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Quantitative Determination of Technological Improvement from Patent Data

Citations

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Cited by:

  1. Higham, Kyle & de Rassenfosse, Gaétan & Jaffe, Adam B., 2021. "Patent Quality: Towards a Systematic Framework for Analysis and Measurement," Research Policy, Elsevier, vol. 50(4).
  2. Li, Yan & Zhang, Yiren & Hu, Jian & Wang, Zeyu, 2024. "Insight into the nexus between intellectual property pledge financing and enterprise innovation: A systematic analysis with multidimensional perspectives," International Review of Economics & Finance, Elsevier, vol. 93(PA), pages 700-719.
  3. Jeong, Yujin & Park, Inchae & Yoon, Byungun, 2019. "Identifying emerging Research and Business Development (R&BD) areas based on topic modeling and visualization with intellectual property right data," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 655-672.
  4. Mariam Barry & Giorgio Triulzi & Christopher L. Magee, 2017. "Food Productivity Trends from Hybrid Corn: Statistical Analysis of Patents and Field-test data," Papers 1706.05911, arXiv.org.
  5. Adam B. Jaffe & Gaétan de Rassenfosse, 2017. "Patent citation data in social science research: Overview and best practices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1360-1374, June.
  6. Verluise, Cyril & Cristelli, Gabriele & Higham, Kyle & de Rassenfosse, Gaetan, 2020. "The Missing 15 Percent of Patent Citations," SocArXiv x78ys_v1, Center for Open Science.
  7. Abeliansky, Ana L. & Martínez-Zarzoso, Imnaculada & Prettner, Klaus, 2015. "The impact of 3D printing on trade and FDI," University of Göttingen Working Papers in Economics 262, University of Goettingen, Department of Economics.
  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. Verluise, Cyril & Cristelli, Gabriele & Higham, Kyle & de Rassenfosse, Gaetan, 2020. "The Missing 15 Percent of Patent Citations," SocArXiv x78ys, Center for Open Science.
  10. 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).
  11. 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.
  12. Yoon, Byungun & Magee, Christopher L., 2018. "Exploring technology opportunities by visualizing patent information based on generative topographic mapping and link prediction," Technological Forecasting and Social Change, Elsevier, vol. 132(C), pages 105-117.
  13. Mariani, Manuel Sebastian & Medo, Matúš & Lafond, François, 2019. "Early identification of important patents: Design and validation of citation network metrics," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 644-654.
  14. Manuel Acosta & Daniel Coronado & Esther Ferrándiz & Manuel Jiménez, 2022. "Effects of knowledge spillovers between competitors on patent quality: what patent citations reveal about a global duopoly," The Journal of Technology Transfer, Springer, vol. 47(5), pages 1451-1487, October.
  15. 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.
  16. 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).
  17. Abeliansky, Ana Lucia & Martínez-Zarzoso, Inmaculada & Prettner, Klaus, 2020. "3D printing, international trade, and FDI," Economic Modelling, Elsevier, vol. 85(C), pages 288-306.
  18. Christopher L. Benson & Pranav D Sumanth & Alina P Colling, 2018. "A Quantitative Analysis of Possible Futures of Autonomous Transport," Papers 1806.01696, arXiv.org.
  19. 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).
  20. Mun, Changbae & Yoon, Sejun & Raghavan, Nagarajan & Hwang, Dongwook & Basnet, Subarna & Park, Hyunseok, 2021. "Function score-based technological trend analysis," Technovation, Elsevier, vol. 101(C).
  21. Flamand, Marina & Frigant, Vincent & Miollan, Stéphane & Dimitrova, Zlatina & Sauve, Henri, 2024. "Evaluating the TIS's knowledge production function using patent data: A multi-criteria approach applied to the technological bricks of the hydrogen storage," MPRA Paper 123050, University Library of Munich, Germany.
  22. Deyu Li & Floor Alkemade & Koen Frenken & Gaston Heimeriks, 2023. "Catching up in clean energy technologies: a patent analysis," The Journal of Technology Transfer, Springer, vol. 48(2), pages 693-715, April.
  23. Higham, Kyle & de Rassenfosse, Gaetan & Jaffe, Adam B, 2020. "Patent Quality: Towards a Systematic Framework for Analysis and Measurement," SocArXiv 49qxk_v1, Center for Open Science.
  24. 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.
  25. Donghyun You & Hyunseok Park, 2018. "Developmental Trajectories in Electrical Steel Technology Using Patent Information," Sustainability, MDPI, vol. 10(8), pages 1-15, August.
  26. 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.
  27. Hu, Zewen & Zhou, Xiji & Lin, Angela, 2023. "Evaluation and identification of potential high-value patents in the field of integrated circuits using a multidimensional patent indicators pre-screening strategy and machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
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