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Analyzing E-Learning Adoption via Recursive Partitioning

  • Philipp Köllinger
  • Christian Schade

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 elearning 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 parsimonious, 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.

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File URL: http://www.diw.de/documents/publikationen/73/diw_01.c.40405.de/dp346.pdf
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Paper provided by DIW Berlin, German Institute for Economic Research in its series Discussion Papers of DIW Berlin with number 346.

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Length: 39 p.
Date of creation: 2003
Date of revision:
Handle: RePEc:diw:diwwpp:dp346
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  1. Dosi, Giovanni, 1993. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 22(2), pages 102-103, April.
  2. Fudenberg, Drew & Tirole, Jean, 1985. "Preemption and Rent Equilization in the Adoption of New Technology," Review of Economic Studies, Wiley Blackwell, vol. 52(3), pages 383-401, July.
  3. William D. Nordhaus, 2001. "Productivity Growth and the New Economy," NBER Working Papers 8096, National Bureau of Economic Research, Inc.
  4. Reinganum, Jennifer F, 1981. "On the Diffusion of New Technology: A Game Theoretic Approach," Review of Economic Studies, Wiley Blackwell, vol. 48(3), pages 395-405, July.
  5. Stoneman, Paul & Kwon, Myung-Joong, 1994. "The Diffusion of Multiple Process Technologies," Economic Journal, Royal Economic Society, vol. 104(423), pages 420-31, March.
  6. Erik Brynjolfsson & Lorin M. Hitt, 2003. "Computing Productivity: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 793-808, November.
  7. Colombo, Massimo G & Mosconi, Rocco, 1995. "Complementarity and Cumulative Learning Effects in the Early Diffusion of Multiple Technologies," Journal of Industrial Economics, Wiley Blackwell, vol. 43(1), pages 13-48, March.
  8. Church, J. & Gandal, N., 1991. "Complementary Network Externalities and Technological Adoption," Papers 5-91, Tel Aviv.
  9. Georg Götz, 1999. "Monopolistic Competition and the Diffusion of New Technology," RAND Journal of Economics, The RAND Corporation, vol. 30(4), pages 679-693, Winter.
  10. Dale W. Jorgenson, 2001. "Information Technology and the U.S. Economy," American Economic Review, American Economic Association, vol. 91(1), pages 1-32, March.
  11. Dale W. Jorgenson, 2001. "Information Technology and the U. S. Economy," Harvard Institute of Economic Research Working Papers 1911, Harvard - Institute of Economic Research.
  12. Jensen, Richard, 1982. "Adoption and diffusion of an innovation of uncertain profitability," Journal of Economic Theory, Elsevier, vol. 27(1), pages 182-193, June.
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