IDEAS home Printed from https://ideas.repec.org/r/eee/tefoso/v104y2016icp16-29.html
   My bibliography  Save this item

Identification and monitoring of possible disruptive technologies by patent-development paths and topic modeling

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. 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.
  2. Minhao Xiang & Dian Fu & Kun Lv, 2023. "Identifying and Predicting Trends of Disruptive Technologies: An Empirical Study Based on Text Mining and Time Series Forecasting," Sustainability, MDPI, vol. 15(6), pages 1-22, March.
  3. Teso, E. & Olmedilla, M. & Martínez-Torres, M.R. & Toral, S.L., 2018. "Application of text mining techniques to the analysis of discourse in eWOM communications from a gender perspective," Technological Forecasting and Social Change, Elsevier, vol. 129(C), pages 131-142.
  4. Juite Wang & Tzu-Yen Hsu, 2023. "Early discovery of emerging multi-technology convergence for analyzing technology opportunities from patent data: the case of smart health," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4167-4196, August.
  5. Martin Kalthaus, 2017. "Identifying technological sub-trajectories in photovoltaic patents," Jena Economics Research Papers 2017-010, Friedrich-Schiller-University Jena.
  6. Leonid Gokhberg & Ilya Kuzminov & Pavel Bakhtin & Elena Tochilina & Alexander Chulok & Anton Timofeev & Alina Lavrinenko, 2017. "Big-Data-Augmented Approach to Emerging Technologies Identification: Case of Agriculture and Food Sector," HSE Working papers WP BRP 76/STI/2017, National Research University Higher School of Economics.
  7. Christian Mühlroth & Laura Kölbl & Michael Grottke, 2023. "Innovation signals: leveraging machine learning to separate noise from news," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(5), pages 2649-2676, May.
  8. de Paulo, Alex Fabianne & Nunes, Breno & Porto, Geciane, 2020. "Emerging green technologies for vehicle propulsion systems," Technological Forecasting and Social Change, Elsevier, vol. 159(C).
  9. Kang, Jia-Ning & Wei, Yi-Ming & Liu, Lan-cui & Wang, Jin-Wei, 2021. "Observing technology reserves of carbon capture and storage via patent data: Paving the way for carbon neutral," Technological Forecasting and Social Change, Elsevier, vol. 171(C).
  10. 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.
  11. Junwei Ma & Jianhua Wang & Philip Szmedra, 2019. "Sustainable Competitive Position of Mobile Communication Companies: Comprehensive Perspectives of Insiders and Outsiders," Sustainability, MDPI, vol. 11(7), pages 1-15, April.
  12. Roh, Taeyeoun & Yoon, Byungun, 2023. "Discovering technology and science innovation opportunity based on sentence generation algorithm," Journal of Informetrics, Elsevier, vol. 17(2).
  13. Xiwen Liu & Xuezhao Wang & Lucheng Lyu & Yanpeng Wang, 2022. "Identifying disruptive technologies by integrating multi-source data," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5325-5351, September.
  14. Weifeng Jia & Shuo Wang & Yongping Xie & Zifeng Chen & Kaixin Gong, 2022. "Disruptive technology identification of intelligent logistics robots in AIoT industry: Based on attributes and functions analysis," Systems Research and Behavioral Science, Wiley Blackwell, vol. 39(3), pages 557-568, May.
  15. Wei, Yi-Ming & Kang, Jia-Ning & Yu, Bi-Ying & Liao, Hua & Du, Yun-Fei, 2017. "A dynamic forward-citation full path model for technology monitoring: An empirical study from shale gas industry," Applied Energy, Elsevier, vol. 205(C), pages 769-780.
  16. Alfonso Ávila-Robinson & Shintaro Sengoku, 2017. "Tracing the knowledge-building dynamics in new stem cell technologies through techno-scientific networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1691-1720, September.
  17. Sommarberg, Matti & Mäkinen, Saku J., 2019. "A method for anticipating the disruptive nature of digitalization in the machine-building industry," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 808-819.
  18. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
  19. Xuan Shi & Lingfei Cai & Hongfang Song, 2019. "Discovering Potential Technology Opportunities for Fuel Cell Vehicle Firms: A Multi-Level Patent Portfolio-Based Approach," Sustainability, MDPI, vol. 11(22), pages 1-22, November.
  20. Wang, Lili & Jiang, Shan & Zhang, Shiyun, 2020. "Mapping technological trajectories and exploring knowledge sources: A case study of 3D printing technologies," Technological Forecasting and Social Change, Elsevier, vol. 161(C).
  21. Feder, Christophe, 2018. "The effects of disruptive innovations on productivity," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 186-193.
  22. Won Sang Lee & So Young Sohn, 2017. "Identifying Emerging Trends of Financial Business Method Patents," Sustainability, MDPI, vol. 9(9), pages 1-21, September.
  23. Sun, Bixuan & Kolesnikov, Sergey & Goldstein, Anna & Chan, Gabriel, 2021. "A dynamic approach for identifying technological breakthroughs with an application in solar photovoltaics," Technological Forecasting and Social Change, Elsevier, vol. 165(C).
  24. Paweł Ziemba, 2023. "Selection of Photovoltaic Panels Based on Ranges of Criteria Weights and Balanced Assessment Criteria," Energies, MDPI, vol. 16(17), pages 1-18, September.
  25. Cheng, Yu & Huang, Lucheng & Ramlogan, Ronnie & Li, Xin, 2017. "Forecasting of potential impacts of disruptive technology in promising technological areas: Elaborating the SIRS epidemic model in RFID technology," Technological Forecasting and Social Change, Elsevier, vol. 117(C), pages 170-183.
  26. Anne Parlina & Kalamullah Ramli & Hendri Murfi, 2021. "Exposing Emerging Trends in Smart Sustainable City Research Using Deep Autoencoders-Based Fuzzy C-Means," Sustainability, MDPI, vol. 13(5), pages 1-28, March.
  27. Fernández, Ana María & Ferrándiz, Esther & Medina, Jennifer, 2022. "The diffusion of energy technologies. Evidence from renewable, fossil, and nuclear energy patents," Technological Forecasting and Social Change, Elsevier, vol. 178(C).
  28. Huang, Hung-Chun & Su, Hsin-Ning, 2019. "The innovative fulcrums of technological interdisciplinarity: An analysis of technology fields in patents," Technovation, Elsevier, vol. 84, pages 59-70.
  29. Han, Xiaotong & Zhu, Donghua & Lei, Ming & Daim, Tugrul, 2021. "R&D trend analysis based on patent mining: An integrated use of patent applications and invalidation data," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
  30. 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).
  31. Guo, Jianfeng & Pan, Jiaofeng & Guo, Jianxin & Gu, Fu & Kuusisto, Jari, 2019. "Measurement framework for assessing disruptive innovations," Technological Forecasting and Social Change, Elsevier, vol. 139(C), pages 250-265.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.