Innovation among the OECD members. An approach through Dynamic Network Data Envelopment Analysis and Hierarchical Conglomerate Analysis


The objective of this study is to make a comparison by cluster of the innovative efficiency of 33 Organisation for Economic Co-operation and Development (OECD) members. To do so, two methodologies are used: a Dynamic Network Data Envelopment Analysis (DEA) and a Hierarchical Conglomerate Analysis. The inputs of DEA analysis of the first stage were three factors: institutions, human capital, and infrastructure. As intermediate variables, the sophistication of the domestic market and the sophistication of the business market were considered. As products of the system, scientific and technological products were used. The results show that the most efficient innovative leaders were Estonia, Hungary, Switzerland, and Ireland. Fourteen other countries were classified as strong innovators. Mexico was found in the cluster of moderate innovators along with Spain and Japan, while the least efficient countries were Australia, Austria, Canada, Denmark, Finland, France, Greece, Iceland, New Zealand, Norway, Poland, and Portugal.
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Aguado, R. y Martínez, J. (2014) R&D productivity in Europe: towards a regional taxonomy in the European Union. Harvard Deusto Business Research Vol. 3 No. 1 pp: 2-22.

Blázquez, M. y García-Ochoa, M. (2009) Clústeres de innovación tecnológica en Latinoamérica. Revista Globalización, Competitividad y Gobernabilidad Vol. 3 No. 3 pp: 16-33.

Burns, R., & Robert, B. (2008). Business Research Methods and Statistics using SPSS.

Comisión Europea (2017). European Innovation Scoreboard. Commission of the European Communities, Luxembourg, 2006.

Dzemydaite, G., Dzemyda, I. y Galiniene, B. (2016) The efficiency of regional innovation systems in new member states of the European Union: a nonparametric DEA approach. Economics and Business No. 28 pp: 83-89.

Hollanders, H. y Celikel, F. (2007) Measuring innovation efficiency. Maastricht. Innometrics.

Johnson, R., & Wichern, D. (2007). Applied Multivariate Statistical Analysis. New Jersey: Prentice Hall.

Lee, H. y Park, Y. (2005) An international comparison of R&D efficiency: DEA approach. Asian Journal of Technology Innovation Vol. 13 No. 2 pp: 207-222.

OCDE (2005) Manual de Oslo. Guía para recolectar e interpretar datos sobre innovación. 3ª. Edición. París. OCDE.

OCDE (2016) Science, technology and innovation outlook 2016. París. Organización para la Cooperación y el Desarrollo Económicos.

OMPI, Universidad de Cornell e INSEAD (Organización Mundial de la Propiedad Intelectual) (Institut Européen d'Administration des Affaires) (2016), The Global Innovation Index, OMPI, Universidad de Cornell e INSEAD, Ginebra, Suiza.

Porter, M. y Stern, S. (2001) National innovative capacity. Global Competitiveness Report 2001-2002. Nueva York. Oxford University Press.

Restrepo, M. y Villegas J. (2007), “Clasificación de grupos de investigación colombianos aplicando análisis envolvente de datos”, Revista Facultad de Ingeniería, núm. 42 pp. 105-119.

Tone, Kaoru y Miki Tsutsui (2014), “Dynamic DEA with network structure: Aslacks-based measure approach”, Omega, núm. 42, Elsevier, Amsterdam, Holanda, pp.124-131.

Yang, Chyan y Hsian-Ming Liu (2012), “Managerial efficiency in Taiwan bank branches: A network DEA”, Economic Modelling, núm. 29, Elsevier, Amsterdam, Holanda, pp. 450–461.