A cognitive model for managing the national innovation system parameters based on international comparisons (the case of the EU countries)


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To carry out a comparative analysis of the EU countries’ national innovation systems (NIS), a feature vector has been compiled, covering three modules, namely, science, education, and innovation. The feature vector is a valid multidimensional data set of sixteen official statistics indices and two sub-indices of the Global Innovation Index. The development of a cognitive model for managing the NIS parameters required a preliminary three-stage empirical study to determine its elements. In the first stage, cluster analysis was performed (the k-means, metric – Euclidean distance algorithm was used). As a result, the EU countries were divided into four clusters (following multidimensional scaling estimates). In the second stage, a classification analysis (using decision trees) was carried out, which allowed determining three parameters that distinguish clusters (or classes) optimally. These parameters are recognized as important ones in terms of positioning the countries in the general ranking; that is, they can be considered as a priority for the NIS development and improving the countries’ positions in international comparisons. In the third stage, based on the authors’ approach, the significance (information content) of each key parameter is estimated. As a result, a cognitive model was compiled, taking into account the parameter significance. The model can be used in managing the NIS parameters, seeking to increase the system performance and improve the international position of a specific country. The model can also be used by partner countries, for example, Ukraine, as it demonstrates the landscape of EU innovative development and outlines the directions for priority development of NIS towards the European progress.

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    • Figure 1. Cognitive model for managing the NIS parameters of the EU countries
    • Table 1. A feature vector of the EU countries’ NISs, structuring its parameters according to the three main modules
    • Table 2. The resulting country clusters according to the NIS parameters