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Exploring the nonlinear effects of patent H index, patent citations, and essential technological strength on corporate performance by using artificial neural network

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  • Zhang, Sifei
  • Yuan, Chien-Chung
  • Chang, Ke-Chiun
  • Ken, Yun

Abstract

This study utilizes the artificial neural network to explore the nonlinear relationships between patent performance and the corporate performance of the pharmaceutical companies. Patent performance measured from patent H index, patent citations, and essential technological strength (ETS). The result shows that patent H index, patent citations, and ETS has the nonlinear effect on the corporate performance of the pharmaceutical companies.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:infome:v:6:y:2012:i:4:p:485-495
    DOI: 10.1016/j.joi.2012.03.006
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    References listed on IDEAS

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    Cited by:

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    3. 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).
    4. 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.
    5. Miloš Milovančević & Dušan Marković & Vlastimir Nikolić & Igor Mladenović, 2017. "RETRACTED ARTICLE: Determination of the most influential factors for number of patents prediction by adaptive neuro-fuzzy technique," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(3), pages 1207-1216, May.

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