Innovative Development in Northern Russia Assessed by Triple Helix Model

This paper considers issues related to assessing the level of innovative development in the northern regions of Russia. A comparative assessment of the level of innovative development in seven regions of the Far North of Russia (FNR) for 2017 was carried out based on statistical data from the composite integrated index. A version of the Triple Helix (TH) econometric model served as the foundation for the assessment. This article presents the analytical results according to three elements of the TH model: the effectiveness of research and development (science), the effectiveness of innovation (industry), and budget expenditure on science and innovation (government). Regional innovative profiles were built during the analysis, which helped identify the strengths and weaknesses of the influence of science, business, and government on the development of innovative activities in the region. The results of such ratings make it possible to assess the comparative advantages and disadvantages of specific regions for further analysis. The data can be used in program documents on the region’s innovative development. The methodology proposed for an innovation activity rating can help predict the main development trends of the entire territory of the Far North. Finally, it can be applied to other regions and countries if relevant statistical information is available.

Indicators; Innovative development of the region; Rating; Russia, far north; Subject

The present development of economies in many countries, including Russia, is based on innovative development and the actual task of assessing of assessing a country’s innovative regional development (IDR).

Continuous monitoring of IDR indicators is necessary for making various organizational and managerial decisions by local executive authorities on the development of the innovative economy of a territory.

Assessing a region’s innovation potential based on the continuous monitoring of changes in its indicators becomes a necessary tool. This helps determine the level of development in the innovation part of the regional economy.

Currently, there are a number of research papers on quantitative measurements of the (Leydesdorff and Park, 2014; Mêgnigbêto, 2018; Nurutdinova and Dmitrieva, 2018) and according to high-tech industries (Leydesdorff et al., 2015). One research paper (Istomina et al., 2018) on the TH model presents an econometric analysis of the quantitative relationship between innovation activity indicators based on statistics by the Federal Service for State Statistics (Rosstat). As the literary review of the works of foreign and domestic researchers shows, there are currently no practical tools for quantifying the IDR level based on the theoretical TH model, except for the simulation model of relations between TH actors (Ivanova and Leydesdorff, 2014).

At present, the main organizations that regularly carry out IDR ratings include the Association of Innovation Development of Russian Regions (Rating of Innovation Development of Russian Regions, 2018) and the National Research University "Higher School of Economics" (HSE) (Russian Regional Innovation Scoreboard, 2020).

To assess the level of IDR, the main problem is the lack of a scientifically substantiated number of indicators in the innovation sphere, approximately 15–20 indicators (Lisina, 2012).

The development of the TH model in the region requires a quantitative assessment of actor interaction in innovation. Due to the complexity of the analyzed processes, there is no unambiguous approach to assessing the processes occurring in the TH model (Popodko and Nagaeva, 2019).

In this regard, in contrast to existing methods and based on the TH model (Etzkowitz, 2003; Etzkowitz and Leydesdorff, 2003; Chacko, L., 2019), Egorov developed a methodology for the quantitative assessment of IDR by a minimum number of key indicators in the field of scientific and innovative activity (Egorov et al., 2019; Berawi, M.A. 2016; Berawi, M.A., 2021; Shichkov, A. et al., 2019). The main advantage of the methodology compared with other methods is the use of data from official statistical sources, which excludes the subjectivity of an expert assessment of the calculation results.

The assessment of the level of innovative development is carried out for northern countries of the world located to the north of the Arctic Circle and includes the zone of the Far North. These also include both countries of the European part (Denmark, Iceland, Norway, Finland, Sweden, and Russia) and countries of North America (Canada and the USA). Despite the fact that the countries of northern Europe occupy 20% of the entire northern territory of the globe, their combined population is small and accounts for only 4% of all those living in this part of the world (Northern territories in the all-Russian, 2012; Vasiliev and Selin, 2012; European Commission. Regional Innovation Scoreboard, 2019).

According to Bloomberg's annual Innovation Index in 2020, the leading economies are Germany, South Korea, Singapore, Switzerland, and Sweden (Table 1).

In recent years, Russia has consistently ranked 25^th–27^th, although in 2016, it occupied 12^th place according to this rating.

Table 1 Innovative economies rating for northern countries

 Source: Innovative economies rating, 2020

Currently, there are eight regions whose territories are fully part of the Far North of Russia (next FNR): the Murmansk and Magadan regions, the Republic of Sakha (Yakutia), Kamchatka Territory, and four autonomous areas: the Nenets Autonomous Area, Khanty-Mansi Autonomous Area, Yamalo-Nenets Autonomous Area, and Chukotka Autonomous Area (list of areas qualified as the regions of the Far North).

Thus, the above discussion determines the relevance of this research, the object of which is the innovative development of a region’s economy. The aim of this study was to quantify and analyze the level of innovative development of regions based on the TH model.

The scientific novelty of the work lies in using the author’s econometric TH model to assess the contribution and identify the strengths and weaknesses of the influence of the scientific and education system, business, and the state on the innovative development of the region according to their minimum key statistical indicators in the field of innovation.

The study demonstrates a significant difference between FNR regions in terms of innovative development. Five FNR regions show higher values for the composite innovation index than the average (0.26). The values vary by region, from 0.05 (Nenets Autonomous Area) to 0.46 (Yamal-Nenets Autonomous Area). Different positions of regions are also shown in the individual sub-indexes’ ratings. Creating innovative profiles clearly points out the strengths and weaknesses of the influence of science, business, and local authorities on the region’s innovative development.

The results obtained will fulfil the information needs of the regional authorities that make and implement decisions in the field of innovation policy. The ratings will allow manufacturers to consider regional specifics when implementing and using various innovative projects and developments. In addition, it will help citizens to evaluate the performance of executive bodies in the regions.

Thus, based on the studies carried out, the following results were obtained: (1) Based on the econometric model of the TH, there is a significant difference in the Arctic regions in terms of their innovative development; (2) The share of the TH partners' contribution to the overall innovative development of the Arctic regions of Russia was determined, and the strengths and weaknesses of the influence of science, business, and local authorities on the innovative development of the region were identified; (3) The results of the rating assessments will allow regional authorities and manufacturing enterprises to fully incorporate the regional specifics when implementing and using various innovative projects and developments in their activities; and (4) The proposed methodology for the rating of innovation activities in the regions will allow the prediction of the main trends in the development of the Far North.

It should be noted that the above research methodology can be used for other regions and countries of the world, provided that relevant statistical information in the field of innovation is available.

Further research on this topic will be aimed at studying the impact of the results of innovative activities on improving the livelihoods of the population in the regions in the context of the digital transformation of industries and the social sphere.

    This research was partially funded by the Ministry of Science and Higher Education of the Russian Federation under the strategic academic leadership program Priority 2030 (Agreement 075-15-2021-1333 dated 30.09.2021).

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