Three-Dimensional Assessment of the Knowledge Production System: Region-City-Organization

• Published in: Electronic journal of knowledge management : EJKM 2021-09, Vol.19 (2), p.pp118-132
• Main Authors: Mikhaylov, Andrey S., Mikhaylova, Anna A., Hvaley, Dmitry V.
• Format: Article
• Language: English
• Subjects: Cities; Heterogeneity; Innovations; Knowledge management; Knowledge representation; Municipalities; Organizations; Scientometrics
• ISSN: 1479-4411; 1479-4411
• DOI: 10.34190/ejkm.19.2.2410

Conceptualization of the region as an integral territorial system of knowledge production has formed a widely used research strategy for innovation studies within regional boundaries. Regional level studies are supported by detailed innovation statistics, which is unavailable for smaller administrative-territorial units, such as municipalities or settlements. The development of spatial scientometrics gave impetus for a new round of research on knowledge and innovation geography with a closer approximation in the context of cities and urban agglomerations. The scope of recent research also includes individual organizations that generate new knowledge or innovation. Despite the topic prominence, the entire array of studies is fragmented, and connections between different levels are not established: region – city – organization. Whereas this is critically important for the implementation of an effective innovation policy. In this regard, in this study, we test the hypothesis that the aggregate data obscures a wide variety of knowledge nodes, which are represented by a dominant knowledge centre. In the case of the region, such centres are often the largest cities, and in the case of cities – the largest organizations. The research design is focused on assessing the knowledge production at a multiscale level – organization, city and region, using the method of spatial scientometrics. The example of the Russian Federation illustrates well the territorial and institutional diversity in the distribution of knowledge production centres of different levels due to its great length and complexity of the structure of the national innovation system. This fact determines the high degree of heterogeneity of the Russian innovation space at the interregional, intercity and inter-organizational levels. The research results show a strong correlation between the knowledge profiles of regions and their primary knowledge-generating cities (KGCs). In cases of a strong central-peripheral structure of the regional knowledge production system, the regional profile completely coincides with the profile of its primary KGC. The knowledge capacity of second-tier cities remains hidden. At the city level, the identified trend is exacerbated. The absence of a pronounced leader among knowledge-intensive organizations (KIOs) against organizational diversity leads to a strong blur of the effectiveness of the knowledge production capabilities of a city. The example of Khabarovsk shows that the