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

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  • Chen, Ssu-Han
  • Huang, Mu-Hsuan
  • Chen, Dar-Zen

Abstract

This paper attempts to illustrate the technology evolution for describing the emergence, development, or demise of a technology field. The basic idea is to divide a technology field into tight-knit communities over time and track their inter-year continuity. Then the evolving trajectories are presented through visualizing the timeline plot where each community is drawn as a function of its size, average age, and time. Analyzing a set of patents related to smart grid, we found that this technology consists of several trajectories. Among them, the subjects of network management and e-commerce are relatively young and active. The power system recently has emerged owing to the joining of integration and management concepts. As aging subjects, wireless communication system receives more attention than wired one does. The proposed timeline plot gives insights into evolving trajectories, from which the structure of the technology could be investigated and certain emerging subjects might be figured out. Such understandings are essential information to experts who endeavor to profile technology development and keep up with current trends.

Suggested Citation

  • Chen, Ssu-Han & Huang, Mu-Hsuan & Chen, Dar-Zen, 2012. "Identifying and visualizing technology evolution: A case study of smart grid technology," Technological Forecasting and Social Change, Elsevier, vol. 79(6), pages 1099-1110.
    • Handle: RePEc:eee:tefoso:v:79:y:2012:i:6:p:1099-1110
    DOI: 10.1016/j.techfore.2011.12.011
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    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. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.
    8. 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).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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).
    16. 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).
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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).

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