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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. 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. 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).
    3. 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.
    4. Matthias Niggli & Christian Rutzer, 2023. "Digital technologies, technological improvement rates, and innovations “Made in Switzerland”," Swiss Journal of Economics and Statistics, Springer;Swiss Society of Economics and Statistics, vol. 159(1), pages 1-31, December.
    5. 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.
    6. 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).
    7. Salvador Pueyo, 2019. "Limits to green growth and the dynamics of innovation," Papers 1904.09586, arXiv.org, revised May 2019.
    8. 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.
    9. 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.
    10. 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.
    11. Park, Inchae & Triulzi, Giorgio & Magee, Christopher L., 2022. "Tracing the emergence of new technology: A comparative analysis of five technological domains," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    12. 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.
    13. Subarna Basnet & Christopher L Magee, 2017. "Artifact interactions retard technological improvement: An empirical study," PLOS ONE, Public Library of Science, vol. 12(8), pages 1-17, August.
    14. Mario Coccia, 2019. "Technological Parasitism," Papers 1901.09073, arXiv.org.
    15. Li, Yanan & Lin, Jun & Qian, Yanjun & Li, Dehong, 2023. "Feed-in tariff policy for biomass power generation: Incorporating the feedstock acquisition process," European Journal of Operational Research, Elsevier, vol. 304(3), pages 1113-1132.
    16. 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.
    17. 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.
    18. 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).
    19. Annapoornima M. Subramanian & Moren Lévesque & Vareska van de Vrande, 2020. "“Pulling the Plug:” Time Allocation between Drug Discovery and Development Projects," Production and Operations Management, Production and Operations Management Society, vol. 29(12), pages 2851-2876, December.
    20. Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
    21. 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.
    22. Puccetti, Giovanni & Giordano, Vito & Spada, Irene & Chiarello, Filippo & Fantoni, Gualtiero, 2023. "Technology identification from patent texts: A novel named entity recognition method," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    23. Hugo Confraria & Vitor Hugo Ferreira & Manuel Mira Godinho, 2021. "Emerging 21st Century technologies: Is Europe still falling behind?," Working Papers REM 2021/0188, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
    24. 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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