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Time Of Technology Adoption And Learning By Using

Author

Listed:
  • Bruce Mcwilliams
  • David Zilbermanfr

Abstract

Learning by using is introduced into an adoption model to explain why larger and more educated firms adopt earlier. Dynamic economies of scale arise in learning by using that speed up adoption. The empirical estimation of time of adoption using Tobit analysis integrates the concepts of adoption and diffusion, allowing the diffusion of the technology to be derived from the time of adoption analysis. In addition, by introducing heterogeneity among the adopters, Tobit analysis is shown to provide superior results to the traditional logit and probit analysis of the dichotomous adopt/not adopt variable.

Suggested Citation

  • Bruce Mcwilliams & David Zilbermanfr, 1996. "Time Of Technology Adoption And Learning By Using," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 4(2), pages 139-154.
    • Handle: RePEc:taf:ecinnt:v:4:y:1996:i:2:p:139-154
    DOI: 10.1080/10438599600000005
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    References listed on IDEAS

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    Cited by:

    1. Federico Castillo & J. Keith Gilless & Amir Heiman & David Zilberman, 2018. "Time of adoption and intensity of technology transfer: an institutional analysis of offices of technology transfer in the United States," The Journal of Technology Transfer, Springer, vol. 43(1), pages 120-138, February.
    2. Jorge Niosi & Petr Hanel & Susan Reid, 2012. "The international diffusion of biotechnology: the arrival of developing countries," Journal of Evolutionary Economics, Springer, vol. 22(4), pages 767-783, September.
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    8. Michael Euler & Stefan Schwarze & Hermanto Siregar & Matin Qaim, 2016. "Oil Palm Expansion among Smallholder Farmers in Sumatra, Indonesia," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(3), pages 658-676, September.
    9. Zhang, Cen & Schmöcker, Jan-Dirk & Kuwahara, Masahiro & Nakamura, Toshiyuki & Uno, Nobuhiro, 2020. "A diffusion model for estimating adoption patterns of a one-way carsharing system in its initial years," Transportation Research Part A: Policy and Practice, Elsevier, vol. 136(C), pages 135-150.
    10. Blanca Isabel Sánchez-Toledano & Zein Kallas & Oscar Palmeros Rojas & José M. Gil, 2018. "Determinant Factors of the Adoption of Improved Maize Seeds in Southern Mexico: A Survival Analysis Approach," Sustainability, MDPI, vol. 10(10), pages 1-22, October.
    11. Torben Schubert, 2024. "Captive Offshoring, Innovation and Market Diffusion: Evidence from the Swedish Community Innovation Survey," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 15(2), pages 5650-5678, June.
    12. David Zilberman & Amir Heiman, 1997. "The Value of Economic Research," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 79(5), pages 1539-1544.
    13. Sauer, Johannes & Zilberman, David, 2009. "Innovation behaviour at micro level - selection and identification," CUDARE Working Papers 120636, University of California, Berkeley, Department of Agricultural and Resource Economics.
    14. Vu Linh Toan Le & Tien Hoang Nguyen & Khanh Duy Pham, 2023. "What Drives Industry 4.0 Technologies Adoption? Evidence from a SEM-Neural Network Approach in the Context of Vietnamese Firms," Sustainability, MDPI, vol. 15(7), pages 1-32, March.
    15. Bourke, Jane & Roper, Stephen, 2014. "The influence of experiential learning on medical equipment adoption in general practices," Health Policy, Elsevier, vol. 118(1), pages 37-47.
    16. Ira Matuschke & Matin Qaim, 2008. "Seed Market Privatisation and Farmers’ Access to Crop Technologies: The Case of Hybrid Pearl Millet Adoption in India," Journal of Agricultural Economics, Wiley Blackwell, vol. 59(3), pages 498-515, September.
    17. Jane Bourke & Stephen Roper, 2012. "In with the new: the determinants of prescribing innovation by general practitioners in Ireland," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 13(4), pages 393-407, August.
    18. Bourke, Jane & Roper, Stephen, 2016. "AMT adoption and innovation: An investigation of dynamic and complementary effects," Technovation, Elsevier, vol. 55, pages 42-55.
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    More about this item

    Keywords

    Time of adoption; learning-by-using; tobit; diffusion J.E.L. Codes: 033; C24;
    All these keywords.

    JEL classification:

    • C24 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Truncated and Censored Models; Switching Regression Models; Threshold Regression Models

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