Skip to main content

Advertisement

Log in

Correlation Between Transmission Power and Some Indicators Used to Measure the Knowledge-Based Economy: Case of Six OECD Countries

  • Published:
Journal of the Knowledge Economy Aims and scope Submit manuscript

Abstract

In this paper, we study the correlation between the transmission power and some indicators used to measure the knowledge-based economy. For the case study, we select six OECD countries (USA, Canada, France, Germany, Japan and South Korea) and six indicators (gross domestic expenditure for research and development (GERD), number of researchers, gross domestic product (GDP) growth rate, GDP per capita, Human Development Index (HDI) and total factor productivity (TFP)). The time series of the transmission power over a 10-year period (2001–2010) are built on the basis of publication data collected from the Web of Science. The correlation between transmission power and the selected indicators is computed. Results show that Japan and South Korea exhibit a positive strong correlation between transmission power and GERD on one hand and transmission power and number of researchers on the other hand. These two countries have the same pattern as regarding the transmission power and each of the selected indicators; other countries do not show any comparable pattern. The study concludes that the transmission power computed at national level only is not sufficient to measure the extent to which an economy is knowledge-based, because it does not take into account the synergy contributed at international level by a nation innovation actor.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

Similar content being viewed by others

Notes

  1. Since the term has been coined, conferences were held (e.g. by the European Union, cf. European Commission - EUROSTAT 2006; or OECD, cf. OECD & National Science Foundation, 1999) to discuss its measurement. Frameworks were set up to provide the concept with indicators (APEC Economic Committee 2000; Chen and Dahlman 2005; OECD 1996) and enable its measurement. Strategies and plans were formulated at international, regional and national levels (e.g. APEC Economic Committee 2000; Australian Bureau of Statistics and Trewin 2002; European Commission 2010a).

  2. cf. Godin (2006) for the case of OECD and Karahan (2012) for an overview of the indicators used by international organizations like OECD, the World Bank, the Asia-Pacific Economic Cooperation and the European Union.

  3. The OECD published several reports related to the knowledge-based economy (e.g. OECD 1996, 1999, 2013). It used up to 60 indicators with variations from one publication to another.

  4. The Word Bank established the Knowledge Economy Framework which built two indicators: the Knowledge Economy Index (KEI) and the Knowledge Index (KI) (Chen and Dahlman 2005).

  5. In the 2010 innovation scoreboard, the European Commission (2010b) published 26 statistics, but more recently, it has set up a composite index, the Summary Innovation Index—which summarizes the performance of a range of 25 different indicators (cf. European Commission 2014, p. 8).

  6. The Asian-Pacific Economic Cooperation defined the idealised knowledge-based economy under the name of Nikuda and fixed its characteristics (APEC Economic Committee 2000, pp. 3–16). The statistics are recognized as too idealistic.

  7. The United Nations Economic Commission for Europe (2002) suggested the Global Knowledge-Based Economy Index (GKEI) as a measure.

  8. The areas are networked access, networked learning, networked society, networked economy and networked policy. See http://www.readinessguide.org.

  9. https://data.oecd.org/lprdty/multifactor-productivity.htm.

  10. The HDI time series cover only the period 2005–2010. Indeed, the indicator is provided by interval of 5 years from 1980 to 2000 and for each year from 2005 to 2013 in the recent Human Development Report (UNDP 2014). So, for methodological reasons, we restrict data to the period 2005–2010 for the HDI.

  11. The databases searched were Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), Conference Proceedings Citation Index-Science (CPCI-S) and Conference Proceedings Citation Index-Social Science & Humanities (CPCI-SSH).

  12. Globalization after the end of the Cold War between 1990 and 2000 (cf. Leydesdorff and Sun 2009).

  13. The bilateral entropy values are not presented in this article.

References

  • Adams, J., King, C., Miyairi, N., & Pendlebury, D. (2010). Global research report: Japan. Philadelphia: Thomson Reuters.

    Google Scholar 

  • APEC Economic Committee. (2000). Towards knowledge-based economies in APEC (p. 204). Asia-Pacific Economic Cooperation.

  • Arvanitidis, P. A., & Petrakos, G. (2011). Defining knowledge-driven economic dynamism in the world economy: a methodological perspective. In P. Nijkamp & I. Siedschlag (Eds.), Innovation, growth and competitiveness: dynamic regions in the knowledge-based world economy (p. 380). New York: Springer.

    Google Scholar 

  • Australian Bureau of Statistics, & Trewin, D. (2002). Measuring a knowledge-based economy and society: an Australian framework (No. 1375.0) (p. 48). Camberra: Australian Bureau of Statistics.

    Google Scholar 

  • Bordons, M., & Gomez, I. (2000). Collaboration networks in science. In B. Cronin & H. B. Atkins (Eds.), A festschrift in honor of Eugene Garfield (pp. 197–213). Medford: Information Today.

    Google Scholar 

  • Chen, D. H., & Dahlman, C. J. (2005). The knowledge economy, the KAM methodology and World Bank operations. World Bank Institute Working Paper, (37256).

  • European Commission. (2010a). Europe 2020: a strategy for smart, sustainable and inclusive growth (communication from the commission no. COM(2010) 2020 final) (p. 35). Brussels: European Commission.

    Google Scholar 

  • European Commission. (2010b). European innovation scoreboard. Luxembourg: Office for Official Publications of the European Communities.

    Google Scholar 

  • European Commission. (2014). Innovation union scoreboard. Luxembourg: Office for Official Publications of the European Communities.

    Google Scholar 

  • European Commission - EUROSTAT. (2006). Conference on knowledge economy: challenges for measurement, Luxembourg, 8–9 December 2005. Luxembourg: European Commission, Joint Research Centre.

    Google Scholar 

  • Glänzel, W. (2001). National characteristics in international scientific co-authorship relations. Scientometrics, 51(1), 69–115.

    Article  Google Scholar 

  • Godin, B. (2006). The knowledge-based economy: conceptual framework or buzzword? The Journal of Technology Transfer, 31(1), 17–30.

    Article  Google Scholar 

  • Guns, R., & Rousseau, R. (2014). Recommending research collaborations using link prediction and random forest classifiers. Scientometrics, 101(2), 1461–1473. doi:10.1007/s11192-013-1228-9.

    Article  Google Scholar 

  • Ivanova, I. A., Strand, Ø., & Leydesdorff, L. (2014). Synergy cycles in the Norwegian innovation system: The relation between synergy and cycle values. Available at SSRN 2492456.

  • Karahan, Ö. (2012). Input-output indicators of knowledge-based economy and Turkey. Journal of Business, Economics & Finance, 1(2), 21–36.

    Google Scholar 

  • Katz, J. S., & Martin, B. R. (1997). What is research collaboration? Research Policy, 26, 1–26.

    Article  Google Scholar 

  • Kwon, K.-S., Park, H. W., So, M., & Leydesdorff, L. (2012). Has globalization strengthened South Korea’s national research system? National and international dynamics of the Triple Helix of scientific co-authorship relationships in South Korea. Scientometrics, 90(1), 163–176.

    Article  Google Scholar 

  • Leydesdorff, L. (2003). The mutual information of university-industry-government relations: an indicator of the Triple Helix dynamics. Scientometrics, 58(2), 445–467.

    Article  Google Scholar 

  • Leydesdorff, L., & Ivanova, I. A. (2014). Mutual redundancies in interhuman communication systems: steps towards a calculus of processing meaning. Journal of the Association for Information Science and Technology, 65(2), 386–399. doi:10.1002/asi.22973.

    Article  Google Scholar 

  • Leydesdorff, L., & Park, H. (2014). Can synergy in Triple Helix relations be quantified? A review of the development of the Triple Helix indicator. Triple Helix: A Journal of University-Industry-Government Innovation and Entrepreneurship, 1(1), 1–18. doi:10.1186/s40604-014-0004-z.

  • Leydesdorff, L., & Sun, Y. (2009). National and international dimensions of the Triple Helix in Japan: university-industry-government versus international co-authorship relations. Journal of the American Society for Information Science, 60(4), 778–788.

    Article  Google Scholar 

  • Leydesdorff, L., & Zhou, P. (2013). Measuring the knowledge-based economy of China in terms of synergy among technological, organizational, and geographic attributes of firms. Scientometrics, 98(3), 1703–1719.

    Article  Google Scholar 

  • Leydesdorff, L., Perevodchikov, E., & Uvarov, A. (2015). Measuring triple-helix synergy in the Russian innovation systems at regional, provincial, and national levels. Journal of the Association for Information Science and Technology, 66(6), 1229–1238. doi:10.1002/asi.23258.

    Article  Google Scholar 

  • Liu, Y. (2011). The diffusion of scientific ideas in time and indicators for the description of this process (Doctoral dissertation). Antwerp: University of Antwerp.

    Google Scholar 

  • Liu, Y., Rousseau, R., & Guns, R. (2013). A layered framework to study collaboration as a form of knowledge sharing and diffusion. Journal of Informetrics, 7(3), 651–664. doi:10.1016/j.joi.2013.04.002.

    Article  Google Scholar 

  • Mêgnigbêto, E. (2013). Triple Helix of university-industry-government relationships in west Africa. Journal of Scientometric Research, 2(3), 54–62. doi:10.4103/2320-0057.135413.

    Article  Google Scholar 

  • Mêgnigbêto, E. (2014a). Efficiency, unused capacity and transmission power as indicators of the Triple Helix of university-industry-government relationships. Journal of Informetrics, 8(1), 284–294. doi:10.1016/j.joi.2013.12.009.

    Article  Google Scholar 

  • Mêgnigbêto, E. (2014b). Information flow between west African Triple Helix actors. ISSI Newsletter, 10(1), 14–20.

    Google Scholar 

  • Mêgnigbêto, E. (2014c). Information flow within west African innovation systems. Triple Helix: A Journal of University-Industry-Government Innovation and Entrepreneurship, 1(1), 1–13. doi:10.1186/s40604-014-0005-y.

  • Mêgnigbêto, E. (2015). Profiles of six OECD countries with regard to mutual information and transmission power. ISSI Newsletter, 11(1), 16–23.

    Google Scholar 

  • OECD. (1996). The knowledge-based economy (No. OCDE/GD(96)102) (p. 46). Paris: OECD.

    Google Scholar 

  • OECD. (1999). The knowledge-based economy: a set of facts and figures. Paris: OECD.

    Google Scholar 

  • OECD. (2009). OECD reviews of innovation policy: Korea. Paris: OECD Publishing.

    Google Scholar 

  • OECD. (2010). Measuring innovation: a new perspective. Paris: OECD.

    Google Scholar 

  • OECD. (2013). OECD science, technology and industry scoreboard 2013: innovation for growth. Paris: OECD Publishing. doi:10.1787/sti_scoreboard-2013-en.

  • OECD. (2014a). Gross domestic spending on R&D (indicator). Retrieved from doi:10.1787/d8b068b4-en.

  • OECD. (2014b). Researchers (indicator). Retrieved from doi:10.1787/20ddfb0f-en.

  • OECD. (2015a). Gross domestic product (GDP) (indicator). OECD. Retrieved from doi: 10.1787/dc2f7aec-en.

  • OECD. (2015b). Multifactor productivity (indicator). OECD. Retrieved from doi: 10.1787/a40c5025-en.

  • Olmeda-Gómez, C., Perianes-Rodríguez, A., & Antonia Ovalle-Perandones, M. A. (2008). Comparative analysis of university-government-enterprise co-authorship networks in three scientific domains in the region of Madrid. Information Research, 13(3). http://informationr.net/ir/13-3/paper352.html.

  • Park, H. W., Hong, H. D., & Leydesdorff, L. (2005). A comparison of the knowledge-based innovation systems in the economies of South Korea and the Netherlands using Triple Helix indicators. Scientometrics, 65(1), 3–27.

    Article  Google Scholar 

  • Shannon, C. E. (1948). A mathematical theory of communication. The Bell System Technical Journal, 27(3), 379–423. 656.

    Article  Google Scholar 

  • Shannon, C. E., & Weaver, W. (1949). The mathematical theory of communication. Urbana: University of Illinois.

    Google Scholar 

  • UNDP. (2014). Sustaining human progress: reducing vulnerabilities and building resilience. New York: United Nations Development Programme.

    Book  Google Scholar 

  • United Nations Economic Commission for Europe. (2002). Towards a knowledge-based economy: final report. New York and Geneva: United Nations.

    Google Scholar 

  • World Bank. (1999). World development report: knowledge for development: 1988/1989. Oxford: Oxford University Press.

    Google Scholar 

  • Ye, Y. F., Yu, S. S., & Leydesdorff, L. (2013). The Triple Helix of university-industry-government relations at the country level, and its dynamic evolution under the pressures of globalization. Journal of the American Society for Information Science and Technology, 64, 2317–2325.

    Article  Google Scholar 

  • Zitt, M., Bassecoulard, E., & Okubo, Y. (2000). Shadows of the past in international cooperation: collaboration profiles of the top five producers of science. Scientometrics, 47(3), 627–657.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Eustache Mêgnigbêto.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Mêgnigbêto, E. Correlation Between Transmission Power and Some Indicators Used to Measure the Knowledge-Based Economy: Case of Six OECD Countries. J Knowl Econ 9, 1168–1183 (2018). https://doi.org/10.1007/s13132-016-0408-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13132-016-0408-2

Keywords

Navigation