Although cloud computing has a number of benefits, privacy risks play a critical role in organizations’ decisions to switch to cloud services. However, few studies on cloud adoption have investigated effects of both benefits and costs on switching intention. Based on cost-benefit analysis and technology acceptance model, this study develops a research model to investigate how benefits (perceived usefulness and perceived ease of use) and costs (perceived risk and privacy concerns) influence organizations’ switching intention to cloud Enterprise Resource Planning (ERP). The model also accounts for trust and perceived control in the context of cloud computing. Perceived usefulness, perceived ease of use, and privacy concerns significantly affect switching intention. Trust can enhance perceived usefulness and perceived ease of use and reduce perceived risk. Perceived control can also reduce perceived risk and privacy concerns. The findings are useful to understand switching issues from traditional ERP to cloud ERP for both researchers and practitioners.
AgagGEl-MasryAA (2016) Understanding consumer intention to participate in online travel community and effects on consumer intention to purchase travel online and WOM. Computers in Human Behavior60(C): 97–111.
3.
BentlerPBonnettD (1980) Significance tests and goodness-of-fit in the analysis of covariance structures. Psychological Bulletin88(3): 588–606.
4.
BrowneMWCudeckR (1993) Alternative ways of assessing model fit. In: BollenKALongJS (Eds.), Testing Structural Equation Models. Newbury Park: Sage Publications Inc, pp. 136–162.
5.
ChangYWHsuPYShiauWL (2014) An empirical study of managers’ usage intention in BI. Cognition Technology & Work16(2): 247–258.
6.
DavisFD (1986) A technology acceptance model for empirically testing new end–user information systems: theory and results. Dissertation, Sloan School of Management, Massachusetts Institute of Technology.
7.
DavisFDBagozziRPWarshawPR (1989) User acceptance of computer technology: a comparison of two theoretical models. Management Science35(8): 982–1003.
8.
DinevTBellottoMHartPRussoVSerraIColauttiC (2006) Privacy calculus model in e–commerce – a study of Italy and the United States. European Journal of Information Systems15, 389–402.
9.
DinevTHartP (2006) An extended privacy calculus model for e–commerce transactions. Information Systems Research17(1): 61–80.
10.
FishbeinMAjzenI (1975) Belief, attitude, intentions, and behavior: an introduction to theory and research. Boston: Addison-Wesley.
GéczyPIzumiNHasidaK (2012) Cloud sourcing: managing cloud adoption. Global Journal of Business Research6(2): 57–70.
13.
GefenDStraubDWBoudreauM (2000) Structural equation modeling techniques and regression: Guidelines for research practice. Communications of AIS4(7): 2–76.
14.
GefenDKarahannaEStraubDW (2003) Trust and TAM in online shopping: An integrated model. MIS Quarterly27(1): 51–90.
GenF (2009) New IDC IT cloud services survey: Top benefits and challengeshttp://blogs.idc.com/ie/?p=730(accessed 20 May 2016).
17.
HarmanHH (1976) Modern factor analysis. New York: University of Chicago Press.
18.
HeijdenHVerhagenTCreemersM (2003) Understanding online purchase intentions: contributions from technology and trust perspectives. European Journal of Information Systems12, 41–48.
19.
HerzbergFMausnerBSnydermanB (1959) The motivation to work. New York: Wiley.
20.
HuLBentlerPM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling A Multidisciplinary Journal6(1): 1–55.
21.
JarvenpaaSLTractinskyNVitaleM (2000) Consumer trust in an Internet store. Information Technology and Management1, 45–71
22.
KettingerWJLeeCC (1994) Perceived service quality and user satisfaction with the information services function. Decision Sciences25(5–6): 737–763.
23.
LanoisP (2010) Caught in the Clouds: the Web 2.0, Cloud Computing, and Privacy?Northwestern Journal of Technology and Intellectual Property9(2): 29–49.
24.
LedererALMaupinDJSenaMPZhuangY (2000) The technology acceptance model and the World Wide Web. Decision Support Systems29(3): 269–282.
25.
LeeSGGhaeSHChoKM (2013) Drivers and inhibitors of SaaS adoption in Korea. International Journal of Information Management33(3): 429–440.
26.
LiHGuptaAZhangJSarathyR (2014) Examining the decision to use standalone personal health record systems as a trust–enabled fair social contract. Decision Support Systems57(3): 376–386.
27.
LianJWYenDCWangYT (2014) An exploratory study to understand the critical factors affecting the decision to adopt cloud computing in Taiwan hospital. International Journal of Information Management34, 28–36.
28.
LiaoCLiuCCChenK (2011) Examining the impact of privacy, trust and risk perceptions beyond monetary transactions: An integrated model. Electronic Commerce Research and Applications10(6): 702–715.
29.
LowCYChenYHWuMC (2011) Understanding the determinants of cloud computing adoption. Industrial Management & Data Systems111(7): 1006–1023.
30.
MattilaASEnzCA (2002) The role of emotions in service encounters. Journal of Service Research4(4): 268–277.
NicolaouAIHarrison McKnightD (2006) Perceived information quality in data exchanges: effects on risk, trust, and intention to use. Information Systems Research17(4): 332–351.
33.
PaquetteSJaegerPTWilsonSC (2010) Identifying the security risks associated with governmental use of cloud computing. Government Information Quarterly27, 243–253.
34.
ParkSCRyooSY (2013) An empirical investigation of end–users’ switching toward cloud computing: A two factor theory perspective. Computers in Human Behavior29(1): 160–170.
35.
PavlouPA (2003) Consumer acceptance of electronic commerce: integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce7(3): 101–134
36.
PeeLGKankanhalliAOngLLVuMK (2010) Antecedents, and impact of knowledge management capability in public organizations. In 2010 Pacific Asia Conference on Information Systems. 713–724.
37.
RattenV (2016) Continuance use intention of cloud computing: Innovativeness and creativity perspectives. Journal of Business Research1737–1740
38.
ScottJE (1995) The measurement of information systems effectiveness: Evaluating a measuring instrument. Data Base Advances26(1): 43–61.
39.
SubashiniSKavithaV (2011) A survey of security issues in service delivery models of cloud computing. Journal of Network and Computer Applications34(1): 1–11.
The NIST Definition of Cloud Computing by Peter Mell and Timothy Grance, Special Publication 800–145 National Institute of Standards and Technology (NIST) an agency of the U.S. Department of Commerce under the Federal Information Security Management Act (FISMA) of 2002, Public Law, 107–347.
42.
TungFCChangSCChouCM (2008) An extension of trust and TAM model with IDT in the adoption of the electronic logistics information system in HIS in the medical industry. International Journal of Medical Informatics77, 324–335.
43.
VenkateshVMorrisM GDavisGBDavisFD (2003) User acceptance of information technology: toward a unified view. MIS Quarterly27(3): 425–478.
44.
VenkateshVDavisFD (2000) A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management Science46(2): 186–204.
45.
XuHDinevTSmithJHartP (2011) Information privacy concerns: linking individual perceptions with institutional privacy assurances. Journal of the Association for Information Systems12(12): 798–824.
46.
WelshMGregoryT (2012) Oracle JD Edwards Cloud Computing “Choosing a deployment strategy that fits”An Oracle White Paper.
47.
WuJHWangSC (2005) What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information & Management42, 719–729.
48.
ZhouT (2013) An empirical examination of user adoption of location–based services. Electronic Commerce Research13(1): 25–39.
