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Positioning research and innovation performance using shape centroids of h-core and h-tail

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  • Kuan, Chung-Huei
  • Huang, Mu-Hsuan
  • Chen, Dar-Zen

Abstract

We propose a novel yet practical method capturing an individual's research or innovation performance by the shape centroids of the h-core and h-tail areas of its publications or patents. A large number of individuals’ relative performance with respect to their h-cores and h-tails can be simultaneously positioned and conveniently observed in two-dimensional coordinate systems. Two approaches are further proposed to the utilization of the two-dimensional distribution of shape centroids. The first approach specifically determines, within a group of individuals, those outperforming or being outperformed by a target individual. The second approach provides a quick qualitative categorization of the individuals so that the nature of their performance is revealed. Using patent assignees as an illustrative case, the approaches are tested with empirical patent assignee data.

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  • Kuan, Chung-Huei & Huang, Mu-Hsuan & Chen, Dar-Zen, 2011. "Positioning research and innovation performance using shape centroids of h-core and h-tail," Journal of Informetrics, Elsevier, vol. 5(4), pages 515-528.
    • Handle: RePEc:eee:infome:v:5:y:2011:i:4:p:515-528
    DOI: 10.1016/j.joi.2011.04.003
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    Cited by:

    1. Chen, Dar-zen & Huang, Mu-hsuan & Ye, Fred Y., 2013. "A probe into dynamic measures for h-core and h-tail," Journal of Informetrics, Elsevier, vol. 7(1), pages 129-137.
    2. Wei, Shelia X. & Tong, Tong & Rousseau, Ronald & Wang, Wanru & Ye, Fred Y., 2022. "Relations among the h-, g-, ψ-, and p-index and offset-ability," Journal of Informetrics, Elsevier, vol. 16(4).
    3. García-Pérez, Miguel A., 2012. "An extension of the h index that covers the tail and the top of the citation curve and allows ranking researchers with similar h," Journal of Informetrics, Elsevier, vol. 6(4), pages 689-699.
    4. Antonis Sidiropoulos & Dimitrios Katsaros & Yannis Manolopoulos, 2015. "Ranking and identifying influential scientists versus mass producers by the Perfectionism Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 1-31, April.
    5. Nikolay Arefiev, 2014. "A Theory Of Data-Oriented Identification With A Svar Application," HSE Working papers WP BRP 79/EC/2014, National Research University Higher School of Economics.
    6. Liu, Joan Q. & Rousseau, Ronald & Wang, Mona S. & Ye, Fred Y., 2013. "Ratios of h-cores, h-tails and uncited sources in sets of scientific papers and technical patents," Journal of Informetrics, Elsevier, vol. 7(1), pages 190-197.
    7. Jean J. Wang & Sarah X. Shao & Fred Y. Ye, 2021. "Identifying 'seed' papers in sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6001-6011, July.
    8. 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.
    9. Shaibu Mohammed & Emmanuel K. Nyantakyi & Anthony Morgan & Prosper Anumah & Justice Sarkodie-kyeremeh, 2021. "Use of relative extra citation counts and uncited publications to enhance the discriminatory power of the h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 181-199, January.

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