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Measuring technological performance of assignees using trace metrics in three fields

Author

Listed:
  • Mu-Hsuan Huang

    (National Taiwan University)

  • Dar-Zen Chen

    (National Taiwan University)

  • Danqi Shen

    (National Taiwan University)

  • Mona S. Wang

    (Zhejiang University)

  • Fred Y. Ye

    (Nanjing University)

Abstract

The study establishes three synthetic indicators derived from academic traces—assignee traces T 1, T 2 and ST—and investigates their application in evaluating technological performance of assignees. Patent data for the top 100 assignees in three fields, “Computer Hardware & Software”, “Motors, Engines & Parts”, and “Drugs & Medical”, were retrieved from USPTO for further analysis. The results reveal that traces are indeed valid and useful indicators for measuring technological performance and providing detailed technical information about assignees and the industry. In addition, we investigate the relationship between traces and three other indicators: patent citation counts, Current Impact Index and patent h-index. In comparison with the three other indicators, traces demonstrate unique advantages and can be a good complement to patent citation analysis.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:scient:v:104:y:2015:i:1:d:10.1007_s11192-015-1604-8
    DOI: 10.1007/s11192-015-1604-8
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    1. Manuel Trajtenberg, 1990. "A Penny for Your Quotes: Patent Citations and the Value of Innovations," RAND Journal of Economics, The RAND Corporation, vol. 21(1), pages 172-187, Spring.
    2. Bronwyn H. Hall & Adam Jaffe & Manuel Trajtenberg, 2005. "Market Value and Patent Citations," RAND Journal of Economics, The RAND Corporation, vol. 36(1), pages 16-38, Spring.
    3. Hagedoorn, John & Cloodt, Myriam, 2003. "Measuring innovative performance: is there an advantage in using multiple indicators?," Research Policy, Elsevier, vol. 32(8), pages 1365-1379, September.
    4. Fred Y. Ye & Loet Leydesdorff, 2014. "The “academic trace” of the performance matrix: A mathematical synthesis of the h-index and the integrated impact indicator (I3)," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(4), pages 742-750, April.
    5. Bruno Van Pottelsberghe & Eleftherios Sapsalis & Ran Navon, 2006. "Academic vs. industry patenting: an in-depth analysis of what determines patent value," ULB Institutional Repository 2013/6197, ULB -- Universite Libre de Bruxelles.
    6. Bronwyn H. Hall & Adam B. Jaffe & Manuel Trajtenberg, 2000. "Market Value and Patent Citations: A First Look," NBER Working Papers 7741, National Bureau of Economic Research, Inc.
    7. Bronwyn H. Hall & Adam B. Jaffe & Manuel Trajtenberg, 2001. "The NBER Patent Citation Data File: Lessons, Insights and Methodological Tools," NBER Working Papers 8498, National Bureau of Economic Research, Inc.
    8. Karki, M. M. S., 1997. "Patent citation analysis: A policy analysis tool," World Patent Information, Elsevier, vol. 19(4), pages 269-272, December.
    9. Dietmar Harhoff & Francis Narin & F. M. Scherer & Katrin Vopel, 1999. "Citation Frequency And The Value Of Patented Inventions," The Review of Economics and Statistics, MIT Press, vol. 81(3), pages 511-515, August.
    10. Parthasarathi Banerjee & B. M. Gupta & K. C. Garg, 2000. "Patent Statistics as Indicators of Competition an Analysis of Patenting in Biotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 47(1), pages 95-116, January.
    11. Loet Leydesdorff & Lutz Bornmann, 2011. "Integrated impact indicators compared with impact factors: An alternative research design with policy implications," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 62(11), pages 2133-2146, November.
    12. Narin, Francis & Noma, Elliot & Perry, Ross, 1987. "Patents as indicators of corporate technological strength," Research Policy, Elsevier, vol. 16(2-4), pages 143-155, August.
    13. Hirschey, Mark & Richardson, Vernon J & Scholz, Susan, 2001. "Value Relevance of Nonfinancial Information: The Case of Patent Data," Review of Quantitative Finance and Accounting, Springer, vol. 17(3), pages 223-235, November.
    14. 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.
    15. 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.
    16. Martin Meyer, 2000. "What is Special about Patent Citations? Differences between Scientific and Patent Citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 49(1), pages 93-123, August.
    17. Fred Y. Ye & Ronald Rousseau, 2010. "Probing the h-core: an investigation of the tail–core ratio for rank distributions," Scientometrics, Springer;Akadémiai Kiadó, vol. 84(2), pages 431-439, August.
    18. Kuan, Chung-Huei & Huang, Mu-Hsuan & Chen, Dar-Zen, 2011. "Ranking patent assignee performance by h-index and shape descriptors," Journal of Informetrics, Elsevier, vol. 5(2), pages 303-312.
    19. Patrick Thomas, 2001. "A relationship between technology indicators and stock market performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 51(1), pages 319-333, April.
    20. Chun-Ting Zhang, 2009. "The e-Index, Complementing the h-Index for Excess Citations," PLOS ONE, Public Library of Science, vol. 4(5), pages 1-4, May.
    21. 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.
    22. Shyh-Jen Wang, 2007. "Factors to evaluate a patent in addition to citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 71(3), pages 509-522, June.
    23. Hirschey, Mark & Richardson, Vernon J., 2001. "Valuation effects of patent quality: A comparison for Japanese and U.S. firms," Pacific-Basin Finance Journal, Elsevier, vol. 9(1), pages 65-82, January.
    24. Sapsalis, Eleftherios & van Pottelsberghe de la Potterie, Bruno & Navon, Ran, 2006. "Academic versus industry patenting: An in-depth analysis of what determines patent value," Research Policy, Elsevier, vol. 35(10), pages 1631-1645, December.
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