A Study on Analyzing Innovation Efficiency in Service Sector of Korea

우리나라 서비스부문의 혁신효율성 분석에 관한 연구

  • Choi, Joung-In (Department of Management of Technology, Sungkyunkwan University) ;
  • Gwon, Seong-Hoon (Department of Management of Technology, Sungkyunkwan University) ;
  • Song, Sung-Hwan (R&D Policy Team, Korea Institute of Oriental Medicine) ;
  • Hwang, Seog-Won (Economic Analysis Research Division, Science and Technology Policy Institute)
  • 최정인 (성균관대학교 기술경영학과) ;
  • 권성훈 (성균관대학교 기술경영학과) ;
  • 송성환 (한국한의학연구원 연구정책팀) ;
  • 황석원 (과학기술정책연구원 경제분석연구단)
  • Received : 2009.07.20
  • Accepted : 2009.11.04
  • Published : 2009.12.01


One of primary assumptions on DEA is that all DMUs for evaluation should be homogeneous. In comparative analysis among DMUs with relative efficiency measurement, it should be evaluated under identical conditions by ruling out external environmental influences. In this study, a measurement of innovation efficiency using the three-stage approach is performed. The approach employs DEA to measure relative efficiency and Tobit regressions to control external variables affecting innovation activity. The approach applied to firms in Korean Innovation Survey: Service Sector 2003 and 2006. Final efficiency scores of the approach represent net efficiency of the innovation. This study found that there is a increasing on technical efficiency of third stage, and it has difference with first stage significantly. Besides, a decrease on standard deviation of third stage is found. It means DMUs biased lower due to unfavorable condition and ones biased higher due to favorable condition are fallen into an identical operating environment through the approach. A measurement of net efficiency, excluding external effects, ensures the homogeneity of DMUs so that improves the reliability in terms of its analysis results. This study is expected to provide a direction and to be a valuable reference to further evaluation of innovation performance in Korean service sector.


  1. Afriat, S. (1972), Efficiency Estimation of Production Functions, International Economic Review, 13(3), 563-598
  2. Banker, R. D., Charnes, A., and Cooper, W. W. (1984), Models for Estimating Technical and Scale Efficiencies, Management Science, 30(9), 1078-1092 https://doi.org/10.1287/mnsc.30.9.1078
  3. Byrnes, P., Färe, R., and Grosskop, S. (1984), Measuring Productive Efficiency: An Application to Illinois Strip Mines, Management Science, 30(6). 671-681 https://doi.org/10.1287/mnsc.30.6.671
  4. Chapelle, K. and Plane, P. (2005), Productive Efficiency in the Ivorian Manufacturing Sector: An Exploratory Study Using a Data Envelopment Analysis Approach, The Developing Economies, 43(4), 450-471 https://doi.org/10.1111/j.1746-1049.2005.tb00954.x
  5. Charnes, A., Cooper, W. W., and Rhodes, E. (1978), Measuring Efficiency of Decision Making Units, European Journal of Operational Research, 1(6), 429-444 https://doi.org/10.1016/0377-2217(78)90138-8
  6. Chen, Y., Yang, Z., Shu, F., Hu, Z., Meyer, M., and Bhattacharya, S. (2009). A Patent Based Evaluation of Technological Innovation Capability in Eight Economic Regions in PR China, World Patent Information, 31(2), 104-110 https://doi.org/10.1016/j.wpi.2008.06.010
  7. Chudnovsky, D., Lopez, A., and Pupato, G. (2006), Innovation and Productivity in Developing Countries: A Study of Argentine Manufacturing Firms’ Behavior (1992~2001), Research Policy, 35(2), 266-288 https://doi.org/10.1016/j.respol.2005.10.002
  8. Comanor, W. S. and Scherer, F. M. (1969), Patent Statistics as a Measure of Technical Change, The Journal of Political Economy, 77(3), 329-398 https://doi.org/10.1086/259519
  9. Drake, L., Gall, M. J. B., and Simper, R. (2006), The Impact of Macroeconomic and Regulatory Factors on Bank Efficiency: A Non-parametric Analysis of Hong Kong’s Banking System, Journal of Banking and Finance, 30(5), 1443-1466 https://doi.org/10.1016/j.jbankfin.2005.03.022
  10. Ernst, H. (1995), Patenting Strategies in the German Mechanical Engineering Industry and Their Relationship to Firm Performance, Technovation, 15(4), 225-240 https://doi.org/10.1016/0166-4972(95)96605-S
  11. Farrell, M. J. (1957), The Measurement of Productive Efficiency, Journal of the Royal Statistical Society, Series A (General), 120(3), 253-290 https://doi.org/10.2307/2343100
  12. Fried, H. O., Schmidt, S. S., and Yaisawarng, S. (1999), Incorporating the Operating Environment into a Nonparametric Measure of Technical Efficiency, Journal of Productivity Analysis, 12(3), 249-267 https://doi.org/10.1023/A:1007800306752
  13. Fu, X. (2008), Foreign Direct Investment, Absorptive Capacity and Regional Innovation Capabilities: Evidence from China, Oxford Development Studies, 36(1), 89-110 https://doi.org/10.1080/13600810701848193
  14. Greenhalgh, C. and Rogers, M. (2006), The Value of Innovation: The Interaction of Competition, R&D and IP, Research Policy, 35(4), 562-580 https://doi.org/10.1016/j.respol.2006.02.002
  15. Hoffman, K., Parejo, M., Bessant, J., and Perren, L. (1998), Small Firms, R&D, Technology and Innovation in the UK: A Literature Review, Technovation, 18(1), 39-55 https://doi.org/10.1016/S0166-4972(97)00102-8
  16. Huergo, E. (2006), The Role of Technological Management as a Source of Innovation: Evidence from Spanish Manufacturing Firms, Research Policy, 35(9), 1377-1388 https://doi.org/10.1016/j.respol.2006.07.005
  17. Hsu, F. M. and Hsueh, C. C. (2009), Measuring Relative Efficiency of Government-sponsored R&D Projects: A Three-stage Approach, Evaluation and Program Planning, 32(2), 178-186 https://doi.org/10.1016/j.evalprogplan.2008.10.005
  18. Kim, J. B., Kim, W. J., and Cho, N. W. (2008), An Efficiency Evaluation among Manufacturing Processes using Hybrid DEA/AHP Model, IE Interfaces, 21(3), 302-311
  19. Luukkonen, T. (2000), Additionality of EU Framework Programmes, Research Policy, 29(6), 711-724 https://doi.org/10.1016/S0048-7333(99)00041-4
  20. Nelson, A. J. (2009), Measuring Knowledge Spillovers: What Patents, Licenses and Publications Reveal about Innovation Diffusion, Research Policy, 38(6), 994-1005 https://doi.org/10.1016/j.respol.2009.01.023
  21. Park, S. and Choi, T. (2009), A Study on the analyzing impact factors on production of Korea S&T knowledge, 2009 Proceedings of Korea Technology Innovation Society, 165-177
  22. Raggi, A. (1993), Technological Growth in the Italian Economy: Some Indicators Compared, Technovation, 13(1), 3-15 https://doi.org/10.1016/0166-4972(93)90010-S
  23. Santarelli, E. and Piergiovanni, R. (1996), Analyzing Literature-based Innovation Output Indicators: The Italian Experience, Research Policy, 25(5), 689-711 https://doi.org/10.1016/0048-7333(95)00849-7
  24. Science and Technology Policy Institute (2004), Korean Innovation Survey 2003 : Service Sector, Seoul, Korea: STEPI
  25. Science and Technology Policy Institute (2006), Report on the Korean Innovation Survey 2006: The Services Sector, Seoul, Korea: STEPI
  26. Sher, P. J. and Yang, P. Y. (2005), The Effects of Innovative Capabilities and R&D Clustering on Firm Performance: The Evidence of Taiwan’s Semiconductor Industry, Technovation, 25(1), 33-43 https://doi.org/10.1016/S0166-4972(03)00068-3
  27. Sohn, S-Y. and Joo, Y-G. (2004), Data Envelopment Analysis and Logistic Model for BRAIN KOREA 21, IE Interfaces, 17(3), 249-260
  28. Teece, D. (1986), Profiting from Technological Innovation: Implications for Integration, Collaboration, Licensing and Public Policy, Research Policy, 15(6), 285-305 https://doi.org/10.1016/0048-7333(86)90027-2
  29. Uzun, A. (2001), Technological Innovation Activities in Turkey: The Case of Manufacturing Industry, 1995~1997, Technovation, 21(3), 189-196 https://doi.org/10.1016/S0166-4972(00)00033-X
  30. Wang, E. C. and Huang, W. (2007), Relative Efficiency of R&D Activities: A Cross-Country Study Accounting for Environmental Factors in the DEA Approach, Research Policy, 36(2), 260-273 https://doi.org/10.1016/j.respol.2006.11.004