IDEAS home Printed from https://ideas.repec.org/r/eee/tefoso/v79y2012i6p1099-1110.html

Identifying and visualizing technology evolution: A case study of smart grid technology

Citations

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


Cited by:

  1. Esmaelian, Majid & Tavana, Madjid & Di Caprio, Debora & Ansari, Reza, 2017. "A multiple correspondence analysis model for evaluating technology foresight methods," Technological Forecasting and Social Change, Elsevier, vol. 125(C), pages 188-205.
  2. Shino Iwami & Junichiro Mori & Ichiro Sakata & Yuya Kajikawa, 2014. "Detection method of emerging leading papers using time transition," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1515-1533, November.
  3. Junmo Kim & Juneseuk Shin, 2018. "Mapping extended technological trajectories: integration of main path, derivative paths, and technology junctures," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(3), pages 1439-1459, September.
  4. 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).
  5. 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.
  6. Sanghoon Lee & Wonjoon Kim, 2017. "The knowledge network dynamics in a mobile ecosystem: a patent citation analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 717-742, May.
  7. Hui-Yun Sung & Hsi-Yin Yeh & Jin-Kwan Lin & Ssu-Han Chen, 2017. "A visualization tool of patent topic evolution using a growing cell structure neural network," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1267-1285, June.
  8. Huo, Dong & Cui, Xinyuan & Huang, Xiaolin, 2026. "Market, R&D and multi-technology Co-evolution: An explorative study on metaverse," Technovation, Elsevier, vol. 151(C).
  9. Xi, Xi & Ren, Feifei & Yu, Lean & Yang, Jing, 2023. "Detecting the technology's evolutionary pathway using HiDS-trait-driven tech mining strategy," Technological Forecasting and Social Change, Elsevier, vol. 195(C).
  10. Lothar Walter & Alfred Radauer & Martin G. Moehrle, 2017. "The beauty of brimstone butterfly: novelty of patents identified by near environment analysis based on text mining," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 103-115, April.
  11. Li, Xin & Xie, Qianqian & Daim, Tugrul & Huang, Lucheng, 2019. "Forecasting technology trends using text mining of the gaps between science and technology: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 432-449.
  12. Weiwei Liu & Yuan Tao & Zhile Yang & Kexin Bi, 2019. "Exploring and Visualizing the Patent Collaboration Network: A Case Study of Smart Grid Field in China," Sustainability, MDPI, vol. 11(2), pages 1-18, January.
  13. Ssu-Han Chen & Mu-Hsuan Huang & Dar-Zen Chen, 2013. "Driving factors of external funding and funding effects on academic innovation performance in university–industry–government linkages," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 1077-1098, March.
  14. Eilers, Kathi & Frischkorn, Jonas & Eppinger, Elisabeth & Walter, Lothar & Moehrle, Martin G., 2019. "Patent-based semantic measurement of one-way and two-way technology convergence: The case of ultraviolet light emitting diodes (UV-LEDs)," Technological Forecasting and Social Change, Elsevier, vol. 140(C), pages 341-353.
  15. Li, Shuying & Garces, Edwin & Daim, Tugrul, 2019. "Technology forecasting by analogy-based on social network analysis: The case of autonomous vehicles," Technological Forecasting and Social Change, Elsevier, vol. 148(C).
  16. Park, Sanghyun & Lee, Sungjoo, 2026. "Extracting technical problems from patent texts to support innovation: Integrating human intelligence with machine learning for electric vehicle battery technologies," Technovation, Elsevier, vol. 150(C).
  17. Hong Wu & Huifang Yi & Chang Li, 2021. "An integrated approach for detecting and quantifying the topic evolutions of patent technology: a case study on graphene field," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6301-6321, August.
  18. Choi, Jaemyoung & Lee, Sungsoo & Lee, Hakyeon, 2026. "Navigating the AI technology landscape from GitHub data," Technology in Society, Elsevier, vol. 84(C).
  19. Venugopalan, Subhashini & Rai, Varun, 2015. "Topic based classification and pattern identification in patents," Technological Forecasting and Social Change, Elsevier, vol. 94(C), pages 236-250.
  20. Zhou, Yuan & Dong, Fang & Kong, Dejing & Liu, Yufei, 2019. "Unfolding the convergence process of scientific knowledge for the early identification of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 144(C), pages 205-220.
  21. Le Song & Guilong Zhu & Xiao Yin, 2024. "Evaluating the wisdom of scholar crowds from the perspective of knowledge diffusion," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5103-5139, September.
  22. 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.
  23. Lee, Keeeun & Kim, Sunhye & Yoon, Byungun, 2022. "A systematic idea generation approach for developing a new technology: Application of a socio-technical transition system," Technological Forecasting and Social Change, Elsevier, vol. 176(C).
  24. Sohrabi, Babak & Khalilijafarabad, Ahmad, 2018. "Systematic method for finding emergence research areas as data quality," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 280-287.
  25. Hwang, Seonho & Shin, Juneseuk, 2019. "Extending technological trajectories to latest technological changes by overcoming time lags," Technological Forecasting and Social Change, Elsevier, vol. 143(C), pages 142-153.
  26. Li, Munan & Porter, Alan L. & Suominen, Arho & Burmaoglu, Serhat & Carley, Stephen, 2021. "An exploratory perspective to measure the emergence degree for a specific technology based on the philosophy of swarm intelligence," Technological Forecasting and Social Change, Elsevier, vol. 166(C).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.