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A new approach for measuring the value of patents based on structural indicators for ego patent citation networks

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  • Xiaojun Hu
  • Ronald Rousseau
  • Jin Chen

Abstract

Technology sectors differ in terms of technological complexity. When studying technology and innovation through patent analysis it is well known that similar amounts of technological knowledge can produce different numbers of patented innovation as output. A new multilayered approach to measure the technological value of patents based on ego patent citation networks (PCNs) is developed in this study. The results show that the structural indicators for the ego PCN developed in this contribution can characterize groups of patents and, hence, in an indirect way, the health of companies.

Suggested Citation

  • Xiaojun Hu & Ronald Rousseau & Jin Chen, 2012. "A new approach for measuring the value of patents based on structural indicators for ego patent citation networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(9), pages 1834-1842, September.
    • Handle: RePEc:bla:jamist:v:63:y:2012:i:9:p:1834-1842
    DOI: 10.1002/asi.22632
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    Citations

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    Cited by:

    1. Adam B. Jaffe & Gaétan de Rassenfosse, 2017. "Patent citation data in social science research: Overview and best practices," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(6), pages 1360-1374, June.
    2. JinHyo Joseph Yun & EuiSeob Jeong & JinSeu Park, 2016. "Network Analysis of Open Innovation," Sustainability, MDPI, vol. 8(8), pages 1-21, July.
    3. Lyu, Haihua & Bu, Yi & Zhao, Zhenyue & Zhang, Jiarong & Li, Jiang, 2022. "Citation bias in measuring knowledge flow: Evidence from the web of science at the discipline level," Journal of Informetrics, Elsevier, vol. 16(4).
    4. Hu, Xiaojun & Rousseau, Ronald, 2016. "Scientific influence is not always visible: The phenomenon of under-cited influential publications," Journal of Informetrics, Elsevier, vol. 10(4), pages 1079-1091.
    5. Guijie Zhang & Guang Yu & Yuqiang Feng & Luning Liu & Zhenhua Yang, 2017. "Improving the publication delay model to characterize the patent granting process," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(2), pages 621-637, May.
    6. Wei Yang & Xiang Yu & Dian Wang & Jinrui Yang & Ben Zhang, 2021. "Spatio-temporal evolution of technology flows in China: patent licensing networks 2000–2017," The Journal of Technology Transfer, Springer, vol. 46(5), pages 1674-1703, October.
    7. Nobuya Fukugawa, 2022. "Effects of the quality of science on the initial public offering of university spinoffs: evidence from Japan," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4439-4455, August.
    8. Xiaojun Hu & Ronald Rousseau, 2015. "A simple approach to describe a company’s innovative activities and their technological breadth," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(2), pages 1401-1411, February.
    9. Jiang, Xiaorui & Zhuge, Hai, 2019. "Forward search path count as an alternative indirect citation impact indicator," Journal of Informetrics, Elsevier, vol. 13(4).
    10. Hu, Xiaojun & Rousseau, Ronald, 2018. "A new approach to explore the knowledge transition path in the evolution of science & technology: From the biology of restriction enzymes to their application in biotechnology," Journal of Informetrics, Elsevier, vol. 12(3), pages 842-857.
    11. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.

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