Fig. 1. Gross Domestic Spending on R&D of OECD countries, 2015. Source: OECD Homepage(https://data.oecd.org/rd/)
Table 1. OUTLINE OF SAMPLES
Table 2. DESCRIPTION OF VARIABLES
Table 3. DESCRIPTIVE STATISTICS AND CORRELATION ANALYSIS
Table 4. SUMMARY OF REGRESSION ANALYSES
참고문헌
- OECD Homepage. https://data.oecd.org/rd/grossdomestic-spending-on-r-d.htm
- W. Sul. (2017). A proposal of new policies for improving the performance analysis on government R&D expenditure in ICT industry. Journal of Digital Convergence, 15(5), 151-159. DOI : 10.14400/JDC.2017.15.5.151
- KISTEP(Korea Institute of S&T Evaluation and Planning). (2016). 2015 Survey of research and development in Korea.
- F. M. Hsu & C. C. Hsueh. (2009). Measuring relative efficiency of government-sponsored R&D projects: A three-stage approach, Evaluation and Program Planning, 32(2), 178-186. DOI : 10.1016/j.evalprogplan.2008.10.005
- S. H. Yoon. (2013). Analysis of application and diffusion effect factor of outcomes from national energy R&D project, Master dissertation, Sungkyunkwan University, Seoul.
- H. M. Kim, J. W. Yoo & J. S. Ryu. (2013). A study on the relationship between cooperative factors of national R&D projects and performance: Focusing on the moderating effect of projects' characteristics. Korean Journal of Business Administration, 26(3), 695-718.
- H. S. Lee, J. S. Lee & J. M. Park. (2015). Technological performance analyses of SMEs based on type of government R&D support, Korea Technology Innovation Society, 18(1), 73-97.
- J. Y. Choi & K. B. Kang. (2016). Factors that influence the technological performance of national R&D programs: in the case of the machinery and chemical technology R&D. Journal of Korea Technology Innovation Society, 19(1), 161-190.
- S. S. Choi, I. H. Oh & D. M. Lee. (2016). A study on R&D performance analysis of marine technology, Journal of Korean Society for Marine Environment & Energy, 19(2), 165-171. DOI : 10.7846/JKOSMEE.2016.19.2.165
- O. A. Carboni. (2017). The Effect of Public Support on Investment and R&D: An empirical evaluation on European manufacturing firms. Thchnological Forecasting & Social Change, 117, 282-295. DOI : 10.1016/j.techfore.2016.11.017