Nowadays, it has been besetted with knowledge input and output measurement instead of analyze the quality of the knowledge produced. Since knowledge become central into capitalistic production processes, the competitive advantage of countries is the principal engine for the production of high-value, non-ubiquitous and complex knowledge (see Dicken, 2007). The aim of the present study is to measure the knowledge complexity of the European countries from 2004 to 2013, designing the knowledge evolution and distribution. One consequent intention is to examine in which way the spatial knowledge diffusion might be connected to complexity. To achieve these purposes, we identified the presence of the European country-tech knowledge network, used as starting point to compute the Knowledge Complexity Index, in which the technological classes are composed by the countries having an relative technological advantage in terms of patent spread. Subsequently, we used two distinct types of statistical analysis: the first, based on non-linear clustering with Self-Organizing Map (SOM hereafter) neural network, for evaluating the performance development of the European countries between 2004 and 2013, where, for example, Eat countries, located at the top of the map, show low values in almost all considered variables; and the second one, based on the Knowledge Complexity Index (KCI hereafter) of technological classes, for quantifying the European knowledge complexity; describing the possible spatial patterns and transformation of the European knowledge. What emerges from the present study is an inactive spatial state of art, in which only the Northern European countries produce the most conglomerate knowledge and technologies. In this structure, it is necessary to stress out that what affirmed previously could be considered the basis for the improvement and the achievement of the specific country’s innovation policy. However, under complex knowledge terms, the previous innovation policies might be assimilated with a useful tool for the increment of that technologies that present more complexity than the original ones.

The Eurpean complex knowledge

CIALFI Daniela
;
COLANTONIO Emiliano
2019

Abstract

Nowadays, it has been besetted with knowledge input and output measurement instead of analyze the quality of the knowledge produced. Since knowledge become central into capitalistic production processes, the competitive advantage of countries is the principal engine for the production of high-value, non-ubiquitous and complex knowledge (see Dicken, 2007). The aim of the present study is to measure the knowledge complexity of the European countries from 2004 to 2013, designing the knowledge evolution and distribution. One consequent intention is to examine in which way the spatial knowledge diffusion might be connected to complexity. To achieve these purposes, we identified the presence of the European country-tech knowledge network, used as starting point to compute the Knowledge Complexity Index, in which the technological classes are composed by the countries having an relative technological advantage in terms of patent spread. Subsequently, we used two distinct types of statistical analysis: the first, based on non-linear clustering with Self-Organizing Map (SOM hereafter) neural network, for evaluating the performance development of the European countries between 2004 and 2013, where, for example, Eat countries, located at the top of the map, show low values in almost all considered variables; and the second one, based on the Knowledge Complexity Index (KCI hereafter) of technological classes, for quantifying the European knowledge complexity; describing the possible spatial patterns and transformation of the European knowledge. What emerges from the present study is an inactive spatial state of art, in which only the Northern European countries produce the most conglomerate knowledge and technologies. In this structure, it is necessary to stress out that what affirmed previously could be considered the basis for the improvement and the achievement of the specific country’s innovation policy. However, under complex knowledge terms, the previous innovation policies might be assimilated with a useful tool for the increment of that technologies that present more complexity than the original ones.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11564/713115
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