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Analyzing E-Learning Adoption via Recursive Partitioning

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  • Philipp Köllinger
  • Christian Schade

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 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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Bibliographic Info

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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Related research

Keywords: Technology Adoption; Path Dependence; Interaction between Different Technologies; Regression Trees; Predictive Segmentation; Logistic Regression; E-Learning; E-Business;

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  1. Dosi, Giovanni, 1982. "Technological paradigms and technological trajectories : A suggested interpretation of the determinants and directions of technical change," Research Policy, Elsevier, vol. 11(3), pages 147-162, June.
  2. Dale W. Jorgenson, 2001. "Information Technology and the U. S. Economy," Harvard Institute of Economic Research Working Papers 1911, Harvard - Institute of Economic Research.
  3. Brynjolfsson, Erik & Hitt, Lorin M., 2004. "Computing Productivity: Firm-Level Evidence," Working papers 4210-01, Massachusetts Institute of Technology (MIT), Sloan School of Management.
  4. 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.
  5. Fudenberg, Drew & Tirole, Jean, 1985. "Preemption and Rent Equilization in the Adoption of New Technology," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 52(3), pages 383-401, July.
  6. William D. Nordhaus, 2002. "Productivity Growth and the New Economy," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 33(2), pages 211-265.
  7. Reinganum, Jennifer F, 1981. "On the Diffusion of New Technology: A Game Theoretic Approach," Review of Economic Studies, Wiley Blackwell, Wiley Blackwell, vol. 48(3), pages 395-405, July.
  8. Dale W. Jorgenson, 2001. "Information Technology and the U.S. Economy," American Economic Review, American Economic Association, vol. 91(1), pages 1-32, March.
  9. Stoneman, Paul & Kwon, Myung-Joong, 1994. "The Diffusion of Multiple Process Technologies," Economic Journal, Royal Economic Society, Royal Economic Society, vol. 104(423), pages 420-31, March.
  10. Church, J. & Gandal, N., 1991. "Complementary Network Externalities and Technological Adoption," Papers, Tel Aviv 5-91, Tel Aviv.
  11. 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.
  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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