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http://dspace.mediu.edu.my:8181/xmlui/handle/10419/18082Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.creator | Köllinger, Philipp | - |
| dc.creator | Schade, Christian | - |
| dc.date | 2003 | - |
| dc.date.accessioned | 2013-10-16T06:58:11Z | - |
| dc.date.available | 2013-10-16T06:58:11Z | - |
| dc.date.issued | 2013-10-16 | - |
| dc.identifier | http://hdl.handle.net/10419/18082 | - |
| dc.identifier | ppn:371860725 | - |
| dc.identifier.uri | http://koha.mediu.edu.my:8181/xmlui/handle/10419/18082 | - |
| dc.description | 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. | - |
| dc.language | eng | - |
| dc.publisher | Deutsches Institut für Wirtschaftsforschung (DIW) Berlin | - |
| dc.relation | DIW-Diskussionspapiere 346 | - |
| dc.rights | http://www.econstor.eu/dspace/Nutzungsbedingungen | - |
| dc.subject | L29 | - |
| dc.subject | C14 | - |
| dc.subject | O30 | - |
| dc.subject | ddc:330 | - |
| dc.subject | Technology Adoption | - |
| dc.subject | Path Dependence | - |
| dc.subject | Interaction Between Different Technologies | - |
| dc.subject | Regression Trees | - |
| dc.subject | Predictive Segmentation | - |
| dc.subject | Logistic Regression | - |
| dc.subject | Computergestütztes Lernen | - |
| dc.subject | Betriebliche Bildungsarbeit | - |
| dc.subject | E-Business | - |
| dc.subject | Innovationsdiffusion | - |
| dc.subject | Schätzung | - |
| dc.subject | EU-Staaten | - |
| dc.title | Analyzing E-Learning Adoption via Recursive Partitioning | - |
| dc.type | doc-type:workingPaper | - |
| Appears in Collections: | EconStor | |
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