DOI QR코드

DOI QR Code

Empirical analysis of strategy selection for the technology leading and technology catch-up in the IT industry

  • Byung-Sun Cho (Technology Policy Research Division, Electronics and Telecommunications Research Institute) ;
  • Sang-Sup Cho (Digital Technology Management Department, Hoseo University) ;
  • Sung-Sik Shin (Technology Policy Research Division, Electronics and Telecommunications Research Institute) ;
  • Gang-hoon Kim (Department of Health Service, Wonkwang Health Science University)
  • 투고 : 2021.11.30
  • 심사 : 2022.04.18
  • 발행 : 2023.04.20

초록

R&D strategies of companies with low and high technological levels are discussed based on the concept of technology convergence and divergence. However, empirically detecting enterprise technology convergence in the distribution of enterprise technology (total productivity increase) over time and identifying key change factors are challenging. This study used a novel statistical indicator that captures the internal technology distribution change with a single number to clearly measure the technology distribution peak as a change in critical bandwidth for enterprise technology convergence and presented it as evidence of each technology convergence or divergence. Furthermore, this study applied the quantitative technology convergence identification method. Technology convergence appeared from the separation of total corporate productivity distribution of 69 IT companies in Korea in 2019-2020 rather than in 2015-2016. Results indicated that when the total technological level was separated from the technology leading and technology catch-up, IT companies were found to be pursuing R&D strategies for technology catch-up.

키워드

과제정보

This research was supported by Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korean government. [21ZR1400, Research on technology policy for national intelligence, and 21HH6400, Testing environment infrastructure of 5G infra equipment].

참고문헌

  1. D. T. Quah, Empirics for growth and distribution: Stratification, polarization and convergence clubs, J. Econ. Growth 2 (1996), no. 1, 27-59. https://doi.org/10.1023/A:1009781613339
  2. W. Baumol, Productivity growth, convergence, and welfare, Am. Econ. Rev. 76 (1986), 1072-1085.
  3. D. T. Quah, Empirics for growth and distribution: Stratification, polarization and convergence clubs, J. Econ. Growth 2 (1997), no. 1, 27-59. https://doi.org/10.1023/A:1009781613339
  4. O. Galor, Convergence? Inferences from theoretical models, Econ. J. 106 (1996), no. 437, 1056-1069. https://doi.org/10.2307/2235378
  5. B. Silverman, Density Estimation for Statistics and Data Analysis, Chapman & Hall, 1986.
  6. M. Bianchi, Testing for convergence: Evidence from non-parametric multimodality tests, J. Appl. Economet. 12 (1997), no. 4, 393-409. https://doi.org/10.1002/(SICI)1099-1255(199707)12:4<393::AID-JAE447>3.0.CO;2-J
  7. M. Krause, The millennium peak in club convergence: A New Look at Distributional Changes in The Wealth of Nations, J. Appl. Economet. 32 (2017), no. 3, 621-642. https://doi.org/10.1002/jae.2542
  8. C. Freeman and C. Perez, Structural crises of adjustment, business cycles and investment behaviour, In Technical change and economic theory, G. Dosi et al. (eds.), Pinter Publishers, London New York, 1988, 38-66.
  9. R. Kapoor and R. Adner, Technology intredependence and the evolution of semiconductor lithography, Solid State Technol. 50 (2007), 51-54.
  10. S. Broring and L. M. Cloutier, Value creation in new product development within converging value chains, Br. Food J. (2008), no. 1, 76-97.
  11. S. Breschi and F. Lissoni, Knowledge relatedness in firm technological diversification, Res. Pol. 32 (2003), 69-87. https://doi.org/10.1016/S0048-7333(02)00004-5
  12. R. Narula and J. Dunning, Explaining international R&D alliances and the Role of Governments, Int. Business Rev. 7 (1998), 377-397. https://doi.org/10.1016/S0969-5931(98)00019-5
  13. J. Barney, Firm resources and sustained competitive advantage, J. Manag. 17 (1991), 99-120.
  14. G. Tassey, R&D in the Modern Economy, Northampton USA, In The technology imperative. Edward Elgar, 2007, 91-210.
  15. S. McEvily and B. Chakravarthy, The persistence of Knowledge based advantage, Strat. Manag. J. 23 (2002), 285-305. https://doi.org/10.1002/smj.223
  16. J. Hagedoorn, Inter-firm R&D partnerships: an overview of major trends and patterns since 1960, Research Policy 31 (2002), 477-492. https://doi.org/10.1016/S0048-7333(01)00120-2
  17. M. Dodourova, Alliances as strategic tools: a cross-industry study of partnership planning, formation and success, Manag. Decis. 47 (2009), no. 5, 831-844. https://doi.org/10.1108/00251740910960150
  18. G. N. Von Tunzelmann, Technology generation. Technology use and economic growth, Eur. Rev. Econo. Hist. 4 (1999), no. 2, 121-146. https://doi.org/10.1017/S1361491600000022
  19. F. Rothaermel and D. L. Deeds, Exploration and exploitation alliances in biotechnology, Strat. Mgmt. J. 25 (2004), 201-221. https://doi.org/10.1002/smj.376
  20. R. Fare, S. Grosskopf, M. Norris, and Z. Zhang, Productivity growth, technical progress, and efficiency change in industrialized countries, Am. Econ. Rev. 84 (1994), 66-83.
  21. E. Thanassoulis, Introduction to the Theory and Application of Data Analysis, Springer Science & Business Media, 2001.
  22. A. W. Bowman and A. Azzalini, Applied Smoothing Techniques for Data Analysis, Oxford University Press, 1997.
  23. S. Y. Hee and S. H. I. N. Sung Sik, Testing environment infrastructure of 5G infra equipment, ETRI (2021).
  24. D. J. Henderson, C. F. Parmeter, and R. Russell, Modes, weighted modes, and calibrated modes, J. Appl. Economet. 23 (2008), no. 5, 607-638. https://doi.org/10.1002/jae.1023
  25. M. Pittau, R. Roberto, and P. A. Johnson, Mixture models, convergence clubs, and ploarization, Rev. Income Wealth 56 (2010), 102-122. https://doi.org/10.1111/j.1475-4991.2009.00365.x