Gennadiy Shevchenko

Modern economy is characterized by rapid qualitative and quantitative changes that significantly affect the nature of economic, socio-economic and social relations. Innovative processes and trends are very specific manifestations, which are reflected in the economic and marketing theory. A greater place in science and practice is occupied by the concepts of new economy, knowledge economy, knowledge society. Therefore, the study of knowledge economy marketing becomes more and more relevant.
The paper is aimed to develop a technique for selection of the key parameters for building the model of national knowledge economy marketing.
For this purpose, it is proposed to conduct a cluster analysis based on aggregated data. Classification of differences between clusters is given. As a result of classification, the authors have identified a group of indicators, which make all clusters distinctive and, first and foremost, determine positions of countries in the global landscape. These indicators are interpreted as key factors of the knowledge economy.
Based on the suggested mathematical functions, the authors assessed the value of every key factor within the selected group. It became the second step in selecting the parameters to build a multifactor model of knowledge economy marketing at the national level. The paper also justifies that it is reasonable to use cognitive approach to address challenges in the sphere under consideration. This approach is able to become a sound basis for building the model of national knowledge economy marketing in the form of cognitive map.

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.

Due to the large volumes of empirical digitized data, a critical challenge is to identify their hidden and unobvious patterns, enabling to gain new knowledge. To make efficient use of data mining (DM) methods, it is required to know its capabilities and limits of application as a cognitive tool. The paper aims to specify the capabilities and limits of DM methods within the methodology of scientific cognition. This will enhance the efficiency of these DM methods for experts in this field as well as for professionals in other fields who analyze empirical data. It was proposed to supplement the existing classification of cognitive levels by the level of empirical regularity (ER) or provisional hypothesis. If ER is generated using DM software algorithm, it can be called the man-machine hypothesis. Thereby, the place of DM in the classification of the levels of empirical cognition was determined. The paper drawn up the scheme illustrating the relationship between the cognitive levels, which supplements the well-known schemes of their classification, demonstrates maximum capabilities of DM methods, and also shows the possibility of a transition from practice to the scientific method through the generation of ER, and further from ER to hypotheses, and from hypotheses to the scientific method. In terms of the methodology of scientific cognition, the most critical fact was established – the limitation of any DM methods is the level of ER. As a result of applying any software developed based on DM methods, the level of cognition achieved represents the ER level.

Innovation plays a crucial role in ensuring economic growth and competitiveness of national economies, creating conditions for their sustainable development. By focusing on supporting innovation, the EU is particularly helping to accelerate the development of those member states that lag far behind the EU average. This requires the selection of the indicators reflecting the development of innovation that determine the differences between member countries to the greatest extent. Therefore, the aim of the study is to identify the key factors of the innovation gap (FIG) between EU countries based on a comparison of indicators characterizing the national innovation systems (NIS).
For this purpose, 22 relative indicators were selected from the indicators included in the Global Innovation Index to form an array of empirical data. At the first stage, the EU countries were divided into four clusters using the k-means method. At the second stage, using the decision tree method, a group of indicators was identified that together distinguish the obtained clusters to the greatest extent and, accordingly, determine the differences between EU countries and can be considered as FIG, namely: “Researchers”, “GERD financed by business”, “Joint venture/strategic alliance deals”, “Software spending”, and “High-tech manufacturing”. This allows individual member states to prioritize the development of those indicators (i.e. FIG) that most determine their position in the EU and therefore improve their NIS. At the EU level, this will contribute to the complementarity of the NIS, overcome differences between member states and increase the overall level of convergence in innovation.