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Deutsches Institut für Wirtschaftsforschung (DIW), Berlin >
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http://hdl.handle.net/10419/18082
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| Title: | | Analyzing E-Learning Adoption via Recursive Partitioning  |
| Authors: | | Köllinger, Philipp Schade, Christian |
| Issue Date: | | 2003 |
| Series/Report no.: | | DIW-Diskussionspapiere 346 |
| 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. |
| Subjects: | | Technology Adoption Path Dependence Interaction Between Different Technologies Regression Trees Predictive Segmentation Logistic Regression |
| JEL: | | L29 C14 O30 |
| Document Type: | | Working Paper |
| Appears in Collections: | | Publikationen von Forscherinnen und Forschern des DIW DIW-Diskussionspapiere
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