Location

Hilton Waikoloa Village, Hawaii

Event Website

http://www.hicss.hawaii.edu

Start Date

1-4-2017

End Date

1-7-2017

Description

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.

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Jan 4th, 12:00 AM Jan 7th, 12:00 AM

Prediction of Propensity for Enterprise Cloud Computing Adoption

Hilton Waikoloa Village, Hawaii

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.

http://aisel.aisnet.org/hicss-50/os/enterprise_system_integration/3