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Quantitative empirical trends in technical performance

Author

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  • Magee, C.L.
  • Basnet, S.
  • Funk, J.L.
  • Benson, C.L.

Abstract

Technological improvement trends such as Moore's law and experience curves have been widely used to understand how technologies change over time and to forecast the future through extrapolation. Such studies can also potentially provide a deeper understanding of R&D management and strategic issues associated with technical change. However, such uses of technical performance trends require further consideration of the relationships among possible independent variables — in particular between time and possible effort variables such as cumulative production, R&D spending, and patent production. The paper addresses this issue by analyzing performance trends and patent output over time for 28 technological domains. In addition to patent output, production and revenue data are analyzed for the integrated circuits domain. The key findings are:1.Sahal's equation is verified for additional effort variables (for patents and revenue in addition to cumulative production where it was first developed).2.Sahal's equation is quite accurate when all three relationships — (a) an exponential between performance and time, (b) an exponential between effort and time, (c) a power law between performance and the effort variable — have good data fits (r2>0.7).3.The power law and effort exponents determined are dependent upon the choice of effort variable but the time dependent exponent is not.4.All 28 domains have high quality fits (r2>0.7) between the log of performance and time whereas 9 domains have very low quality (r2<0.5) for power law fits with patents as the effort variable.5.Even with the highest quality fits (r2>0.9), the exponential relationship is not perfect and it is thus best to consider these relationships as the foundation upon which more complex (but nearly exponential) relationships are based.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:tefoso:v:104:y:2016:i:c:p:237-246
    DOI: 10.1016/j.techfore.2015.12.011
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    References listed on IDEAS

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    1. Farmer, J. Doyne & Lafond, François, 2016. "How predictable is technological progress?," Research Policy, Elsevier, vol. 45(3), pages 647-665.
    2. 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.
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    Cited by:

    1. Alexander Kott, 2020. "Toward universal laws of technology evolution: modeling multi-century advances in mobile direct-fire systems," The Journal of Defense Modeling and Simulation, , vol. 17(4), pages 373-388, October.
    2. 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.
    3. 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).
    4. Salvador Pueyo, 2019. "Limits to green growth and the dynamics of innovation," Papers 1904.09586, arXiv.org, revised May 2019.
    5. Changbae Mun & Sejun Yoon & Hyunseok Park, 2019. "Structural decomposition of technological domain using patent co-classification and classification hierarchy," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 633-652, November.
    6. Fang Han & Christopher L. Magee, 2018. "Testing the science/technology relationship by analysis of patent citations of scientific papers after decomposition of both science and technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 767-796, August.
    7. Coccia, Mario, 2019. "The theory of technological parasitism for the measurement of the evolution of technology and technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 141(C), pages 289-304.
    8. 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.
    9. Mario Coccia, 2019. "Technological Parasitism," Papers 1901.09073, arXiv.org.
    10. 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.
    11. Magee, Christopher L. & Devezas, Tessaleno C., 2017. "A simple extension of dematerialization theory: Incorporation of technical progress and the rebound effect," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 196-205.
    12. Feng, Sida & Magee, Christopher L., 2020. "Technological development of key domains in electric vehicles: Improvement rates, technology trajectories and key assignees," Applied Energy, Elsevier, vol. 260(C).
    13. Christopher L. Benson & Christopher L. Magee, 2018. "Data-Driven Investment Decision-Making: Applying Moore's Law and S-Curves to Business Strategies," Papers 1805.06339, arXiv.org.
    14. Zhang, Guanglu & McAdams, Daniel A. & Shankar, Venkatesh & Darani, Milad Mohammadi, 2017. "Modeling the evolution of system technology performance when component and system technology performances interact: Commensalism and amensalism," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 116-124.

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