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Exploring the h‐index at patent level

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  • Jian Cheng Guan
  • Xia Gao

Abstract

As an acceptable proxy for innovative activity, patents have become increasingly important in recent years. Patents and patent citations have been used for construction of technology indicators. This article presents an alternative to other citation‐based indicators, i.e., the patent h‐index, which is borrowed from bibliometrics. We conduct the analysis on a sample of the world's top 20 firms ranked by total patents granted in the period 1996–2005 from the Derwent Innovations Index in the semiconductor area. We also investigate the relationships between the patent h‐index and other three indicators, i.e., patent counts, citation counts, and the mean family size (MFS). The findings show that the patent h‐index is indeed an effective indicator for evaluating the technological importance and quality, or impact, for an assignee. In addition, the MFS indicator correlates negatively and not significantly with the patent h‐index, which indicates that the “social value” of a patent is in disagreement with its “private value.” The two indicators, patent h‐index and MFS, both provide an overview of the value of patents, but from two different angles.

Suggested Citation

  • Jian Cheng Guan & Xia Gao, 2009. "Exploring the h‐index at patent level," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(1), pages 35-40, January.
    • Handle: RePEc:bla:jamist:v:60:y:2009:i:1:p:35-40
    DOI: 10.1002/asi.20954
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    Cited by:

    1. Federico Caviggioli & Boris Forthmann, 2022. "Reach for the stars: disentangling quantity and quality of inventors’ productivity in a multifaceted latent variable model," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7015-7040, December.
    2. Yu Chen & Haoming Shi & Jun Ma & Victor Shi, 2020. "The Spatial Spillover Effect in Hi-Tech Industries: Empirical Evidence from China," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
    3. Bing Cao & Zishu Han & Ling Liang & Yuanyuan Liu & Jialiang Wang & Jiaping Xie, 2022. "Independent Innovation or Secondary Innovation: The Moderating of Network Embedded Innovation," Sustainability, MDPI, vol. 14(22), pages 1-14, November.
    4. Susan George & Hiran H. Lathabai & Thara Prabhakaran & Manoj Changat, 2020. "A framework towards bias-free contextual productivity assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 127-157, January.
    5. Jun Liu & Yu Qian & Huihong Chang & Jeffrey Yi-Lin Forrest, 2022. "The Impact of Technology Innovation on Enterprise Capacity Utilization—Evidence from China’s Yangtze River Economic Belt," Sustainability, MDPI, vol. 14(18), pages 1-17, September.
    6. Zhu, Xinhua & Li, Yan & Zhang, Peifeng & Wei, Yigang & Zheng, Xuyang & Xie, Lingling, 2019. "Temporal–spatial characteristics of urban land use efficiency of China’s 35mega cities based on DEA: Decomposing technology and scale efficiency," Land Use Policy, Elsevier, vol. 88(C).
    7. Mark Bukowski & Sandra Geisler & Thomas Schmitz-Rode & Robert Farkas, 2020. "Feasibility of activity-based expert profiling using text mining of scientific publications and patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 123(2), pages 579-620, May.
    8. Haisen Wang & Gangqiang Yang & Jiaying Qin, 2020. "City Centrality, Migrants and Green Inovation Efficiency: Evidence from 106 Cities in the Yangtze River Economic Belt of China," IJERPH, MDPI, vol. 17(2), pages 1-21, January.
    9. Xiao Li, 2020. "The effectiveness of internal control and innovation performance: An intermediary effect based on corporate social responsibility," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-31, June.
    10. Liu, Jun & Chang, Huihong & Forrest, Jeffrey Yi-Lin & Yang, Baohua, 2020. "Influence of artificial intelligence on technological innovation: Evidence from the panel data of china's manufacturing sectors," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    11. Zhang, Lin & Thijs, Bart & Glänzel, Wolfgang, 2011. "The diffusion of H-related literature," Journal of Informetrics, Elsevier, vol. 5(4), pages 583-593.
    12. Jun Hong Park & Hyunseog Chung & Ki Hong Kim & Jin Ju Kim & Chulung Lee, 2021. "The Impact of Technological Capability on Financial Performance in the Semiconductor Industry," Sustainability, MDPI, vol. 13(2), pages 1-20, January.
    13. Tang Yao & Yigang Wei & Jianhong Zhang & Yani Wang & Yunjiang Yu & Wenyang Huang, 2022. "What influences the urban sewage discharge in China? The effect of diversified factors on the urban sewage discharge in different regions of China," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(5), pages 6099-6135, May.
    14. Kai Guo & Tiantian Zhang & Yan Liang & Jiyao Zhao & Xiangmin Zhang, 2023. "Research on the promotion path of green technology innovation of an enterprise from the perspective of technology convergence: configuration analysis using new energy vehicles as an example," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(6), pages 4989-5008, June.
    15. Xu, Le & Yang, Lili & Li, Ding & Shao, Shuai, 2023. "Asymmetric effects of heterogeneous environmental standards on green technology innovation: Evidence from China," Energy Economics, Elsevier, vol. 117(C).
    16. Yayuan Pang & Xinjun Wang, 2020. "Land-Use Efficiency in Shandong (China): Empirical Analysis Based on a Super-SBM Model," Sustainability, MDPI, vol. 12(24), pages 1-20, December.

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