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National Innovation Systems Archetypal Analysis

机译:国家创新系统原型分析

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The national innovation system (NIS) determines the innovation capability of a country, and its economic development. However, recently, very little is known regarding the determinants of NIS functioning in various countries. Probably the easiest way to obtain such an understanding is to begin with the structural representation of the NIS. Particularly, it is quite natural to assume that there exists several 'cornerstone type NIS' or 'archetypal NIS', and all the other types can be considered a mixture of them. The aim of this paper is to somewhat study the advances in the structural understanding of the NIS. For this purpose we conducted our study based on the data set from the Global Innovation Indexes' (GII) seven pillars and using archetypal analysis. It is also important to note that the concept of entropy was also naturally determined under archetypal analysis. We demonstrate that each NIS can be considered a mixture of three archetypical NISs, which are as follows: The first one is a prototype of a highly developed NIS (with a high level GII score and a low level of entropy); the second one is a prototype of an underdeveloped NIS (with a low level GII score and a low level of entropy); and the third one is an intermediate form of NIS (with a medium level GII score and a high level of entropy). Hence, we establish that such a multidimensional phenomenon, such as the NIS (described in this study as the 7-dimensional vector - GII pillars), with an acceptable level of the accuracy, essentially can be considered a 2-dimensional object; and the corresponding barycentric coordinates are a convenient means of describing NISs. We also introduce an important indicator - the NIS entropy - which characterises the level of the disorder or randomness in the NIS.
机译:国家创新体系(NIS)决定着一个国家的创新能力及其经济发展。但是,最近,关于NIS在各个国家运行的决定因素知之甚少。获得这种理解的最简单方法可能是从NIS的结构表示开始。特别是,很自然地假设存在几种“基石类型的NIS”或“原型NIS”,而所有其他类型都可以被视为它们的混合。本文的目的是对NIS的结构理解进行一些研究。为此,我们基于全球创新指数(GII)七个支柱的数据集并使用原型分析进行了研究。还必须注意,熵的概念也是在原型分析中自然确定的。我们证明了每个NIS可以看作是三个原型NIS的混合,如下所示:第一个是高度发达的NIS(具有较高的GII评分和较低的熵)的原型;第二个是不发达的NIS(低GII评分和低熵)的原型;第三个是NIS的中间形式(GII得分中等,熵也很高)。因此,我们建立了这样一种多维现象,例如NIS(在本研究中称为7维向量-GII支柱),其准确度在可接受的水平上,基本上可以视为二维对象。重心坐标和相应的重心坐标是描述NIS的便捷方式。我们还引入了一个重要指标-NIS熵-表征NIS中无序或随机性的水平。

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