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A Novel Forecasting Methodology for Sustainable Management of Defense Technology

Author

Listed:
  • Sungchul Kim

    (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

A dynamic methodology for sustainable management of defense technology is proposed to overcome the limitations of the static methodology, which involves comparative analysis based on the criterion of the highest technology level and has limitations for time series analysis, because the country with the highest level undergoes technical changes over time. To address these limitations, this study applies a technology growth model for a dynamic analysis of the Delphi result. An effective method using patents is also proposed to verify and adjust the analysis results. First, technology levels of the present and future are examined by the Delphi technique, and the growth curve is extracted based on the technology growth model. Second, the technology growth curve based on patents is extracted using the annual number of unexamined and registered patents related to the technology. Lastly, the statistical significance of the two growth curves is examined using regression analysis. Then the growth curves are adjusted by the rate of increase in patents. This methodology could provide dynamic technology level data to facilitate sustainable management of defense technology. The results could be useful to research institutions, as they establish strategies for securing technologies in defense or private domains.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:7:y:2015:i:12:p:15844-16736:d:60832
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    References listed on IDEAS

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    8. 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.
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    10. 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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