IDENTIFICATION OF THE NATIONAL INNOVATION SYSTEM IN A GLOBALIZED ENVIRONMENT

Abstract

The activation of innovation processes always requires the presence of an appropriate enabling environment - an effective national innovation system (NIS), which determines the country’s ability to generate and implement various kinds of innovations. However, the formation of a full-fledged country’s innovation system is quite difficult, at least for two reasons: the presence of purely national features of the functioning of the main agents of change, as well as the specifics of the modern world, when many important processes for innovation go beyond individual countries, forming a globalized environment, the presence and the specifics of which determine the potential and strategic directions for the further development of each NIS. The scientific and methodological approach proposed in the paper is generally based on the hypothesis of the expediency of regulating a particular national innovation system development, taking into account its belonging to a certain basic type, which determines the specific features of development. For its implementation, the methods of genetic algorithms, cluster analysis, and neural network training were used. Within the framework of the study, four basic types of NIS were identified and qualitatively interpreted, which have characteristic features in the context of the quadruple helix concept of development. To identify the national innovation system, a neural network, which simplifies the modeling of its development, was built. As a demonstration of the additional possibilities of the toolkit, four supranational associations of countries have been identified and analyzed. The practical significance of the results lies in the possibility of conducting variable analytical and predictive studies in the course of substantiating the optimal directions for the further development of the national innovation system in terms of global and cluster trends.

Keywords

• national innovation system
• genetic algorithm
• clustering
• neural network model
• basic type
• regulation
• modeling