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A stochastic patent citation analysis approach to assessing future technological impacts

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Cited by:

  1. Armin Mertens & Marc Scheufen, 2024. "Intellectual property and fourth industrial revolution technologies: how the patent system is shaping the future in the data-driven economy," European Journal of Law and Economics, Springer, vol. 57(1), pages 275-310, April.
  2. Lee, Changyong & Kwon, Ohjin & Kim, Myeongjung & Kwon, Daeil, 2018. "Early identification of emerging technologies: A machine learning approach using multiple patent indicators," Technological Forecasting and Social Change, Elsevier, vol. 127(C), pages 291-303.
  3. Serkan Altuntas & Zulfiye Erdogan & Turkay Dereli, 2020. "A clustering-based approach for the evaluation of candidate emerging technologies," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1157-1177, August.
  4. Myoungjae Choi & Sun-Hi Yoo & Jongtaik Lee & Jeongsub Choi & Byunghoon Kim, 2022. "A modified gamma/Gompertz/NBD model for estimating technology lifetime," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5731-5751, October.
  5. Hong, Suckwon & Kim, Juram & Woo, Han-Gyun & Kim, Young-Choon & Lee, Changyong, 2022. "Screening ideas in the early stages of technology development: A word2vec and convolutional neural network approach," Technovation, Elsevier, vol. 112(C).
  6. Xipeng Liu & Xinmiao Li, 2022. "Early Identification of Significant Patents Using Heterogeneous Applicant-Citation Networks Based on the Chinese Green Patent Data," Sustainability, MDPI, vol. 14(21), pages 1-27, October.
  7. Ho, Jonathan C. & Saw, Ewe-Chai & Lu, Louis Y.Y. & Liu, John S., 2014. "Technological barriers and research trends in fuel cell technologies: A citation network analysis," Technological Forecasting and Social Change, Elsevier, vol. 82(C), pages 66-79.
  8. Yuan Zhou & Fang Dong & Yufei Liu & Liang Ran, 2021. "A deep learning framework to early identify emerging technologies in large-scale outlier patents: an empirical study of CNC machine tool," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 969-994, February.
  9. Perruchas, François & Consoli, Davide & Barbieri, Nicolò, 2020. "Specialisation, diversification and the ladder of green technology development," Research Policy, Elsevier, vol. 49(3).
  10. Kim, Juram & Hong, Suckwon & Kang, Yubin & Lee, Changyong, 2023. "Domain-specific valuation of university technologies using bibliometrics, Jonckheere–Terpstra tests, and data envelopment analysis," Technovation, Elsevier, vol. 122(C).
  11. Lee, Changyong & Song, Bomi & Park, Yongtae, 2015. "An instrument for scenario-based technology roadmapping: How to assess the impacts of future changes on organisational plans," Technological Forecasting and Social Change, Elsevier, vol. 90(PA), pages 285-301.
  12. Antonio Messeni Petruzzelli & Daniele Rotolo & Vito Albino, 2014. "Determinants of Patent Citations in Biotechnology: An Analysis of Patent Influence Across the Industrial and Organizational Boundaries," SPRU Working Paper Series 2014-05, SPRU - Science Policy Research Unit, University of Sussex Business School.
  13. Kim, Juram & Lee, Gyumin & Lee, Seungbin & Lee, Changyong, 2022. "Towards expert–machine collaborations for technology valuation: An interpretable machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 183(C).
  14. Wei Du & Yibo Wang & Wei Xu & Jian Ma, 2021. "A personalized recommendation system for high-quality patent trading by leveraging hybrid patent analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9369-9391, December.
  15. Sajad Ashouri & Anne-Laure Mention & Kosmas X. Smyrnios, 2021. "Anticipation and analysis of industry convergence using patent-level indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5727-5758, July.
  16. Farshad Madani, 2015. "‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 323-335, October.
  17. Yuan, Xiaodong & Cai, Yuchen, 2021. "Forecasting the development trend of low emission vehicle technologies: Based on patent data," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  18. Changyong Lee & Suckwon Hong & Juram Kim, 2021. "Anticipating multi-technology convergence: a machine learning approach using patent information," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(3), pages 1867-1896, March.
  19. Jang, Hyun Jin & Woo, Han-Gyun & Lee, Changyong, 2017. "Hawkes process-based technology impact analysis," Journal of Informetrics, Elsevier, vol. 11(2), pages 511-529.
  20. Nicoló Barbieri & François Perruchas & Davide Consoli, 2020. "Specialization, Diversification, and Environmental Technology Life Cycle," Economic Geography, Taylor & Francis Journals, vol. 96(2), pages 161-186, March.
  21. Huang, Ying & Li, Ruinan & Zou, Fang & Jiang, Lidan & Porter, Alan L. & Zhang, Lin, 2022. "Technology life cycle analysis: From the dynamic perspective of patent citation networks," Technological Forecasting and Social Change, Elsevier, vol. 181(C).
  22. Jeeeun Kim & Sungjoo Lee, 2017. "Forecasting and identifying multi-technology convergence based on patent data: the case of IT and BT industries in 2020," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 47-65, April.
  23. Byunghoon Kim & Gianluca Gazzola & Jae-Min Lee & Dohyun Kim & Kanghoe Kim & Myong K. Jeong, 2014. "Inter-cluster connectivity analysis for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1811-1825, March.
  24. Jorge Nogueira de Paiva Britto & Leonardo Costa Ribeiro & Lucas Teixeira Araújo & Eduardo da Motta e Albuquerque, 2019. "Patent citations, knowledge flows and catching-up: evidences of different national experiences for the period 1982-2006," Textos para Discussão Cedeplar-UFMG 606, Cedeplar, Universidade Federal de Minas Gerais.
  25. Kim, Gabjo & Bae, Jinwoo, 2017. "A novel approach to forecast promising technology through patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 228-237.
  26. Byunghoon Kim & Gianluca Gazzola & Jaekyung Yang & Jae-Min Lee & Byoung-Youl Coh & Myong K. Jeong & Young-Seon Jeong, 2017. "Two-phase edge outlier detection method for technology opportunity discovery," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 1-16, October.
  27. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  28. Pontus Braunerhjelm & Roger Svensson, 2024. "Inventions, commercialization strategies, and knowledge spillovers in SMEs," Small Business Economics, Springer, vol. 63(1), pages 275-297, June.
  29. Ha, Sung Ho & Liu, Weina & Cho, Hune & Kim, Sang Hyun, 2015. "Technological advances in the fuel cell vehicle: Patent portfolio management," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 277-289.
  30. Chung, Park & Sohn, So Young, 2020. "Early detection of valuable patents using a deep learning model: Case of semiconductor industry," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
  31. Youngjae Choi & Sanghyun Park & Sungjoo Lee, 2021. "Identifying emerging technologies to envision a future innovation ecosystem: A machine learning approach to patent data," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 5431-5476, July.
  32. Jeon, Daeseong & Ahn, Joon Mo & Kim, Juram & Lee, Changyong, 2022. "A doc2vec and local outlier factor approach to measuring the novelty of patents," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
  33. Niemann, Helen & Moehrle, Martin G. & Frischkorn, Jonas, 2017. "Use of a new patent text-mining and visualization method for identifying patenting patterns over time: Concept, method and test application," Technological Forecasting and Social Change, Elsevier, vol. 115(C), pages 210-220.
  34. Song, Haoyang & Hou, Jianhua & Zhang, Yang, 2023. "The measurements and determinants of patent technological value: Lifetime, strength, breadth, and dispersion from the technology diffusion perspective," Journal of Informetrics, Elsevier, vol. 17(1).
  35. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
  36. Gao, Lidan & Porter, Alan L. & Wang, Jing & Fang, Shu & Zhang, Xian & Ma, Tingting & Wang, Wenping & Huang, Lu, 2013. "Technology life cycle analysis method based on patent documents," Technological Forecasting and Social Change, Elsevier, vol. 80(3), pages 398-407.
  37. Changyong Lee & Gyumin Lee, 2019. "Technology opportunity analysis based on recombinant search: patent landscape analysis for idea generation," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(2), pages 603-632, November.
  38. Juram Kim & Changyong Lee, 2017. "Stochastic service life cycle analysis using customer reviews," The Service Industries Journal, Taylor & Francis Journals, vol. 37(5-6), pages 296-316, April.
  39. Lee, Changyong & Kang, Bokyoung & Shin, Juneseuk, 2015. "Novelty-focused patent mapping for technology opportunity analysis," Technological Forecasting and Social Change, Elsevier, vol. 90(PB), pages 355-365.
  40. Keller, Jonas & von der Gracht, Heiko A., 2014. "The influence of information and communication technology (ICT) on future foresight processes — Results from a Delphi survey," Technological Forecasting and Social Change, Elsevier, vol. 85(C), pages 81-92.
  41. Zhu, Lin & Cunningham, Scott W., 2022. "Unveiling the knowledge structure of technological forecasting and social change (1969–2020) through an NMF-based hierarchical topic model," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
  42. Choi, Jinho & Hwang, Yong-Sik, 2014. "Patent keyword network analysis for improving technology development efficiency," Technological Forecasting and Social Change, Elsevier, vol. 83(C), pages 170-182.
  43. Wooseok Jang & Heeyeul Kwon & Yongtae Park & Hakyeon Lee, 2018. "Predicting the degree of interdisciplinarity in academic fields: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(1), pages 231-254, July.
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