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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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    References listed on IDEAS

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    Cited by:

    1. 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.
    2. Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    3. 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.
    4. Joohyung Lim & Sungchul Choi & Chiehyeon Lim & Kwangsoo Kim, 2017. "SAO-Based Semantic Mining of Patents for Semi-Automatic Construction of a Customer Job Map," Sustainability, MDPI, vol. 9(8), pages 1-17, August.
    5. Liu, Weiwei & Tao, Yuan & Bi, Kexin, 2022. "Capturing information on global knowledge flows from patent transfers: An empirical study using USPTO patents," Research Policy, Elsevier, vol. 51(5).
    6. Dae Geon Kim & Sang Ok Choi, 2018. "Impact of Construction IT Technology Convergence Innovation on Business Performance," Sustainability, MDPI, vol. 10(11), pages 1-16, October.
    7. Annamaria Demarinis Loiotile & Francesco De Nicolò & Adriana Agrimi & Loredana Bellantuono & Marianna La Rocca & Alfonso Monaco & Ester Pantaleo & Sabina Tangaro & Nicola Amoroso & Roberto Bellotti, 2022. "Best Practices in Knowledge Transfer: Insights from Top Universities," Sustainability, MDPI, vol. 14(22), pages 1-23, November.
    8. Wanjiru Ruth Irungu & Xiaoguang Liu & Chuyu Han & Alvin Bomer & Wambui Ann Wanjiru, 2020. "Progress towards a World-Class Research University status: The case of Nanjing Agricultural University," Higher Education Studies, Canadian Center of Science and Education, vol. 10(1), pages 1-72, March.
    9. Alptekin Durmuşoğlu, 2017. "Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences," Sustainability, MDPI, vol. 9(5), pages 1-10, May.
    10. Jongchan Kim & Jaehyun Choi & Sangsung Park & Dongsik Jang, 2018. "Patent Keyword Extraction for Sustainable Technology Management," Sustainability, MDPI, vol. 10(4), pages 1-18, April.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. Tsouchnika, Maria & Smolyak, Alex & Argyrakis, Panos & Havlin, Shlomo, 2022. "Patent collaborations: From segregation to globalization," Journal of Informetrics, Elsevier, vol. 16(1).
    16. Yao-Chin Lin & Chun-Liang Chen & Cheng-Fu Chao & Wei-Hung Chen & Henry Pandia, 2020. "The Study of Evaluation Index of Growth Evaluation of Science and Technological Innovation Micro-Enterprises," Sustainability, MDPI, vol. 12(15), pages 1-25, August.
    17. 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).
    18. BangRae Lee & DongKyu Won & Jun-Hwan Park & LeeNam Kwon & Young-Ho Moon & Han-Joon Kim, 2016. "Patent-Enhancing Strategies by Industry in Korea Using a Data Envelopment Analysis," Sustainability, MDPI, vol. 8(9), pages 1-17, September.
    19. Sungchul Kim & Dongsik Jang & Sunghae Jun & Sangsung Park, 2015. "A Novel Forecasting Methodology for Sustainable Management of Defense Technology," Sustainability, MDPI, vol. 7(12), pages 1-17, December.
    20. Sangsung Park & Sunghae Jun, 2017. "Statistical Technology Analysis for Competitive Sustainability of Three Dimensional Printing," Sustainability, MDPI, vol. 9(7), pages 1-16, June.

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