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Rapid improvements with no commercial production: How do the improvements occur?

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  • Funk, Jeffrey L.
  • Magee, Christopher L.

Abstract

This paper empirically examines 13 technologies in which significant cost and performance improvements occurred even while no commercial production occurred. Since the literature emphasizes cost reductions through increases in cumulative production, this paper explores cost and performance improvements from a new perspective. The results demonstrate that learning in these pre-commercial production cases arises through mechanisms utilized in deliberate R&D efforts. We identity three mechanisms – materials creation, process changes, and reductions in feature scale – that enable these improvements to occur and use them to extend models of learning and invention. These mechanisms can also apply during post-commercial time periods and further research is needed to quantify the relative contributions of these three mechanisms and those of production-based learning in a variety of technologies.

Suggested Citation

  • Funk, Jeffrey L. & Magee, Christopher L., 2015. "Rapid improvements with no commercial production: How do the improvements occur?," Research Policy, Elsevier, vol. 44(3), pages 777-788.
    • Handle: RePEc:eee:respol:v:44:y:2015:i:3:p:777-788
    DOI: 10.1016/j.respol.2014.11.005
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    Cited by:

    1. Jeffrey Funk, 2018. "Technology change, economic feasibility, and creative destruction: the case of new electronic products and services," Industrial and Corporate Change, Oxford University Press and the Associazione ICC, vol. 27(1), pages 65-82.
    2. Nandakumar, Karthik & Funk, Jeffrey L., 2015. "Understanding the timing of economic feasibility: The case of input interfaces for human-computer interaction," Technology in Society, Elsevier, vol. 43(C), pages 33-49.
    3. JongRoul Woo & Christopher L. Magee, 2017. "Exploring the relationship between technological improvement and innovation diffusion: An empirical test," Papers 1704.03597, arXiv.org, revised May 2018.
    4. Singh, Anuraag & Triulzi, Giorgio & Magee, Christopher L., 2021. "Technological improvement rate predictions for all technologies: Use of patent data and an extended domain description," Research Policy, Elsevier, vol. 50(9).
    5. Lafond, François & Bailey, Aimee Gotway & Bakker, Jan David & Rebois, Dylan & Zadourian, Rubina & McSharry, Patrick & Farmer, J. Doyne, 2018. "How well do experience curves predict technological progress? A method for making distributional forecasts," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 104-117.
    6. Dosi, Giovanni & Grazzi, Marco & Mathew, Nanditha, 2017. "The cost-quantity relations and the diverse patterns of “learning by doing”: Evidence from India," Research Policy, Elsevier, vol. 46(10), pages 1873-1886.
    7. Anuraag Singh & Giorgio Triulzi & Christopher L. Magee, 2020. "Technological improvement rate estimates for all technologies: Use of patent data and an extended domain description," Papers 2004.13919, arXiv.org.
    8. Triulzi, Giorgio & Alstott, Jeff & Magee, Christopher L., 2020. "Estimating technology performance improvement rates by mining patent data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    9. Ben Klemens, 2021. "Attributing Value to Patents and Trademarks in Complex Production Chains," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 12(2), pages 842-875, June.
    10. Magee, C.L. & Basnet, S. & Funk, J.L. & Benson, C.L., 2016. "Quantitative empirical trends in technical performance," Technological Forecasting and Social Change, Elsevier, vol. 104(C), pages 237-246.

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