The Journal of Fisheries Business Administration (수산경영론집)

The Korean Society of Fisheries Business Administration (한국수산경영학회)
권호 표지
DOI QR코드

DOI QR Code

Case Study on Measuring Technology Level Applying Growth Curve Model: Three Core Areas of Fishery Science and Technology

성장곡선 모형 적용을 통한 기술수준평가 사례 연구 : 특정 수산과학기술 분야를 중심으로

  • Kim, Wan-Min (Department of Business Administration, Pukyong National University) ;
  • Park, Ju-Chan (Industry Policy Team, Policy Planning Agency, Busan Techno-Park) ;
  • Bark, Pyeng-Mu (Department of Systems Management & Engineering/Graduate Program of Management of Technology, Pukyong National University)
  • 김완민 (부경대학교 경영대학 경영학부) ;
  • 박주찬 (부산테크노파크 정책기획단) ;
  • 박병무 (부경대학교 공과대학 시스템경영공학부)
  • Received : 2015.10.12
  • Accepted : 2015.12.09
  • Published : 2015.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this paper is to discuss possibilities of applying growth curve models, such as Logistic, Log-Logistic, Log-Normal, Gompertz and Weibull, to three specific technology areas of Fishery Science and Technology in the process of measuring their technology level between Korea and countries with the state-of-the art level. Technology areas of hazard control of organism, environment restoration, and fish cluster detect were selected for this study. Expert panel survey was conducted to construct relevant panel data for years of 2013, 2016, and a future time of approaching the theoretical maximum technology level. The size of data was 70, 70 and 40 respectively. First finding is that estimation of shape and location parameters of each model was statistically significant, and lack-of-fit test using estimated parameters was statistically rejected for each model, meaning all models were good enough to apply for measuring technology levels. Second, three models other than Pearl and Gompertz seemed very appropriate to apply despite the fact that previous case studies have used only Gompertz and Pearl. This study suggests that Weibull model would be a very valid candidate for the purpose. Third, fish cluster detect technology level is relatively higher for both Korea and a country with the state-of-the-art among three areas as of 2013. However, all three areas seem to be approaching their limits(highest technology level point) until 2020 for countries with the state-of-the-art. This implies that Korea might have to speed up her research activities in order to catch up them prior to 2020. Final suggestion is that future study may better apply various and more appropriate models respectively considering each technology characteristics and other factors.

Keywords

References

  1. Bark, P. M. (2007a), "A Theoretical Approach and Its Application for a Dynamic Method of Estimating and Analyzing Science and Technology Levels : Case Application on Ten Core Technologies for the Next Generation Growth Engine," The Korea Technology Innovation Society, 10:4, 654-686.
  2. Bark, P. M. (2007b), A Theoretical Approach and Its Application for a New Method of Measuring and Analyzing Science and Technology Level, Report 2007-17, Science & Technology Policy Institute.
  3. Bark, P. M. et al. (2010), A Study on Science and Technology Level Appraisal with a New Approach and Development of Related Theory, Korea Institute of Science & Technology Evaluation and Planning.
  4. Bark, P. M, Kim, W. M., Jang, D. H., Yi, C. G., Park, J. O., Hwang, J. B., Park, J. C., Lee, H. Y., Kim, J. C. and An, S. J. (2013), Technology Level Assessment of Fishery Science and Technology, National Fisheries Research & Development Institute Pukyong National University.
  5. Brain, P. and Cousens, R. (1989), "An Equation to Describe Dose Responses Where There is Stimulation of Growth at Low Doses," Weed Research, 29, 93-96. https://doi.org/10.1111/j.1365-3180.1989.tb00845.x
  6. Byeon, S. C., Ryu, J. Y. and Son, S. H. (2008), A Dynamic Technology Assessment Level Using a Technology Growth Curve Model, Issue Paper 2008-13, Korea Institute of Science & Technology Evaluation and Planning.
  7. Cho, H. S. et al. (2010), A Study on R&D Master Plan for Korea National Fisheries Research & Development Institute, National Fisheries Research & Development Institute, TechnologyValue Corporation.
  8. Choi, J. H., Choi, B. H., Yang, W. S. and Kim, E. E. (2000), "Population Forecasting System Based on Growth Curve Models," Korea Journal of Population Studies, 23 (1), 197-215.
  9. Chung, K. H. et al. (2001), International Comparison Study on the Result of Science and Technology Foresight : Case of Korea, Japan, and Germany, Korea Institute of Science & Technology Evaluation and Planning.
  10. Gompertz, B. (1825), "On the Nature of the Function Expressive of Human Morality, and on a New Mode to Determining the Value of Life Contingencies," Philosophical Transactions of the Royal Society of London, 115, 513-585. https://doi.org/10.1098/rstl.1825.0026
  11. Han, M. K., Kim, B. S., Ryu, J. Y. and Byeon, S. C. (2010), "Technology Level Evaluation Based On Technology Growth Model and Its Implication? In Case of Biochip and Biosensor Technology," The Korea Technology Innovation Society, 13:2, 252-281.
  12. Jin, J. H. and Kim, J. S. (2013) "Forecasting Natural Gas Demand Using Nonlinear Model," The Korean Society of Mineral and Energy Resources Engineers, 50 (3), 399-408. https://doi.org/10.12972/ksmer.2013.50.3.399
  13. Kim, B. S. et al. (2009), A Study on Dynamic Method of Estimating Technology Levels Based on the Technology Growth Model, Korea Institute of Science & Technology Evaluation and Planning.
  14. Kim, B. S. et al. (2010), A Study on Technology Capacity and Impact of Marine Science & Technology, KISTEP & KIMST.
  15. Kim, B. S. (2010),"A Case of Forecast-based Technology Evaluation and Its Implications," International Journal of Technology and Planning, 6:4, 317-325. https://doi.org/10.1504/IJTIP.2010.038227
  16. Kim, I. H., Chung, K. H. and Chung, H. S. (1999), A Study on the Science and Technology Level in Korea, Korea Institute of Science & Technology Evaluation and Planning.
  17. Lee, J. R. and An, S. J. (2014), Comparative Analysis of Fishery R&D between Korean and Japan after Mass Earthquake in East Japan, Issue Paper 2014-12, KISTEP.
  18. Medawar, P. B. (1940), "Growth, Growth Energy, and Ageing of the Chicken's Heart," Proc. Soc. London, 129, 332-355.
  19. Ministry of Science, ICT and Future Planning (2015), The Framework Act On Science and Technology, Science and Technology Policy Bureau, 02-2110-2523, enforce 2015.7.01.
  20. MOF (2014), Act on the Promotion of Science and Technology for Food, Agriculture, Forestry and Fisheries, Ministry of Oceans and Fisheries.
  21. Panik, Michael, J. (2014), Growth Curve Modeling : The Theory and Applications, Preface and Chapter 3, John Wiley and Sons, Inc., Hoboken, New Jersey.
  22. Park, Y. T. (2007), Management of Technological Knowledge for Next Generation Innovation, 2nd ed., Life & Power Press.
  23. Ritz, C. and Strebig, J. (2013), DRC : Analysis of Dose-Response Curve Data, R. Package Version, 23-96.
  24. Robertson, T. B. (1908), "On the Normal Rate of Growth of an Individual and Its Biochemical Significance," Roux' Arch, Entwicklungsmech, Organismen, 25, 581-614. https://doi.org/10.1007/BF02163864
  25. Ryu, J. Y. (2012), Dynamic Technology Level Evaluation Methodology and Forecasting based on Technology Growth Curve, Ph. D. Dissertation, Department of Management Science, Korea Advanced Institute of Science and Technology (KAIST).
  26. Seber, G. A. F. and Wild, C. J. (1989), Nonlinear Regression, New York: Wiley&Sons, 325-339.
  27. Weibull, W. (1951), "A Statistical Distribution Function of Wide Applicability," Journal of Applied Mechanics 18, 291-297.
  28. Wright, S. (1926), Book Review, Journal of American Statistical Association, 21, 493-497.
  29. R package, version 3.1.1 (http://www.R-project.org)