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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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    7. 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.
    8. Love, James H. & Roper, Stephen & Vahter, Priit, 2023. "The dynamics of abandoned innovation activities: Learning from failure or learning to prevent failure?," Research Policy, Elsevier, vol. 52(8).
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    14. Park, Timothy A. & Lohr, Luanne, 2001. "Management Strategies And Human Capital Investments By Organic Producers," 2001 Annual meeting, August 5-8, Chicago, IL 20708, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
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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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