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Title:Analyzing E-Learning Adoption via Recursive Partitioning PDF Logo
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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