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A Patent Analysis for Sustainable Technology Management

Author

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  • Junhyeog Choi

    (Department of Secretarial Management, Kimpo University, Gyeonggi 10020, Korea)

  • Sunghae Jun

    (Department of Statistics, Cheongju University, Chungbuk 28503, Korea)

  • Sangsung Park

    (Graduate School of Management of Technology, Korea University, Seoul 02841, Korea)

Abstract

Technology analysis (TA) is an important issue in the management of technology. Most R&D (Research & Development) policies have depended on diverse TA results. Traditional TA results have been obtained through qualitative approaches such as the Delphi expert survey, scenario analysis, or technology road mapping. Although they are representative methods for TA, they are not stable because their results are dependent on the experts’ knowledge and subjective experience. To solve this problem, recently many studies on TA have been focused on quantitative approaches, such as patent analysis. A patent document has diverse information of developed technologies, and thus, patent is one form of objective data for TA. In addition, sustainable technology has been a big issue in the TA fields, because most companies have their technological competitiveness through the sustainable technology. Sustainable technology is a technology keeping the technological superiority of a company. So a country as well as a company should consider sustainable technology for technological competition and continuous economic growth. Also it is important to manage sustainable technology in a given technology domain. In this paper, we propose a new patent analysis approach based on statistical analysis for the management of sustainable technology (MOST). Our proposed methodology for the MOST is to extract a technological structure and relationship for knowing the sustainable technology. To do this, we develop a hierarchical diagram of technology for finding the causal relationships among technological keywords of a given domain. The aim of the paper is to select the sustainable technology and to create the hierarchical technology paths to sustainable technology for the MOST. This contributes to planning R&D strategy for the sustainability of a company. To show how the methodology can be applied to real problem, we perform a case study using retrieved patent documents related to telematics technology.

Suggested Citation

  • Junhyeog Choi & Sunghae Jun & Sangsung Park, 2016. "A Patent Analysis for Sustainable Technology Management," Sustainability, MDPI, vol. 8(7), pages 1-13, July.
    • Handle: RePEc:gam:jsusta:v:8:y:2016:i:7:p:688-:d:74180
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    References listed on IDEAS

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    Cited by:

    1. Sangsung Park & Sunghae Jun, 2017. "Technology Analysis of Global Smart Light Emitting Diode (LED) Development Using Patent Data," Sustainability, MDPI, vol. 9(8), pages 1-15, August.
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    3. Vasile Gherheș & Ciprian Obrad, 2018. "Technical and Humanities Students’ Perspectives on the Development and Sustainability of Artificial Intelligence (AI)," Sustainability, MDPI, vol. 10(9), pages 1-16, August.
    4. David Urbano & Andreu Turro & Sebastian Aparicio, 2020. "Innovation through R&D activities in the European context: antecedents and consequences," The Journal of Technology Transfer, Springer, vol. 45(5), pages 1481-1504, October.
    5. Sangsung Park & Sunghae Jun, 2020. "Sustainable Technology Analysis of Blockchain Using Generalized Additive Modeling," Sustainability, MDPI, vol. 12(24), pages 1-15, December.
    6. Rafael Lizarralde & Jaione Ganzarain & Mikel Zubizarreta, 2020. "Assessment and Selection of Technologies for the Sustainable Development of an R&D Center," Sustainability, MDPI, vol. 12(23), pages 1-23, December.
    7. Sunghae Jun, 2019. "Bayesian Structural Time Series and Regression Modeling for Sustainable Technology Management," Sustainability, MDPI, vol. 11(18), pages 1-12, September.
    8. 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.
    9. 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.
    10. Juhwan Kim & Sunghae Jun & Dongsik Jang & Sangsung Park, 2018. "Sustainable Technology Analysis of Artificial Intelligence Using Bayesian and Social Network Models," Sustainability, MDPI, vol. 10(1), pages 1-12, January.
    11. Daiho Uhm & Jea-Bok Ryu & Sunghae Jun, 2017. "An Interval Estimation Method of Patent Keyword Data for Sustainable Technology Forecasting," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    12. Venkatesh Mani & Catarina Delgado & Benjamin T. Hazen & Purvishkumar Patel, 2017. "Mitigating Supply Chain Risk via Sustainability Using Big Data Analytics: Evidence from the Manufacturing Supply Chain," Sustainability, MDPI, vol. 9(4), pages 1-21, April.
    13. Sangsung Park & Sunghae Jun, 2020. "Patent Keyword Analysis of Disaster Artificial Intelligence Using Bayesian Network Modeling and Factor Analysis," Sustainability, MDPI, vol. 12(2), pages 1-11, January.
    14. Sunghae Jun, 2018. "Bayesian Count Data Modeling for Finding Technological Sustainability," Sustainability, MDPI, vol. 10(9), pages 1-12, September.
    15. Jong-Min Kim & Bainwen Sun & Sunghae Jun, 2019. "Sustainable Technology Analysis Using Data Envelopment Analysis and State Space Models," Sustainability, MDPI, vol. 11(13), pages 1-19, June.
    16. Juhyun Lee & Sangsung Park & Jiho Kang, 2021. "Introducing Patents with Indirect Connection (PIC) for Establishing Patent Strategies," Sustainability, MDPI, vol. 13(2), pages 1-15, January.
    17. 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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