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A stochastic patent citation analysis approach to assessing future technological impacts

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  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. 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.
  3. 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.
  4. 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).
  5. 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.
  6. Perruchas, François & Consoli, Davide & Barbieri, Nicolò, 2020. "Specialisation, diversification and the ladder of green technology development," Research Policy, Elsevier, vol. 49(3).
  7. 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.
  8. 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.
  9. 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.
  10. 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.
  11. 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).
  12. 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.
  13. 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.
  14. Lee, Changyong, 2021. "A review of data analytics in technological forecasting," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
  15. 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.
  16. 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.
  17. 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).
  18. 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.
  19. 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.
  20. 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.
  21. 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.
  22. 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.
  23. 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.
  24. 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.
  25. 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).
  26. 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.
  27. 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).
  28. 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.
  29. Farshad Madani, 2015. "‘Technology Mining’ bibliometrics analysis: applying network analysis and cluster analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 323-335, October.
  30. 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).
  31. 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.
  32. 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.
  33. 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.
  34. 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.
  35. Pontus Braunerhjelm & Roger Svensson, 2024. "Inventions, commercialization strategies, and knowledge spillovers in SMEs," Small Business Economics, Springer, vol. 63(1), pages 275-297, June.
  36. 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.
  37. 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).
  38. 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).
  39. 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).
  40. 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.
  41. 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.
  42. 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).
  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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