Scientific and technological capacity is a major resource for the organization of innovative activities and the implementation of research ideas and projects of scientific and technological progress. The determination of its level in the context of economic entities will allow them to make more effective and correct management decisions and frame better innovation policy at the state level. It should be noted that this category is synthetic and consists of a set of interrelated indicators. In practice, such estimates are usually performed through the calculation of the multiplicative index. The indicators used in this estimation are often equal, which, in practice, is manifested in a different way. In order to determine their proportion or weights, the factor analysis method of principal components may be used. This article considers the basic theoretical principles of innovative activity within the category of scientific and technological capacity. Assessment practices of this value by the leading organizations in Russia are considered. When making these calculations it is necessary to clearly determine the significance of each indicator. In this regard, the algorithm for the calculation of these weights has been offered. In addition, the algorithm groups the indicators, integrating them by types of correlation. From the mathematical point of view, this procedure implies the position of vectors in dispersion cloud space. The article presents the results of calculations according to the data from the subjects of the Russian Federation (80 regions) for 2011–2012 with a preliminary justification of the selected indicators and their bringing to a comparable form on the basis of normalization regarding the best values in each of the periods under consideration. The paper also proposes further research stages, which ensures a more precise and reliable estimation of scientific and technological capacity
Keywords
innovative activity, correlation, scientific and technological capacity, factor (component) analysis, integral estimation, indicator weights