Prediction of Propensity for Enterprise Cloud Computing Adoption Kyriakou, Niki Maragoudakis, Manolis Loukis, Euripidis Themistocleous, Marinos 2016-12-29T01:50:40Z 2016-12-29T01:50:40Z 2017-01-04
dc.description.abstract Cloud computing (CC) can offer significant benefits to enterprises. However, it can pose some risks as well, and this has led to lower adoption than the initial expectations. For this reason, it would be very useful to develop ‘predictive analytics’ in this area, enabling us to predict which enterprises will exhibit a propensity for CC adoption. In this direction, we investigate the use of six well-established classifiers (fast large margin Support Vector Machine, Naive Bayes, Decision Tree, Random Forest, k-Nearest Neighbor, and Linear Regression) for the prediction of enterprise level propensity for CC adoption. Having as our theoretical foundation the Technology – Organization – Environment (TOE) framework, we are using for this prediction of set of technological (concerning existing enterprise information systems), organizational and environmental characteristics. Our first results, using a dataset collected from 676 manufacturing firms of the glass, ceramic and cement sectors from six European countries (Germany, France, Italy, Poland, Spain, and UK) through the e-Business W@tch Survey of the European Commission, are encouraging. It is concluded that among the examined characteristics the technological ones, concerning the existing enterprise systems, seem to be the most important predictors.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2017.596
dc.identifier.isbn 978-0-9981331-0-2
dc.language.iso eng
dc.relation.ispartof Proceedings of the 50th Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.subject Adoption
dc.subject Cloud Computing
dc.subject ERP Systems
dc.title Prediction of Propensity for Enterprise Cloud Computing Adoption
dc.type Conference Paper
dc.type.dcmi Text
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