizational benefits(DeLone and McLean, 2003). Colleague opinion is found to have a positive effect on switchingbenefits when investigating user intention to change to new IS (Kim and Kankanhalli, 2009).Accordingly, factors such as data quality, system quality, information quality, and colleagueopinion 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 becategorized as technological, organizational, and environmental (Tornatzky and Fleisher,1990). TOE framework, including the three contexts, has been used to investigate factors thatdrive 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 two-factor theory and TOE framework. Technological contexts refer to technological characteristics that enhance perceivedbenefits of cloud ERP systems. One of the most famous models explaining the technologicalfactors that influence organizational benefits is DeLone and McLean IS success model, whichposits that system quality and information quality can enhance organizational benefits. Themodel has been widely adopted to explain the effects of IS on organizational benefits (Ifinedoet al., 2010; Lin, 2010; Tsai et al., 2012). Therefore, system quality and information quality areincluded in the technological contexts as the predictors of switching benefits.

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ConstructReference

Benefit-related

Cost-related

Bhattacherjeeand Park (2014)

Systemquality

Informationquality

Financialadvantage

Governmentsupport

Industrypressure

Switchingbenefits

Perceivedrisk

Satisfactionwith incumbentsystems

Breadthof use

Switchingcosts

Fan

et al.

(2015)

þ

þ

−

−

−

Hsu and Lin(2016)

þ

þ

−

Gen (2008, 2009)

þ

þ

−

Gutierrez

et al.

(2015)

þ

þ

Lucia-Palacios et al.

(2016)

þ

−

−

Oliveira

et al.

(2014)

þ

þ

þ

−

Note(s)

: Dependent variable: Cloud computing adoption;

þ

represents positive relationship;

represents negative relationship

Table II.Factors influencingcloud computingadoption

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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 thequality of general systems (DeLone and McLean, 2003). Additionally, the technologicalattributes of cloud systems, including easy-/fast-to-deploy and up-to-date functionalities, areconsidered in this study (Benlian et al., 2011–2012; Ifinedo et al., 2010). Organizations use on-demand cloud systems without investing in cloud IT infrastructures; thus, they can easilyand rapidly deploy cloud ERP systems. In addition, maintenances and up-grade activities aremanaged by cloud service providers. Thus, the abovementioned system quality is perceivedto influence the benefits of using cloud ERP systems. Information quality in this study refersto the characteristics of the output provided by cloud ERP systems, and measures includesuch dimensions as completeness, understandability, relevance, and security (DeLone andMcLean, 2003). With the mobility of cloud computing, organizations can access data on cloudsystems using mobile devices anytime, anywhere. Organizations focus on the accuracy ofinformation provided by cloud ERP systems because information quality is related toworkforce collaboration, productivity, and efficiency. According to Kim and Kankanhalli (2009), the construct of switching benefits is adaptedfrom perceived usefulness. Increases in system quality and information quality driveincreases in usefulness in IS contexts (Seddon and Kiew, 1996). Positive associations havebeen 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 systemquality and information quality of cloud ERP systems lead to their perceived utility. Thisstudy 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 ERPsystems, including structured payments, pay for use, and cost savings (Gen, 2009). Cloud ERPsystems offer potential benefits that increase the flexibility of IT investments and decrease costsin organizations (Geczy et al., 2012). Organizations do not need to invest significant financialresources in their IT infrastructures because cloud service providers maintain and manage cloudERP systems, and this further reduces IT investment costs (Gangwar et al., 2015). The pay-as-you-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 (Wanget 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. Thisstudy hypothesizes the following:

H5. Financial advantage is positively related to switching benefits.

Government support is recognized as a critical environmental factor that affects innovationadoption. Government support in this study refers to the assistance provided by an authorityto encourage the spread of cloud computing in businesses. Governments can provide fundingto encourage organizations to adopt cloud systems. Governments can also help cloud serviceproviders decrease management costs by improving cloud IT infrastructures or establishingcommon 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 encourageorganizations 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 ofusing cloud ERP systems. Thus, we expect that government support positively enhances theperception of the cloud ERP utility for organizations.

Enablers and inhibitors of cloud ERP switching

607

of breadth of use can be explained by the concept of sunk costs, that is, time and effort spent inlearning the feature applications (Park and Ryoo, 2013; Ye et al., 2008). When switching tocloud 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 ERPsystems, 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 studyhypothesizes the following:

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. Methodology4 Data collectionThis study worked with Chunghwa Telecom (CHT) to investigate organizations’ switchingintention to cloud ERP systems because CHT is Taiwan’s largest telecom company and has ahigher market share in cloud computing. The cloud ERP systems are run on the server anddatabase provided by CHT. The cloud ERP systems include modules for sales and distribution,material management, product planning, and financial accounting; the systems alloworganizations 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 savemore the costs of IT infrastructures, implementation, and maintenance than on-premise ERPsystems, and provided software upgrades for the cloud ERP systems at any time. The targeted population was top managers and owners of enterprises because they heldthe authority to decide whether to switch to new ERP systems. In reaching out to the targetedrespondents, CHT contacted Taiwan’s top managers and owners of enterprises by phone and

Organizational Context

Environmental Context

Technological Context System Quality

Information Quality

Financial Advantage

Government Support

Industry Pressure

Perceive Risk

Satisfaction

Breadth of Use

Switching Benefits

Switching Costs

Switching Intention

H

H H

H

H

HH

H

H

H

Enablers

Inhibiters

Figure 1.Research model

Enablers and inhibitors of cloud ERP switching

609

email and invited them to participate in its marketing activities. The goal of these eightmarketing activities was to promote and introduce the cloud ERP systems and solutions forenterprise customers, and even encourage these customers to purchase SaaS ERP licenses.After participating in marketing activities, the participants learned about the advantages anddisadvantages of adopting cloud ERP systems and received enough knowledge to determinewhether to switch from incumbent ERP systems to cloud ERP systems. In these marketingactivities, we also explained the purpose of the study and ensured that the respondents’questionnaires would be kept confidential. Finally, this study administered questionnaires toparticipants and promised to provide a summary of the survey results in exchange forparticipation. This study used a convenience sampling approach to collect data. In order to reduce sampleerror, we ensured that the suitable samples should be top managers and owners of enterprises,and distributed approximately 500 questionnaires externally to respondents who participated inmarketing activities. The data were collected over the period of July to August 2014. A total of212 questionnaires were returned, with a response rate of 42 percent, which was higher than theaverage response rate of 10–15 percent of external surveys (Lindemann, 2018). Most respondentswere male (64 percent), in the 36–40 age group (36 percent), with a university degree (62.percent), and worked in a strategy department (26 percent). Most organizations weredistributed in the industries of electronics and electrical equipment (39 percent). Employees inthe sample primarily ranged from 1 to 10 (34 percent), and the capital of the organizations wasless than NT$ 1 million (39 percent). Table AI lists the respondents’ demographics.

4 Instrument developmentIn this study, all the survey items for 11 constructs in the questionnaire were taken from aprior literature review and modified to fit the context of cloud computing. Items for systemquality and information quality were adapted from DeLone and McLean (2003) and Benlianet al. (2011–2012). Items for financial advantage were adapted from Yao et al. (2007) and Maet al. (2005). Items for government support were adapted from Tan and Teo (2000). Items forindustry pressure were adapted from Kuan and Chau (2001). Items for perceived risk wereadapted 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 benefitsand switching costs were adapted from Kim and Kankanhalli (2009). Items for switchingintention were adapted from Venkatesh et al. (2003). The 36 items were developed for thequestionnaire (see Table AII) and measured by a seven-point Likert scale ranging from“strongly disagree” (1) to “strongly agree” (7).

4 Data analysisA partial least squares structural equation modeling (PLS-SEM) approach is suggested forconducting data analyses when the structural model with many constructs or indicators iscomplex, 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 partialleast squares (PLS) analysis to test measurement and structural models. The assessment ofthe measurement model includes item reliability, convergent validity, and discriminantvalidity, while the assessment of the structural model includes path coefficients and thestatistical significance of hypothesized relationships.

5. Results5 Common method biasTo assess common method bias, Harman’s one-factor test is examined using a principalcomponent analysis. If a single construct accounts for more than 50 percent of the variance,

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Construct Item Factor loading Mean S. CR Cronbach’s alpha AVE

Note(s): The italic values are the squared root of AVE

Table III.Reliability

Table IV.Inter-constructcorrelations

• System quality SQ1 0 5 0 0 0 0.
  • SQ2 0.
  • SQ3 0.
  • SQ4 0.
• Information quality IQ1 0 5 0 0 0 0.
  • IQ2 0.
  • IQ3 0.
  • IQ4 0.
• Financial advantage FA1 0 5 1 0 0 0.
  • FA2 0.
  • FA3 0.
• Government support GS1 0 5 1 0 0 0.
  • GS2 0.
  • GS3 0.
• Industry pressure ID1 0 4 1 0 0 0.
  • ID2 0.
  • ID3 0.
• Perceived risk PR1 0 3 1 0 0 0.
  • PR2 0.
  • PR3 0.
• Satisfaction US1 0 3 1 0 0 0.
  • US2 0.
  • US3 0.
• Breadth of use BOU1 0 3 1 0 0 0.
  • BOU2 0.
• Switching benefits SB1 0 5 1 0 0 0.
  • SB2 0.
  • SB3 0.
  • SB4 0.
• Switching costs SC1 0 3 1 0 0 0.
  • SC2 0.
  • SC3 0.
• Switching intention SI1 0 4 1 0 0 0.
  • SI2 0.
  • SI3 0.
• SQ 0. SQ IQ FA GS IP PR SA BOU SB SC SI
• IQ 0 0.
• FA 0 0 0.
• GS 0 0 0 0.
• IP 0 0 0 0 0.
• PR 0 0 0 0 0 0.
• SA 0 0 0 0 0 0 0.
• BOU 0 0 0 0 0 0 0 0.
• SB 0 0 0 0 0 0 0 0 0.
• SC 0 0 0 0 0 0 0 0 0 0.
• SI 0 0 0 0 0 0 0 0 0 0 0.
• 33, JEIM

dependent variable of previous research mainly focused on cloud computing adoption ratherthan switching intention, so few studies considered switching decisions from incumbentsystems to cloud systems. However, top managers and owners of the enterprises must makedecisions between incumbent and cloud ERP systems based on enablers and inhibitors, suchas benefits of cloud ERP systems, sunk costs of incumbent ERP systems, and uncertaintycosts of cloud ERP systems. The critical factors have been addressed in our developedresearch model grounded in multiple theories. In technological contexts, system quality is an influential factor in switching benefits. Thisfinding 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 organizationalbenefits. Therefore, good system quality of cloud ERP systems increases the organization’sexpected benefits of switching to new systems. Although previous research has shown thatinformation quality is another factor in organizational benefits (Ifinedo et al., 2010; Lin, 2010; Tsaiet al., 2012), the results show that information quality is not found to significantly affect switchingbenefits. One possible explanation is that ERP systems are known for collecting and

Organizational Context

Environmental Context

Technological Context System Quality

Information Quality

Financial Advantage

Government Support

Industry Pressure

Perceive Risk

Satisfaction

Breadth of Use

Switching Benefits

Switching Costs

Switching Intention

0*

• 0. 0***

0***

0* 0***

0***

0***

• 0*

R 2 = 67%

R 2 = 32%

R 2 = 42% *p < 0, **p < 0, ***p < 0.

Hypothesis Path Path coefficient Assessment (p<0)

H1 SB – SI 0*** SupportedH2 SC – SI 0* SupportedH3 SQ – SB 0* SupportedH4 IQ – SB 0 n.H5 FA – SB 0*** SupportedH6 GS – SB 0 n.H7 IP – SB 0*** SupportedH8 PR – SC 0* SupportedH9 SA – SC 0*** SupportedH10 BOU – SC 0*** SupportedNote(s): *p < 0, **p < 0, ***p < 0.

Figure 2. Results

Table V.Path coefficients andsignificance

Enablers and inhibitors of cloud ERP switching

613

Previous studies

Theory

Enabler

Inhibitor

Dependen

t variable

Safari

et al.

(2015)

TOE framework

(1) Relative advantage(2) Compatibility(3) Security andprivacy(4) Competitivepressure(5) Social influence

(1) Complexity

SaaS adoption

Oliveira

et al.

(2014)

DOI and TOE framework

(1) Relative advantage(2) Compatibility(3) Cost saving(4) Competitivepressure(5) Regulatory support

(1) Security concerns(2) Complexity

Cloud computing adoption

This study

TOE framework, two-factor theory, IS successmodel, and ECT

(1) Switching benefits(2) System quality(3) Information quality(4) Financial advantage(5) Governmentsupport(6) Industry pressure

(1) Switching costs(2) Perceived risk of cloudsystems(3) Satisfaction with incumbentsystems(4) Breadth of use of incumbentsystems

Switching intention to cloudcomputing

Table VI.

Enablers and inhibitors of cloud ERP switching

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disseminating integrated data in real time (Wylie, 1990). Both on-premise and on-demand ERPsystems provide high-quality information in this respect. Thus, organizations do not view high-quality information as a strong factor that increases switching benefits of cloud ERP systems. In organizational contexts, the results confirm that financial advantage of using cloudERP systems is an important determinant of switching benefits. The significance of financialadvantage is in line with previous research (Gangwar et al., 2015; Safari et al., 2015), whichpointed out that the advantages associated with cloud computing are based on pay-per-usemodels. 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 of4, which implies that organizations have perceived government support. Previous studiesproposed that organizations with government support are more likely to increase the benefitsof 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 notfound to significantly affect switching benefits. One possible reason for this result is that theTaiwan government tends to support cloud technology by promoting and buildinggovernment clouds and improving the infrastructures for networking. Although the Taiwangovernment plans to invest 24 billion in cloud computing over five years (NationalDevelopment Council, 2012), these measures might provide long-term benefits rather thanimmediately tangible benefits to organizations. As a result, although the score forgovernment support is high, it does not significantly affect switching benefits of cloudERP systems. On the other hand, this study indicates that industry pressure is a significantdeterminant 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 oncloud computing adoption. The finding indicates that organizations are forced to adopt cloudERP systems by trading partners and business competitors in the competitive environment. The results show that perceived risk, satisfaction, and breadth of use are importantdeterminants of switching costs. Prior studies pointed out that sunk costs of giving upincumbent IS increase switching costs (Bhattacherjee and Park, 2014; Fan et al., 2015; Lucia-Palacios et al., 2016), while security risks of using new IS enhance switching costs (Martinset al., 2019; Sabi et al., 2016). According to our findings, satisfaction with and breadth of use ofincumbent ERP systems cause organizations to lose previous efforts and costs, which furtherreduce their switching intention to cloud ERP systems. Cloud ERP systems bring securityrisks and uncertainties, which further lead organizations to retain incumbent ERP systemsand hinder their switching intention to new systems.

7. Implications for theory and practice7 Implications for theorySeveral 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 issuesto cloud computing (Fan et al., 2015; Lucia-Palacios et al., 2016). Specifically, no previous workhas empirically examined switching intention as a dependent variable and focused on theenablers and inhibitors that influence switching intention at the organizational level. Giventhe nature of the dependent variable (i. switching intention), this study aims to determine thefactors that influence organizations’ switching intention to cloud ERP systems and integratesmultiple theories to address switching issues. Since two-factor theory is applicable to simultaneously evaluating users’ perceptions ofmotivators and demotivators, this study introduces this theory as the main theoreticalfoundation for investigating switching benefits and costs. Because organizations can makecomprehensive decisions about IS innovation by considering technological, organizational,

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that their information might be disclosed to others. Cloud service providers should makedetailed efforts to explain their protection policies and convince customers of their datasecurity, such as server authentication, database authorization, and data encryption. Cloudservice providers should also invest resources to build better security protocols andmechanisms (e. secure socket layer (SSL) technology) to ensure security and privacy. Thehighest possible safety standards and government regulations should be observed toalleviate customers’ security concerns. Although satisfaction with and breadth of use of incumbent ERP systems have a largereffect on switching costs, it is difficult to directly reduce such costs for cloud serviceproviders. Because cloud service providers cannot directly change organizationalsatisfaction with incumbent ERP systems, the only action that they can take is to enhancecustomer satisfaction with their cloud systems. Cloud service providers should strive toimprove their products to ensure that organizations are satisfied. A greater breadth of use increases the barrier to organizational switching and makes itmore difficult for cloud ERP systems to lure away existing organizations. For organizationsthat use the features and options of incumbent ERP systems, this result suggests that cloudservice providers should allow organization a trial use, which, in turn, could changeorganizational uses, experiences, and habits. Cloud service providers can also provideeffective training courses and customize cloud ERP systems based on organizations’ needs sothat cloud ERP systems are easy to use and effectively implemented in organizations.

8. Limitations and future researchOur research model is grounded in two-factor theory, which characterizes switching benefitsand costs as motivators and demotivators. This theory can model switching benefits andcosts of system usage and open up many research opportunities. First, the model can befurther extended to investigate factors that enhance switching benefits. The factors areorganized based on TOE framework in this study, but other theoretical perspectives, such asTAM, DOI, and TRA, can also be incorporated in the future. Second, we found directassociations between the factors of perceived risk, satisfaction, and breadth of use andswitching costs. Perceived risk was originally studied in e-commerce contexts. This studyshows that the factor also increases switching costs in public clouds. Further studies can bedevoted to investigating the role of risks in private clouds, which are accessed throughInternet owned by organizations. The effects of satisfaction with and breadth of use wereoriginally studied in personal applications. This study shows that the two factors alsoincrease switching costs of public clouds in enterprise applications. Further studies caninvestigate whether the same factors increase the costs of adopting private clouds. This study contains certain limitations that require further examination and additionalresearch. First, a bias related to self-reported scales might exist in this study. Second, theresults based on 2014 data may be constrained by the age of the data. Caution must to betaken when generalizing the results to today’s research. Third, the empirical data for ourstudy were collected from organizations in Taiwan, and thus, the generalizability of theresults may be limited. The results may differ due to different countries or regions. Fourth, thecloud systems being investigated in the study were from a single software supplier. Thefindings may vary due to different suppliers. Fifth, because the data were cross-sectional andnot longitudinal, the posited causal relationships can only be inferred rather than proven.Finally, although it has been demonstrated that behavioral intention leads to actual usebehavior, this study stopped at switching intention.

9. ConclusionsThis study aims to investigate the enablers and inhibitors that influence organizations’switching intention from incumbent ERP systems to cloud ERP systems. From an academic

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perspective, we provide the empirical evidence regarding switching to public cloudcomputing. This study develops a theoretical model by integrating two-factor theory, TOEframework, IS success model, and ECT. Two-factor theory is used to characterize switchingbenefits and costs that affect switching intention as motivators and demotivators. Thefactors that influence switching benefits are organized based on TOE framework, and thefactors that influence switching costs are the uncertainties inherent in cloud systems and thebenefits of the deployed on-premise ERP systems. From a practical perspective, our findingssuggest that cloud service providers can enhance switching benefits by enhancingtechnological (system quality) and organizational (financial advantage) factors. Thepresentation of environmental factors (industry pressure) is also helpful. Cloud serviceproviders should also try to reduce the perceived risk of using cloud ERP systems becauseperceived risk is one of the factors that contributes to switching costs. Satisfaction withand breadth of use of incumbent ERP systems are also major factors that increase switchingcosts. Cloud service providers can target organizations that are not satisfied withtheir incumbent ERP systems or deploy a limited number of modules of incumbent ERPsystems.

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