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http://hdl.handle.net/10419/18082
  

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dc.contributor.authorKöllinger, Philippen_US
dc.contributor.authorSchade, Christianen_US
dc.date.accessioned2009-01-28T15:39:15Z-
dc.date.available2009-01-28T15:39:15Z-
dc.date.issued2003en_US
dc.identifier.urihttp://hdl.handle.net/10419/18082-
dc.description.abstractThe 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.en_US
dc.language.isoengen_US
dc.publisherDeutsches Institut für Wirtschaftsforschung (DIW) Berlinen_US
dc.relation.ispartofseriesDIW-Diskussionspapiere 346en_US
dc.subject.jelL29en_US
dc.subject.jelC14en_US
dc.subject.jelO30en_US
dc.subject.ddc330en_US
dc.subject.keywordTechnology Adoptionen_US
dc.subject.keywordPath Dependenceen_US
dc.subject.keywordInteraction Between Different Technologiesen_US
dc.subject.keywordRegression Treesen_US
dc.subject.keywordPredictive Segmentationen_US
dc.subject.keywordLogistic Regressionen_US
dc.subject.stwComputergestütztes Lernenen_US
dc.subject.stwBetriebliche Bildungsarbeiten_US
dc.subject.stwE-Businessen_US
dc.subject.stwInnovationsdiffusionen_US
dc.subject.stwSchätzungen_US
dc.subject.stwEU-Staatenen_US
dc.titleAnalyzing E-Learning Adoption via Recursive Partitioningen_US
dc.typeWorking Paperen_US
dc.identifier.ppn371860725en_US
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungen-
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Publikationen von Forscherinnen und Forschern des DIW

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