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Quantitative empirical trends in technical performance

Citations

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Cited by:

  1. 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.
  2. 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).
  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. 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.
  5. 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.
  6. Mario Coccia & Saeed Roshani, 2025. "Path-Breaking Directions in Quantum Computing Technology: A Patent Analysis with Multiple Techniques," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(1), pages 4991-5024, March.
  7. Mario Coccia, 2019. "Technological Parasitism," Papers 1901.09073, arXiv.org.
  8. 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).
  9. Bello, Saheed & Reiner, David, 2025. "Experience curve analyses for green hydrogen technology development," Technological Forecasting and Social Change, Elsevier, vol. 220(C).
  10. 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.
  11. 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.
  12. 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.
  13. 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.
  14. 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.
  15. 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).
  16. 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).
  17. Salvador Pueyo, 2019. "Limits to green growth and the dynamics of innovation," Papers 1904.09586, arXiv.org, revised May 2019.
  18. 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.
  19. 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.
  20. 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.
  21. Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
  22. 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.
  23. 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.
  24. 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).
  25. 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.
  26. 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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