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Using Essential Patent Index and Essential Technological Strength to evaluate industrial technological innovation competitiveness

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  • Dar-Zen Chen

    (National Taiwan University)

  • Wen-Yau Cathy Lin

    (National Taiwan University)

  • Mu-Hsuan Huang

    (National Taiwan University)

Abstract

The aim of this article is to develop new patent indicators for evaluating technological innovation competitiveness between companies. A novel indicator representing an industrial’s patent performance, Essential Patent Index (EPI), was developed by incorporating information on who cited these patents and when these patents were cited, based on the assumption that both contribute to meaningful quality assessment. By combining EPI and Chi’s well known Technological Strength (TS) indicator, a second novel indicator Essential Technological Strength (ETS) was developed to represent the innovation competitiveness of an individual company. In this study, patent performance of three high-tech industries in Taiwan were analyzed using ETS as well as the traditional TS for comparison. Results from this analysis demonstrated that ETS provided better insights by clearly verifying the latent influence of citations, reinforcing the impact of essential patents, and aggrandizing the differences of innovation competitiveness between companies.

Suggested Citation

  • Dar-Zen Chen & Wen-Yau Cathy Lin & Mu-Hsuan Huang, 2007. "Using Essential Patent Index and Essential Technological Strength to evaluate industrial technological innovation competitiveness," Scientometrics, Springer;Akadémiai Kiadó, vol. 71(1), pages 101-116, April.
    • Handle: RePEc:spr:scient:v:71:y:2007:i:1:d:10.1007_s11192-007-1655-6
    DOI: 10.1007/s11192-007-1655-6
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    2. Zhang, Sifei & Yuan, Chien-Chung & Chang, Ke-Chiun & Ken, Yun, 2012. "Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network," Journal of Informetrics, Elsevier, vol. 6(4), pages 485-495.
    3. Mu-Hsuan Huang & Dar-Zen Chen & Danqi Shen & Mona S. Wang & Fred Y. Ye, 2015. "Measuring technological performance of assignees using trace metrics in three fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 61-86, July.
    4. Mu-Hsuan Huang & Hui-Yun Sung & Chun-Chieh Wang & Dar-Zen Chen, 2013. "Exploring patent performance and technology interactions of universities, industries, governments and individuals," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(1), pages 11-26, July.
    5. Hu, Zewen & Zhou, Xiji & Lin, Angela, 2023. "Evaluation and identification of potential high-value patents in the field of integrated circuits using a multidimensional patent indicators pre-screening strategy and machine learning approaches," Journal of Informetrics, Elsevier, vol. 17(2).
    6. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.
    7. Scott D. Bass & Lukasz A. Kurgan, 2010. "Discovery of factors influencing patent value based on machine learning in patents in the field of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 217-241, February.
    8. Si Joo & Keun Lee, 2010. "Samsung's catch-up with Sony: an analysis using US patent data," Journal of the Asia Pacific Economy, Taylor & Francis Journals, vol. 15(3), pages 271-287.
    9. Kang, Kiyeon & Sohn, So Young, 2016. "Evaluating the patenting activities of pharmaceutical research organizations based on new technology indices," Journal of Informetrics, Elsevier, vol. 10(1), pages 74-81.
    10. Yoon, Jungsub & Oh, Yoonhwan & Lee, Jeong-Dong, 2017. "The impact of policy consistency on technological competitiveness: A study on OECD countries," Energy Policy, Elsevier, vol. 108(C), pages 425-434.
    11. Chang, Ke-Chiun & Chen, Dar-Zen & Huang, Mu-Hsuan, 2012. "The relationships between the patent performance and corporation performance," Journal of Informetrics, Elsevier, vol. 6(1), pages 131-139.
    12. Fu, Zhenkang & Zhu, Qinghua & Liu, Bingxiang & Yan, Chungen, 2025. "Patent lifespan prediction and interpreting the key determinants: An application of interpretable machine learning survival analysis approach," Technological Forecasting and Social Change, Elsevier, vol. 215(C).

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