@techreport{Kollinger2003Analyzing,
abstract = {The paper analyzes factors that influence the adoption of e-learning and gives an example of how to forecast technology adoption based on a post-hoc predictive segmentation using a classification and regression tree (CART). We find strong evidence for the existence of technological interdependencies and organizational learning effects. Furthermore, we find different paths to e-learning adoption. The results of the analysis suggest a growing ?digital divide? among firms.
We use cross-sectional data from a European survey about e-business in June 2002, covering almost 6,000 enterprises in 15 industry sectors and 4 countries. Comparing the predictive quality of CART, we find that CART outperforms a traditional logistic regression. The results are more parsimo-nious, i. e. CARTs use less explanatory variables, better interpretable since different paths of adoption are detected, and from a statistical standpoint, because interactions between the covariates are taken into account.},
address = {Berlin},
author = {Philipp K\"{o}llinger and Christian Schade},
copyright = {http://www.econstor.eu/dspace/Nutzungsbedingungen},
keywords = {L29; C14; O30; 330; Technology Adoption; Path Dependence; Interaction Between Different Technologies; Regression Trees; Predictive Segmentation; Logistic Regression; Computergest\"{u}tztes Lernen; Betriebliche Bildungsarbeit; E-Business; Innovationsdiffusion; Sch\"{a}tzung; EU-Staaten},
language = {eng},
number = {346},
publisher = {Deutsches Institut f\"{u}r Wirtschaftsforschung (DIW)},
title = {Analyzing E-Learning Adoption via Recursive Partitioning},
type = {DIW-Diskussionspapiere},
url = {http://hdl.handle.net/10419/18082},
year = {2003}
}
