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A Predictive Model of Technology Transfer Using Patent Analysis

Author

Listed:
  • Jaehyun Choi

    (Department of Industrial Management Engineering, Korea University, Seoul 136-701, Korea)

  • Dongsik Jang

    (Department of Industrial Management Engineering, Korea University, Seoul 136-701, Korea)

  • Sunghae Jun

    (Department of Statistics, Cheongju University, Chungbuk 363-764, Korea)

  • Sangsung Park

    (Graduate School of Management of Technology, Korea University, Seoul 136-701, Korea)

Abstract

The rapid pace of technological advances creates many difficulties for R&D practitioners in analyzing emerging technologies. Patent information analysis is an effective tool in this situation. Conventional patent information analysis has focused on the extraction of vacant, promising, or core technologies and the monitoring of technological trends. From a technology management perspective, the ultimate purpose of R&D is technology commercialization. The core of technology commercialization is the technology transfer phase. Although a great number of patents are filed, publicized, and registered every year, many commercially relevant patents are filtered through registration processes that examine novelty, creativity, and industrial applicability. Despite the efforts of these selection processes, the number of patents being transferred is low when compared with total annual patent registrations. To deal with this problem, this study proposes a predictive model for technology transfer using patent analysis. In the predictive model, patent analysis is conducted to reveal the quantitative relations between technology transfer and a range of variables included in the patent data.

Suggested Citation

  • Jaehyun Choi & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Predictive Model of Technology Transfer Using Patent Analysis," Sustainability, MDPI, vol. 7(12), pages 1-21, December.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:12:p:15809-16195:d:59991
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    Cited by:

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    7. Yang, Guancan & Lu, Guoxuan & Xu, Shuo & Chen, Liang & Wen, Yuxin, 2023. "Which type of dynamic indicators should be preferred to predict patent commercial potential?," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
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    9. Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    10. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    11. Fernando Almeida, 2021. "Systematic Review On Academic Entrepreneurship Indicators," Entrepreneurship, Faculty of Economics, SOUTH-WEST UNIVERSITY "NEOFIT RILSKI", BLAGOEVGRAD, vol. 9(2), pages 7-22.
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    14. Hong Gong & Shan Peng, 2018. "Effects of patent policy on innovation outputs and commercialization: evidence from universities in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(2), pages 687-703, November.
    15. Bangjuan Wang & Weisheng Mao & Junxian Piao & Chengliang Liu, 2023. "Does external linkage stimulate innovation capacity? The analysis based on “dual‐pipelines” framework," Papers in Regional Science, Wiley Blackwell, vol. 102(3), pages 613-633, June.
    16. Jongchan Kim & Joonhyuck Lee & Gabjo Kim & Sangsung Park & Dongsik Jang, 2016. "A Hybrid Method of Analyzing Patents for Sustainable Technology Management in Humanoid Robot Industry," Sustainability, MDPI, vol. 8(5), pages 1-14, May.
    17. Junseok Lee & Ji-Ho Kang & Sunghae Jun & Hyunwoong Lim & Dongsik Jang & Sangsung Park, 2018. "Ensemble Modeling for Sustainable Technology Transfer," Sustainability, MDPI, vol. 10(7), pages 1-15, July.
    18. Tsouchnika, Maria & Smolyak, Alex & Argyrakis, Panos & Havlin, Shlomo, 2022. "Patent collaborations: From segregation to globalization," Journal of Informetrics, Elsevier, vol. 16(1).
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