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A Nonsequential R & D Search Model

Author

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  • Tom K. Lee

    (University of California, San Diego)

Abstract

Managers of research and development (R&D) programs are often confronted with questions of what determines R&D spending (i.e. R&D search intensity) and when R&D spending is unprofitable (i.e. Reservation Technology Level). Three determinants of R&D spending are identified: the current technology level, the length of decision horizon, and basic research. Comparative statics of the effects of these determinants of R&D spending are discussed. The higher the current technology level, the less intensive is R&D search because, ceterus paribus, the lower the probability of finding a better technology. The longer the decision horizon, the more intensive is R&D search because fruitful R&D results can be applied to more time periods and generate more present value profits. Finally, technological opportunities can be improved via basic research. The improvement of technological opportunities in turn spurs R&D search. Existence of a Reservation Technology Level (RTL) is established. It constitutes a stopping rule for R&D search. Whenever the current technology level is greater than or equal to RTL, R&D search stops; otherwise R&D search continues. One interesting property of RTL is that it is strictly less than the highest possible technology level. Thus the economic limit of R&D (RTL) differs from the technological limit of R&D. Ex ante, an R&D program manager should not press for the technological limit of R&D once the current technology level exceeds or equals RTL. However, ex post, one may end up with a technology level greater than RTL. Moreover, R&D search may be resumed in an abandoned R&D area if (i) interest rate declines, (ii) R&D quasi-fixed cost reduces, or (iii) basic research provides a favorable shape-preserving shift of the distribution of technological opportunities.

Suggested Citation

  • Tom K. Lee, 1982. "A Nonsequential R & D Search Model," Management Science, INFORMS, vol. 28(8), pages 900-909, August.
  • Handle: RePEc:inm:ormnsc:v:28:y:1982:i:8:p:900-909
    DOI: 10.1287/mnsc.28.8.900
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    Cited by:

    1. Gray, Richard S. & Malla, Stavroula & Tran, Kien C., 2003. "An Empirical Analysis Of Public And Private Spillovers Within The Canola Biotech Industry," 2003 Annual meeting, July 27-30, Montreal, Canada 22137, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    2. Scott A. Shane & Karl T. Ulrich, 2004. "50th Anniversary Article: Technological Innovation, Product Development, and Entrepreneurship in Management Science," Management Science, INFORMS, vol. 50(2), pages 133-144, February.
    3. Berninghaus, Siegfried & Völker, Rainer, 1986. "Optimale Innovationspolitik bei unvollkommener Information," Discussion Papers, Series I 226, University of Konstanz, Department of Economics.
    4. Gray, Richard & Malla, Stavroula, 2007. "Research Spillovers What They Are and Why They Matter for Policy," CAIRN Policy Briefs 273075, Canadian Agricultural Innovation and Regulation Network (CAIRN).
    5. Murad Antia & Christos Pantzalis & Jung Chul Park, 2021. "Does CEO myopia impede growth opportunities?," Review of Quantitative Finance and Accounting, Springer, vol. 56(4), pages 1503-1535, May.

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