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An Analysis of the Determinants of Government-Funded Defense Companies using a Decision Tree

의사결정나무를 활용한 방산육성지원 수혜기업 결정요인 분석

  • Gowoon Jeon (Department of Management of Technology, Gyeongsang National University / KRIT) ;
  • Seulah Baek (Department of Industrial System Engineering, Gyeongsang National University) ;
  • Jeonghwan Jeon (Department of Industrial System Engineering, Gyeongsang National University) ;
  • Donghee Yoo (Department of Management Information Systems(Bus & Econ Res Inst.), Gyeongsang National University)
  • 전고운 (경상국립대학교 기술경영학과 / 국방기술진흥연구소) ;
  • 백슬아 (경상국립대학교 산업시스템공학부) ;
  • 전정환 (경상국립대학교 산업시스템공학부) ;
  • 유동희 (경상국립대학교 경영정보학과)
  • Received : 2023.10.19
  • Accepted : 2024.01.05
  • Published : 2024.02.05

Abstract

This study attempted to analyze the factors that influence the participation of beneficiary companies in the government's defense industry promotion support project. To this end, experimental data were analyzed by constructing a prediction model consisting of highly important variables in beneficiary company decisions among various company information using the decision tree model, one of the data mining techniques. In addition, various rules were derived to determine the beneficiary companies of the government's support project using the analysis results expressed as decision trees. Three policy measures were presented based on the important rules that repeatedly appear in different predictive models to increase the effect of the government's industrial development. Using the analysis methods presented in this study and the determinants of the beneficiary companies of the government support project will help create a sustainable future defense industry growth environment.

Keywords

References

  1. Korea Research Institute for Defense Technology Planning and Advancement(KRIT), Global defense market yearbook, 2022.
  2. Defense Acquisition Program Administration(DAPA), 23-27 Basic plan for defense industry development, 2023.
  3. S. Oh, P. Jang, "The Effect of Government R&D Support on Manufacturing Firms' Innovation Activities and Innovation Performance," Journal of Korea Technology Innovation Society, Vol. 23, No. 5, pp. 941-966, 2020. https://doi.org/10.35978/jktis.2020.10.23.5.941
  4. Y. Lim, J. Jeon, "Analyzing the Performance of Defense R&D Projects based on DEA," Journal of the Korea Institute of Military Science and Technology, Vol. 22, No. 1, pp. 106-123, 2019.
  5. J. Kang, K. Cho, "An Analysis of the Effect of Government Support on Automation and Smart Factory," Journal of Korea Technology Innovation Society, Vol. 21, No. 2, pp. 738-766, 2018.
  6. D. Yoon, H. Jung, K. Park, S. Lee, J. Lee, "The Effect of Government SME Support Programs(2008- 2017): Focused on Consulting Support Programs," Korean Journal of Business Administration, Vol. 33, No. 9, pp. 1597-1623, 2020.
  7. Rosario, C., Varum, C., Botelho, A. "Impact of Public Support for Innovation on Company Performance: Review and Meta-Analysis," Journal of Sustainability, Vol. 14, No. 8, pp. 4718-4731, 2022. https://doi.org/10.3390/su14084718
  8. H. Kim, H. Lee, "Performance Analysis of Government Support Projects for the Future Mobility: Using PSM and DID Methodology," Journal of Korea  Technology Innovation Society, Vol. 26, No. 2, pp. 245-267, 2023. https://doi.org/10.35978/jktis.2023.4.26.2.245
  9. S. Hwang, Y. Yim, "Development of a methodology for evaluating the economic feasibility of R&D projects considering the decision tree," Science and Technology Policy Institute, Korea, pp. 1-106, 2007.
  10. M. Song, S. Lee, "Analysis of Netflix's Preference for the Composition of Advertising Plans : Focusing on the Analysis of Decision Trees," Advertising Research, Vol. 136, pp. 46-74, 2023. https://doi.org/10.16914/ar.2023.136.46
  11. K. Joo, J. Hwang, "The market segmentation in the context of independent coffee shops using answer tree CHAID algorithm: Focusing on store type and Generation Z consumers," Korean Journal of Hospitality & Tourism, Vol. 30, No. 7, pp. 167- 181, 2021. https://doi.org/10.24992/KJHT.2021.10.30.07.167
  12. M. Seo, "Using Decision Tree Model to Identify the Subgroups with Lower Levels of Learning Achievement by Non-face-to-face Class at Mixed Lecture," Journal of the Korea Academia-Industrial cooperation Society, Vol. 23, No. 4, pp. 326-334, 2022. https://doi.org/10.5762/KAIS.2022.23.4.326
  13. H. Kim, H. Moon, D. Lee, M. Hwang, Y. Kim, "Developing an Expert System for Close Combat using Decision Tree," Journal of the Military Operations Research Society of Korea(MORS-K), Vol. 36, No. 3, pp. 83-97, 2010.
  14. J. Kang, D. Yim, B. Choi, "A Study on Methodology for Air Target Dynamic Targeting Applying Machine Learning," Journal of the Korea Institute of Military Science and Technology, Vol. 22 No. 4, pp. 555-566, 2019.
  15. M. Dash, H. Liu, "Feature Selection for Classification," Intelligent Data Analysis, Vol. 1, No. 3, pp. 131-156, 1997. https://doi.org/10.3233/IDA-1997-1302
  16. I. H. Witten, E, Frank, "Data mining: Practical Machine Learning Tools and Techniques," Morgan Kaufmann Publishers, 2005.