<?xml version="1.0" encoding="ISO-8859-1"?><article xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<front>
<journal-meta>
<journal-id>0188-6266</journal-id>
<journal-title><![CDATA[Acta universitaria]]></journal-title>
<abbrev-journal-title><![CDATA[Acta univ]]></abbrev-journal-title>
<issn>0188-6266</issn>
<publisher>
<publisher-name><![CDATA[Universidad de Guanajuato, Dirección de Investigación y Posgrado]]></publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id>S0188-62662019000100202</article-id>
<article-id pub-id-type="doi">10.15174/au.2019.2244</article-id>
<title-group>
<article-title xml:lang="es"><![CDATA[Innovación de los países miembros de la OCDE. Una aproximación a través del Análisis Envolvente de Datos Network Dinámico y el Análisis de Conglomerados Jerárquicos]]></article-title>
<article-title xml:lang="en"><![CDATA[Innovation among the OECD members. An approach through Dynamic Network Data Envelopment Analysis and Hierarchical Conglomerate Analysis]]></article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Zamora-Torres]]></surname>
<given-names><![CDATA[América Ivonne]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
<contrib contrib-type="author">
<name>
<surname><![CDATA[Favila Tello]]></surname>
<given-names><![CDATA[Antonio]]></given-names>
</name>
<xref ref-type="aff" rid="Aff"/>
</contrib>
</contrib-group>
<aff id="Af1">
<institution><![CDATA[,Universidad Michoacana de San Nicolás de Hidalgo Instituto de Investigaciones Económicas y Empresariales ]]></institution>
<addr-line><![CDATA[Morelia Michoacán]]></addr-line>
<country>México</country>
</aff>
<pub-date pub-type="pub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<pub-date pub-type="epub">
<day>00</day>
<month>00</month>
<year>2019</year>
</pub-date>
<volume>29</volume>
<copyright-statement/>
<copyright-year/>
<self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_arttext&amp;pid=S0188-62662019000100202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_abstract&amp;pid=S0188-62662019000100202&amp;lng=en&amp;nrm=iso"></self-uri><self-uri xlink:href="http://www.scielo.org.mx/scielo.php?script=sci_pdf&amp;pid=S0188-62662019000100202&amp;lng=en&amp;nrm=iso"></self-uri><abstract abstract-type="short" xml:lang="es"><p><![CDATA[Resumen El presente estudio tiene por objetivo el realizar un comparativo por clústeres de la eficiencia de 33 países miembros de la Organización para la Cooperación y el Desarrollo Económico (OCDE) en lo relativo a su desempeño en materia de innovación. Para ello se emplean dos metodologías: el Análisis de la Envolvente de Datos (DEA) Network Dinámico (que sirve para medir eficiencia) y un Análisis de Conglomerados Jerárquicos (cuyo propósito es clasificar). Los insumos del análisis DEA de la primera etapa fueron tres variables: las instituciones, el capital humano y la infraestructura. Como variables intermedias se consideraron a la sofisticación del mercado doméstico y a la sofisticación del mercado empresarial. Como productos del sistema, se utilizaron las variables de los productos científicos y tecnológicos de los países considerados. Los resultados muestran que los líderes innovadores más eficientes fueron Estonia, Hungría, Suiza e Irlanda. Otros catorce países fueron clasificados como innovadores fuertes. México se encontró en el clúster de innovadores moderados junto con España y Japón, mientras los países menos eficientes fueron Australia, Austria, Canadá, Dinamarca, Finlandia, Francia, Grecia, Islandia, Nueva Zelanda, Noruega, Polonia y Portugal.]]></p></abstract>
<abstract abstract-type="short" xml:lang="en"><p><![CDATA[Abstract 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.]]></p></abstract>
<kwd-group>
<kwd lng="es"><![CDATA[Eficiencia]]></kwd>
<kwd lng="es"><![CDATA[OCDE]]></kwd>
<kwd lng="es"><![CDATA[DEA]]></kwd>
<kwd lng="es"><![CDATA[Network]]></kwd>
<kwd lng="es"><![CDATA[Conglomerados]]></kwd>
<kwd lng="en"><![CDATA[Efficiency]]></kwd>
<kwd lng="en"><![CDATA[OECD]]></kwd>
<kwd lng="en"><![CDATA[DEA]]></kwd>
<kwd lng="en"><![CDATA[network]]></kwd>
<kwd lng="en"><![CDATA[clusters]]></kwd>
</kwd-group>
</article-meta>
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<volume>29</volume>
<numero>2</numero>
<issue>2</issue>
<page-range>450-61</page-range></nlm-citation>
</ref>
</ref-list>
</back>
</article>
