Policies for Improving the Efficiency of Innovative Clustering in an Emerging Market

Abstract

The main topic of this study is to define the direction of improving the state policy of Russia in achieving the maximum efficiency of clustering in the country. The chapter aims to study the interconnected influence of innovation clusters on the development of the knowledge economy in Russia and the world. Attention is focused on the improvement based on the cyclical nature of clusters using human capital, technology, policies, and management. To achieve this, a historical review of the formation and successful development of clusters in the Russian Federation is carried out to identify and assess the prominent occurrence cases, the central institutional actors, the indicators of their innovative activity, and the schematics of successful cluster management. The theory section covers current classification methods and typology of innovation-territorial economic associations. Russian innovation policy for cluster development received an up-to-date performance overview as well.

1. Introduction

The strengthening of the integration effects of internationalization and globalization in the world economy characterized the last decades of the twentieth and early twenty-first centuries. Commercial expansions introduced internationalization into every sphere of production, which provoked an intensive reformation of the conditions of added value in the transnational connection of producers. Integration appeared as a systemic congregation of economic blocs supported by institutional and regulatory instruments at the mega and macro-levels.

Now at the mega level, an example of unions is the European Union, when integration at the macro-level is practiced by forming economic blocs on the state’s territory. These macro-subjects are called clusters, which include firms and organizations with a strong association for finalized products. In such economic associations, it becomes necessary to consider the participants’ geographical proximity and physical distance. These concentrated associations in the post-industrial period are now seen as the epicenters of colossal innovation and production potential, achieved through the effective creation and distribution of social, technological, and human capital.

In the presence of these aspects, clusters acquire the status of a stable and competitive infrastructure by realizing their production potential. It is worth noting that the system of clustering of the national economy has been adopted in 70% of the world’s leading countries. Long-term practices show that innovation-territorial clusters are the basis of the most competitive developing economy. These associations have existed in Russia for 10 years, but the clustering program has a different impact on all business areas. As is customary, the programs of each innovation cluster and their associations are developed geographically. They aim to integrate research and industry to optimize the transformation of innovations integrated with the operational chain into full-fledged or experimental products.

The main topic of this study is the direction of improving the state policy of Russia to achieve the maximum efficiency of clustering in the country. Attention will be focused on the cyclical nature of this part of the economy and the perceived priorities for the optimal development of clusters through human capital, management, and technology.

The expectations set by the government and management methods deployed in the current cluster policy prove to be underwhelming. This is the problem statement for this research, which concretizes successful and effective ways to maintain innovative clusters.

The study aims to propose measures for the initial and continuous improvement of the work of innovation clusters in Russia based on the literature review, analysis of factors, and effects of successful management of economic and industrial clustering. At the same time, this research assumes that the Russian economy will become the primary environment for applying the acquired knowledge.

To achieve that, the following tasks must be specified and solved.

1. Identify the features of Russia’s innovative clustering and its role in increasing global competitiveness.

2. Determine the status and explore the tools for forming high-tech industries in Russia.

3. Provide an assessment of the current political activity and strategy of cluster formation applied across the regions of Russia.

4. Define and describe the success factors for the activities of regional innovation clusters.

5. Choose practical management tools for regulating the efficiency of high-tech industries in the Russian Federation.

6. Determine the role, promising directions, and parameters of cluster progress for the future development of innovative systems.

The relevance of the research topic is characterized by the expected potential and proven effectiveness of clustering world economic entities. When extensive globalization prevails in the transition to new modes of production, it is necessary to study the appropriate management methods that will intensify scientific and technological progress to increase the competitiveness of companies and states. Many economists and writers position the cyclical nature of the ongoing changes as the main priority in studying business actors at various levels of the economy. Cluster systematization of the business environment opens new opportunities for stabilizing national innovation systems and protecting them from external economic factors. These trends in the functioning of the world economy are omnipresent and, by their existence, support the study’s relevance.

Due to the relatively innovative status of the interdisciplinary study of the effects and management elements of clustering, it is observed that this topic is relatively unexplored by its contemporaries. The literature overview performed by the author confirms the presence of a research gap in this area. No academic work explores the innovative Russian economy and its development problems on this scale. In addition, current archives are dominated by publications that have lost their analytical relevance some time ago. Therefore, many factors deserve a place in updated qualitative analysis.

2. Features of Russia’s innovative clustering and its role in increasing the country’s global competitiveness

Expanding on the previous observations, the foreign experience of innovative development indicates that the success of implementing innovative programs strongly depends on the effectiveness of the institutional management of these programs. Therefore, developed countries are characterized by clarity, consistency, availability of infrastructure, a well-functioning management structure, and methodological, organizational, and legal documents that work as regulatory tools [1]. For these reasons, while monitoring the successes of the United States and other countries in 1999, the State Duma of the Russian Federation considered the draft law “On innovation activity and state innovation policy” [2].

After giving it some tests, the main principles of the policy of the Russian Federation in the field of scientific and technological development for 2010 were adopted with a further update until 2020. In the context of this document, the main directions, goals, objectives, implementation mechanisms, methods, and measures to stimulate the scientific, technical, and human capital activities of the policy in Russia were established. This policy was set to support the sustainable economic and innovative development of each region and the entire country. Such development of scientific and technical activities was aimed at solving the problems of socioeconomic and global progress, which were seen as the fundamental priorities for Russia after 2002 [3].

Until 2010, the second stage of the regulatory and legal delineation of innovation policy was supposed to organize a national system of innovation activity and finalize the global structure of the scientific and technological complex, but these goals were barely achieved. The effect of the successful implementation of these measures was to ensure the optimized functioning of the country in a market economy to maintain its competitive position in biological disciplines and high technologies worldwide [4].

Future redactions solidified the structure of cluster management at the regional level, which had a hierarchical direction of control over the network economy, as is shown in Figure 1. Over the years, this configuration of regulatory function was occasionally doubted, but it has yet to receive any substantial alteration or development.

Figure 1.

Cluster management structure at the regional level of Russia. Source: [5].

From these normative strategies, new features were determined for the innovative work of the Russian Federation. It was assumed that at least half of the cluster participants were members of the governing body that was to establish an agreement with more of the constituent entities of the Russian Federation to act as cluster management [3].

There should have been at least 10 industrial enterprises on the territory of one or more regions in Russia, at least 1 of which was to be engaged in producing finished products. In addition, when creating and developing a cluster, special attention was paid to the space exploration strategy of the Russian Federation and the plans for overall regional development, as well as the commercial interests of companies that were located on the same territory [3].

At least 5% of industrial products, materials, and components produced by each cluster member had to be used by other members in addition to the manufacturer of the final cluster product. Also, at least 20% of the total product of a cluster member was required to be used by other cluster members, or at least 20% of the total product of a cluster member that produces the end product was used by other cluster members. In addition, at least 50% of tasks in a cluster had to show high-performance indicators [3].

The infrastructure included at least one higher professional or secondary professional education institution and two technical or industrial infrastructure subjects. Thanks to these past and present conditions, the hierarchical structure for the global management of the subjects of innovation policy was constructed. Its ambition was to effectively connect the global and regional levels of management to tighten the connection between the institutions, as shown in Figure 2.

Figure 2.

Cluster management structure at the global level of Russia. Source: [5].

In the third stage of this policy—in 2016, the Ministry of Economic Development of Russia initiated a priority project called “Development of innovative clusters – leaders in first-class investment attractiveness” [6]. Its main tasks were to create pilot epicenters of rapid economic progress, innovative development, export of high-tech products and commercialization of technologies, increase in labor productivity and creation of high-performance jobs, and reflective of the past wishes, the increase of national competitiveness.

The new initiative was built on early experiments to support industrial zones and clusters. However, in the new interpretation, new priority industrial areas were emphasized [6]. The year of initiation, the status of organizational development, information on the number of participants, and characteristics of the type of functioning of these pilot innovation clusters that were actively supported by the state program are presented in Table 1.

Region	First-year	Specialization	Members	Organizational development	Type	Rank
Altai region	2008	Biopharmaceutical cluster	19	Medium	Vertical, state-owned, developing, sustainable, idea generator.	43
Arkhangelsk region	2012	Shipbuilding innovative territorial cluster	23	Medium	Consumer of innovations, with the state’s participation, developing, sustainable, horizontal.	34
Kaluga region	2012	Pharmaceutical cluster, biotechnology, and biomedicine	54	High	Idea generator, horizontal, with the participation of the state cluster leader.	11
Kemerovo region	2012	ITK “Complex processing of coal and industrial waste.”	49	Early	Consumer of innovations, horizontal, with the state’s participation, developing cluster, asynchronous cluster.	35
Krasnoyarsk region	2011	The cluster of innovative technologies ZATO Zheleznogorsk	10	Early	Cluster leader, consumers of innovations, vertical, with the state’s participation, developing cluster, stable cluster.	8
Moscow	2013	A cluster of microelectronics, info-communications “Zelenograd.”	53	Medium	Cluster leader, with the state’s participation, developing cluster, stable cluster, horizontal.	1
Moscow	2014	New materials, laser, and radiation technologies (Troitsk)	53	Early	Consumer of innovation, horizontal, state-owned, emerging clusters, sustainable.	1
Moscow region	2012	Biotechnological innovation territorial cluster, Pushchino	68	Medium	Idea generator, vertical, with the state’s participation, developing cluster, sustainable.	6
Moscow region	2012	An innovative territorial cluster of nuclear physics and nanotechnologies in Dubna	80	Early	Consumers of innovation, vertical, state-owned, emerging, and sustainable.	6
Moscow region	2012	The cluster of pharmaceuticals “Phystech XXI” (Dolgoprudny)	25	Early	Idea generator, horizontal, state-owned, developing, sustainable.	6
Nizhny Novgorod Region	2015	Nizhny Novgorod industrial innovation cluster in the field of automotive and petrochemistry	33	Early	Consumer of innovations, horizontal, with the state, developing, cluster leader.	5
Nizhny Novgorod Region	—	Sarov Innovation Cluster	—	Disbanded/integrated	—	5
Novosibirsk region	2016	The innovative cluster of information and biopharmaceutical technologies	227	High	Idea generator, horizontal, state-owned, developing, cluster leader.	7
Perm region	2012	Innovative Territorial Cluster of Rocket Engine Building “Technopolis New Zvezdniy”	44	Medium	Consumers of innovation, vertical, state-owned, developing, sustainable cluster.	16
Republic of Bashkortostan	2012	Petrochemical territorial cluster	211	High	Consumer of innovation, vertical, state-owned, developing, sustainable.	15
The Republic of Mordovia	2013	Energy-efficient lighting technology and intelligent lighting control systems	24	Medium	Consumer’s innovation, vertical, state-owned, developing, sustainable.	22
Republic of Tatarstan	2012	Kama innovative territorially automotive manufacturing cluster	213	High	Cluster leader, developing, with the state, horizontal, idea generator.	3
Samara Region	2012	Aerospace cluster	13	Medium	Consumer of innovation, vertical, state-owned, developing, sustainable.	14
St. Petersburg	1999	Development of information technologies, radio electronics, instrumentation, and ITC facilities	66	High	Consumer, vertical, with the state’s participation, developing, cluster leader.	2
Leningrad region	2014	The cluster of the medical, pharmaceutical industry, and radiation technologies	12	Early	Idea generator, vertical, state-owned, growing cluster, sustainable.	38
Sverdlovsk region	2012	Titanium cluster	20	Early	Consumer-generator, horizontal, without states, developing, sustainable.	9
Tomsk region	2013	Pharmaceutical, medical engineering, and information technology	52	Early	Сonsumer-generator, vertical, state-owned, developing, sustainable.	4
Ulyanovsk region	2009	Consortium “Scientific-educational-production cluster Ulyanovsk-Avia.”	77	High	Consumer-generator, vertical, with the state, developing, sustainable.	12
Ulyanovsk region	2010	Nuclear innovation cluster of the city of Dimitrovgrad	69	High	Idea generator, vertical, horizontal, state-owned, developing, sustainable.	12
Khabarovsk region	2012	Innovative Territorial Cluster of Aircraft and Shipbuilding	62	Early	Idea generator, vertical, horizontal, state-owned, developing, sustainable.	17
Moscow	2018	Moscow Innovation Cluster, IT Center	34,645	Early	Generator-consumer of ideas, vertical-horizontal, with the state, developing, cluster leader.	1

Table 1.

Authors’ overview of the pilot and main innovation clusters in Russia.

Source: The table arranged by the authors after the qualitative research.

However, the fundamental limitations showed themselves shortly into the implementation process when establishing these policies. One of which was the unpreparedness of local managers for innovative work in scientific, technological, and economic fields. Conclusions based on data from the Russian Cluster Observatory and colorful descriptions of the administrative cons demonstrate that the Russian economy’s current level of innovative development needs to correspond to the goals and objectives of the national strategy for innovative development and its forecasts. The main reasons for this situation are the need for more effectiveness and the nonexistence of consumer orientation in the organizational mechanism of corporate innovation, the culture of which needs to be considered in the national innovation policy [7].

Accordingly, other disadvantages can be observed in the strategic concept of moderate growth of innovation in Russian policy. The lack of proper access to state statistics on the goals of managing innovative development and the delay of any statistical data should also be noted as a con. In addition, the structure of statistical indicators only partially corresponds to the tasks of the current day, which, in the absence of a detailed description of practical measures for the implementation of innovation policy, provokes discoordination among the cluster members. Also, there needs to be a consolidated source of information describing the organizational functioning of innovation clusters. So, enthusiasts such as the author, who is proactive in obtaining information on local cluster management, face the limited nature of their description [8].

However, there are some positive aspects to the strategic situation of the Russian Federation. For example, there is enormous potential for editing and concretizing the strategic vision, and an extensive array of opportunities for interpreting new orientational recommendations can be introduced. With an emphasis on integrating more companies and other forms of business into clusters, small- and medium-sized businesses can be improved. In addition, the detail of procedures for subsidizing clusters is quite transparent. The current strategy extensively describes the range of sectoral and infrastructure activities for implementing the innovation program, which is a positive [6].

It is noteworthy that when discussing the actual state of Russia’s innovative economy, some experts argue that a monopolistic industrial policy is necessary to accelerate economic growth, while the antitrust policy is destructive and creates unnecessary additional restrictions. However, another camp states that the conditions for the fair competition are just and that incentives for investment and the entire operation of free policy are necessary to provide market participants with advantages over competitors and deprive them of monopoly policy [9]. At the same time, the industrial policy of enterprises eliminates the interest in increasing production capacity. Without concrete guidance in this regard, the country is now open to improving the tools of state regulation, the status of legislation, and the stimulation of foreign direct investment, which have been lost due to consistent sanctions from the United States and the European Union [10]. These circumstances have outlined the multiple strengths and weaknesses of the current Russian economy, which are detailed in Table 2.

Strengths		Weaknesses
Index	Rank	Index	Rank
Higher education	14	Regulatory quality	100
Enrollment in higher educational institutions, % of total	15	Law supremacy	109
Graduates in science and technology, %	13	Environmental sustainability	101
QS university ranking, top 3	21	GDP per unit of energy consumption	117
Trading, diversification, and market scale	17	Environmental certifications ISO 14001/bn. GDP in PPP dollars	107
The scale of the domestic market, bln PPP	6	Gross microfinance loans, % of GDP	78
Knowledge-intensive employment, %	18	Investments	116
Working women with advanced degrees, %	10	Venture capital recipients, deals/Bn PPP$ GDP	92
Payments for intellectual property, % of total trade	23	Firms offering formal training, %	94
Patents by origin/billion USD PPP GDP	15	Net FDI inflow, % of GDP	97
Utility models by origin/billion USD PPP GDP	10	ISO 9001 Quality Certifications/billion USD PPP GDP	105
Documents cited H-index	23	Printed and other media, % production	80

Table 2.

Strengths and weaknesses of the Russian innovation economy.

Source: [10].

Many small Russian enterprises face the urgent difficulties of further innovation and survival. At the same time, the country has sufficient potential for comprehensive development: a cheap material base and empty production areas. However, the accepted superficial orientation of the economy to export actively minimized the production capacity of the manufacturing sectors and only stimulated the resource-extracting industries. Now, the competitiveness of the processing and transforming knowledge, primarily industrial and commercial products, requires the regular and tight integration of new management that embodies the best scientific results worldwide.

Now, bringing the investments to clusters should correspond with their needs and interests and the state regulations. Depending on the specific combination of companies and interests, cluster financing can be presented in various forms. Investment can be carried out based on each cluster member’s self-support but according to a single agreed business plan without the involvement of third-party sources. Financing also comes from collective self-financing through creating equity in private fund institutions created by cluster members on a shared basis. Funds raised through investment loans are taken by each cluster member independently, but there is also a real possibility of financial support based on an agreed-upon business plan. Finally, according to the popular method of financing, the cluster is legally represented by a specific participant or fund, provided that some or all of the cluster participants receive government subsidies [7].

Nevertheless, the principal instruments of financial support for clusters used in Russian practice are inter-budget subsidies, targeted federal and state programs, and the activities of state companies and development agencies [7].

An analysis of international and Russian experience in the formation of innovation clusters mainly shows the presence of practical problems in Russian implementation. Of course, there are many more reasons why developing an innovation policy for the Russian Federation could be more effective. However, some of the fundamental aspects can still be outlined:

• inadequacy of the system for insurance of innovative investments and leasing of high-tech products;

• active investment in the development of the raw materials industry, rather than supporting the processing industry and not making venture capital investments;

• the limited scale of development of new or promising innovative directions and innovative apathy of business;

• insufficient institutional support at the administrative and specialized levels;

• mediocre efficiency of tax incentives, tariffs, and general infrastructure;

• low commercialization of the project, as only the declarative nature of cluster activity prevails;

• inadequate relationships between large and small firms, authorized cluster members, and independent experts.

3. Status and known tools of high-tech clusters in Russia

Since 2012, two main trends in the development of clusters have been observed in the Russian Federation. The first is centralized project support from the Ministry of Economic Development of Russia, aimed at forming territorial innovation clusters in each region. Moreover, second, the Ministry of Industry and Trade of Russia provides industrial clusters with software and industrial support [9].

Nevertheless, innovative activity in innovative industries has steadily decreased: from 17.8% in 2017 to 15.1% in 2019. Other negative trends in 2017–2019 were decreased R&D activity from 46.6 to 35.6% in the pharmaceutical industry and from 29.4% to 21.5% in the production of medical devices. However, in recent years, the pandemic has substantially boosted innovation in these areas [11].

Nevertheless, at the same time, in industrial clusters, the mass form of production prevails in the current times, which is contrasted by the continuous production in innovative clusters of a limited range of products at highly specialized jobs. Therefore, mass production in Russia stayed as the long-term strategy for economic development.

As a result, automobiles, tractors, and other specialized vehicles were produced in colossal numbers; agricultural machinery reached hundreds of thousandths of sales per year. The fabrication of these products is usually carried out in unique factories or special workshops oriented toward mass production in industrial clusters. However, innovative products had different results since the population had the lowest possible demand for innovative local products and used well-established analogs from abroad.

Russia lags behind the world leaders in the development of clusters. However, this can be justified because over 75% of innovation clusters were launched after 2012. Therefore, they are still in the initial stage of formation or development, which implies the absence of progressive achievements in the context of innovative leadership or global competitiveness [11].

According to domestic experts, since 2012, when the Government of the Russian Federation announced the implementation of the clustering policy as part of its strategy for innovative development, the policy has yielded significant results only by 2020 in some regions of Russia. It is possible to note that the Republic of Tatarstan has high costs for technological innovations and product innovation activities, as well as the Kaluga region with expressive macroeconomic indicators, where clusters were formed and developed based on the existing powerful production complex. Alternatively, the Smolensk Linen Cluster was created with the participation of Vyazemsky Machine-Building Plant LLC, which reduced material and energy costs because of its innovations. The auxiliary work of this complex made it possible to create a complete production chain with the potential for further development and expansion of membership in a full-fledged cluster [9].

Moscow retains its leading position regarding the degree of innovation and development of the constituent entities of the Russian Federation in 2018/2019 when it has the 32nd position in the technological leadership table from WIPO [10]. This region continues to demonstrate the highest economic, educational, and digital development levels. IT leadership is also seen as the key to its success. There is a colossal potential for digitalization and expressive educational potential of the population in the capital. The manufacturability of the personnel ensures the high innovative activity of organizations, which brings confident costs for technological innovation and prospective confidence in the export of knowledge. The regulatory framework for innovation policy with stable organizational support assists the small innovative business of Moscow with the city’s participation in the federal scientific, technical, and innovation policy [6]. The economic, technological, and innovative achievements of this policy are presented in Table 3.

MOSCOW
Free innovation index	0.551
Rank	1
1. Socioeconomic conditions for innovative activities
1	1.1 Main macroeconomic indicators	0.388	9
0.567	1.2 Educational potential of the population	0.646	13
	1.3 The potential of digitalization	0.864	1
2. Scientific and technical potential
(5) 6	2.1 Funding for research and development	0.298	20
0.455	2.2 Science staff	0.560	3
	2.3 R&D performance	0.558	5
3. Innovation
(7) 2	3.1 Innovative activity of organizations	0.886	1
0.588	3.2 Small innovative business	0.662	3
	3.3 Technological innovation costs	0.660	16
	3.4 Innovation performance	0.143	60
4. Export activity
(2) 4	4.1 Export of goods and services	0.513	12
0.566	4.2 Knowledge export	0.637	4
5. Quality of innovation policy
2	5.1 Normative legal framework for innovation policy	0.750	13
0.581	5.2 Organizational support of innovation policy	0.500	13
	5.3 Budget spending on science and innovation	0.331	4
	5.4 Participation in federal science, technology, and innovation policy	0.713	4

Table 3.

Aggregated innovation indices of Moscow.

Source: Calculated by the authors based on the numbers from [10].

Currently, only St. Petersburg can be perceived as the closest competitor of Moscow. However, the region lags by at least 30 percent in terms of overall digital capacity. These two clusters demonstrate the adequate development of all the advantages of innovation-territorial clusters: Holistic production chains are being created, targeted scientific developments are being conducted, and working talents are being cultivated in the masses. The final stage of the cyclical development of these regions involves the expected minimization of production costs and an increase in production profits. St. Petersburg, just like its competitor, has a confident educational potential of the population, a stable reserve of educational personnel, and a colossal innovative activity of organizations based on the active participation of the cluster in the federal scientific and technical policy [6]. However, as in Moscow, the minimum effectiveness of innovation activity and limited costs for science and innovation from the state budget are present, as shown in Table 4.

SAINT PETERSBURG
Free innovation index	0.530
Rank	(3) 2
1. SOCIOECONOMIC CONDITIONS FOR INNOVATIVE ACTIVITIES
3	1.1 Main macroeconomic indicators	0.349	15
0.527	1.2 Educational potential of the population	0.689	5
	1.3 The potential of digitalization	0.669	2
2. SCIENTIFIC AND TECHNICAL POTENTIAL
(3) 4	2.1 Funding for research and development	0.429	5
0.486	2.2 Science staff	0.544	4
	2.3 R&D performance	0.505	7
3. INNOVATION
3	3.1 Innovative activity of organizations	0.829	2
0.584	3.2 Small innovative business	0.533	8
	3.3 Technological innovation costs	0.639	17
	3.4 Innovation performance	0.257	26
4. EXPORT ACTIVITY
1	4.1 Export of goods and services	0.514	10
0.579	4.2 Knowledge export	0.666	2
5. QUALITY OF INNOVATION POLICY
(9) 10	5.1 Normative legal framework for innovation policy	0.500	50
0.497	5.2 Organizational support of innovation policy	0.500	13
	5.3 Budget spending on science and innovation	0.126	32
	5.4 Participation in federal science, technology, and innovation policy	0.718	3

Table 4

Aggregated innovation indices of St. Petersburg.

Source: Calculated by the authors based on the numbers from [10].

Still, St. Petersburg and Moscow retain leadership in export activities. However, the northern capital has many foreign students who have achieved more impressive results in technology exports and foreign patents for inventions.

St. Petersburg, the Republic of Tatarstan, the Tomsk and Nizhny Novgorod regions, the Chuvash Republic, Moscow, and the Republic of Mordovia confidently retain leading positions in innovation. Furthermore, the ratings of the Russian Cluster Observatory demonstrate that the Republic of Tatarstan has a rich educational potential for the population and substantive costs for technological innovation. In addition, this region surprises by the absolute security of the regulatory framework and the excellent organizational support for innovation policy [6]. However, the effectiveness of innovation and scientific research is at most the levels of other leading clusters in Russia, as shown in Table 5.

Republic of Tatarstan
Free innovation index	0.498
Rank	(2) 3
1. SOCIOECONOMIC CONDITIONS FOR INNOVATIVE ACTIVITIES
(2) 4	1.1 Main macroeconomic indicators	0.427	4
0.516	1.2 Educational potential of the population	0.677	8
	1.3 The potential of digitalization	0.538	6
2. SCIENTIFIC AND TECHNICAL POTENTIAL
(13) 17	2.1 Funding for research and development	0.220	41
0.357	2.2 Science staff	0.420	12
	2.3 R&D performance	0.476	9
3. INNOVATION
(1) 4	3.1 Innovative activity of organizations	0.582	6
0.551	3.2 Small innovative business	0.343	28
	3.3 Technological innovation costs	0.862	5
	3.4 Innovation performance	0.475	5
4. Export activity
(9) 11	4.1 Export of goods and services	0.457	19
0.482	4.2 Knowledge export	0.514	11
5. Quality of innovation policy
1	5.1 Normative legal framework for innovation policy	1	1
0.581	5.2 Organizational support of innovation policy	1	1
	5.3 Budget spending on science and innovation	0.179	19
	5.4 Participation in federal science, technology, and innovation policy	0.655	7

Table 5.

Aggregated innovation indices of the Republic of Tatarstan.

Source: Calculated by the authors based on the numbers from [10].

Russian use of the cluster approach in regional development is problematic because of its necessity to constantly balance the complexity and rigidity of regional control and maximize the synergy of all the included institutions.

Before the progressive development of clusters tried to optimize domestic enterprises’ position inside the production value chain, but it only contributed to an increase in the processing of extracted raw materials, import substitution, and an increase in the localization of assembly plants. It also increased the nonprice competitiveness of domestic goods and services and strengthened partnerships between government agencies and private entrepreneurs in various regions of the Russian Federation. Only now, after achieving limited results, the government focused more on the original and locally produced innovative outputs.

Under growth conditions, it is possible to form miniature clusters of progress or areas of lesser decline even within the limitations of a spontaneous economic decline. This is another positive aspect of cluster policy—structural resistance to the decline in the quality of the world economy caused by disruptive political events. Because of that, for many years, raw material enterprises have been steadily forming the dominant clusters’ role, accounting for a significant part of the Russian GDP [6].

Therefore, this localized economy is a reliable and safe source of accumulated horizontal budgets for implementing other innovative cluster policies. With the successful operation of the cluster, quantitative and qualitative aspects contribute to the effective development of organizations in other areas of scientific and industrial activity. After achieving impressive results in industrial clustering, Russia can afford to use stable capital to develop commodity-oriented and innovation-based clusters, which could serve as a driving force behind the further successful technological development of the country [6].

The possibility of obtaining the lowest unit cost of production compared to other industries in the same region confirms the objective advantage of forming development clusters, which creates a favorable situation for Russia in the domestic and foreign markets. Support for cluster development is currently a legitimate priority of the state policy for the country’s socioeconomic development. The foreign practice has also proven that forming and developing innovative clusters is an effective mechanism for attracting local and foreign direct investments. This trend has contributed to the foreign economic integration of Russia with China and the accumulation of infrastructure and human resources in the state. Such and other activities allow the construction of a network of competitive suppliers and service organizations to ensure that business needs are adequately considered within the framework of regional and global planning mechanisms. Thus, the increase in labor productivity, the formation of new companies, and the creation of new jobs should now be heavily encouraged.

Presently, most of the constituent entities of the Russian Federation are strengthening their positions in the world market and participating in the international exchange of information. As a result, more than 80% of regions increased their export activity, including the export of noncommodity goods, and more than 60% noted an improvement in the export of services and innovative products at the time of 2021. At the same time, more than 90% of the subjects of the Russian Federation increased the export of technologies, three-quarters of the subjects began to export educational services actively, and two-thirds of the subjects began to apply for patents abroad [6]. However, their absolute numbers could be higher for a country of its scale.

As world practice shows, one of the platforms for innovation clusters is the system that encourages operational interaction with jurisdictions and state-owned enterprises. Since Russia already has created some of the required government and regional bodies, it could be developed even further, albeit with specific significant changes. With synergies from all institutions, the development of clusters could gradually open opportunities to achieve continuous optimization of domestic enterprises in the production value chain and increase the localization of assembly lines. In addition to these benefits, improvements in domestic goods and nonprice competitive service levels and strengthening public-private partnerships can be obtained.

Based on the Russian innovation policy by 2022, the fundamental problems are the overall low demand for innovation and the structural inefficiency of the Russian economy—a tendency to purchase finished equipment abroad, which does not contribute to introducing new developments. On average, for pilot innovation clusters, increasing the level of the leading macroeconomic indicators is critical. Neither the private nor the public sector paid due attention to innovation spending because the level of corporate innovation activity is significantly lower than the indicators of the leading countries in the same field. And the costs of technological innovation do not shine with high performance [10].

However, the potential of digitalization, the staff of science, and the effectiveness of scientific research are at a moderate level. When reviewing averages, the previously described problem with insufficient funding for R&D and a general deficit in budget spending on science and innovation, which provokes lower results in innovative activity, is once again apparent. The export of goods and services and knowledge export have good indicators, but the share of innovative products in these batches needs to meet expectations.

The described trends dictate the need to streamline the current innovation policy, shifting the focus from increasing the total amount of support to using flexible and experimental approaches to solving critical issues of innovation development. The current regulatory framework for innovation policy must provide the means by the organizational state strategy [6].

The taken averages of the leading indicators for the most pivotal and successful clusters included in Table 6 suggest that the state government’s goals, missions, and strategies could be more realistic.

AVERAGE PERFORMANCE OF PILOT INNOVATION CLUSTERS IN RUSSIA
Free innovation index	0.4235
1. SOCIOECONOMIC CONDITIONS FOR INNOVATIVE ACTIVITIES
0.445733	1.1 Main macroeconomic indicators	0.36305
	1.2 Educational potential of the population	0.56385
	1.3 The potential of digitalization	0.4103
2. SCIENTIFIC AND TECHNICAL POTENTIAL
0.391067	2.1 Funding for research and development	0.34285
	2.2 Science staff	0.41965
	2.3 R&D performance	0.4107
3. INNOVATION
0.437513	3.1 Innovative activity of organizations	0.43035
	3.2 Small innovative business	0.4081
	3.3 Technological innovation costs	0.583
	3.4 Innovation performance	0.3286
4. EXPORT ACTIVITY
0.448425	4.1 Export of goods and services	0.4276
	4.2 Knowledge export	0.46925
5. QUALITY OF INNOVATION POLICY
0.51695	5.1 Normative legal framework for innovation policy	0.8
	5.2 Organizational support of innovation policy	0.50515
	5.3 Budget spending on science and innovation	0.192
	5.4 Participation in federal science, technology, and innovation policy	0.57065

Table 6.

Average values of innovative and other work of pilot clusters.

Source: Calculated by the authors based on the numbers from [12].

According to the Global Innovation Index 2020, Russia ranks 47th out of 131 countries in innovative development, only two percentage points higher than in 2015. Russia’s cluster development indicators are low compared to the leading countries: 95th in the world, the concentration of clusters barely matters—0.3, and the overall GII score is 3.4 out of 7.

However, in the last GII-2021 reports, Russia has risen by two more positions over the years, taking 45th place out of 132 countries. Over the 5 years, the scoring practices were changed in the country composition analysis; ranking methodologies were adjusted, multiple indicator scores were changed, missing values in the data were calculated, and so on. In contrast to these alterations, Russia’s position was stable [12].

A positive observation here is that increasing the efficiency in all areas of scientific, innovative, and creative activity has narrowed the gap between the leading countries in innovation [12]. In a table of 132 countries, Russia ranked 45th in the report (between Vietnam and India). At the same time, Russia occupies high positions in various GII indicators, specifically, higher education development (14th), trade, competition, and market size (17th), knowledge production (26th), research and development (33rd), information and communication technology (36th), and Internet ideas (47th) [12].

At the same time, the indicators of the Russian Federation are significantly lower in some respects. These include environmental sustainability (101st), regulatory environment (92nd), innovative communications (88th), and creative goods and services (81st) [12]. All of the rankings that are mentioned can be found in Table 7.

Global Innovation Index Russia 2021
45th rank
Institutes	Human capital and research	Infrastructure	Market sophistication	Business sophistication	Results of knowledge and technologies	Creative results
71	30	60	55	42	50	60
Corruption Perceptions Index 2021
29 points out of 100
Global Competitiveness Index 2021
43rd rank

Table 7.

Main ratings and indicators of economic and innovation in Russia, 2021.

Source: Table compiled by the author based on the information from [12, 13].

Thus, it is necessary to note the negative factors in the practice of the current innovation strategy, which are actively associated with the immaturity of the framework conditions for innovation. Weak institutional infrastructure, underdevelopment, the backwardness of the legislative framework in this area, and a low institutional investment activity illustrate the limited state for innovations [12].

In the current situation, only direct financing and tax incentives are considered state support, which, according to critics, reduces the incentive for firms to innovate and improve. The proof of this position is the inefficiency of the Skolkovo project. Due to the lack of a commercial impulse for independent survival, the experimental potential of the company is reduced, which minimizes the innovative activity of the entire region [7].

These shortcomings are manifested in the formation of uneven clusters, which exacerbates the fragmentation of the socioeconomic development of the territory of the Russian Federation. Also, administrative efficiency still needs to be improved and is a fundamental shortcoming of Russia’s competitiveness.

Just as there is no country in the world with a very high level of corruption and very competitive power, there is no country with a low level of corruption and low competitiveness. The main factors hindering national competitiveness are the inefficiency of the financial and banking sector and the inequality of institutions [12].

Also, one of the critical constraints to doing business successfully can be high levels of corruption, which is highly correlated with black market activity and overregulation. Of course, 2 years after the devastating COVID-19 pandemic, the Corruption Perceptions Index (CPI) still shows a flat rate worldwide. Despite promises on paper, 180 countries have yet to progress in fighting corruption over the past decade. Moreover, one of these countries is Russia, which scores 29 out of 100, where 100 is equivalent to a state administration that is clean of corruption [14].

In addition, significant shortcomings of the innovation policy of the Russian Federation are administrative barriers to doing business, insufficient protection of property rights, high tax rates, demanding access to financial resources, inflation, and shortcomings of the current tax system.

4. Empirical research

To assess the factors of influence of cluster elements and management organizations on innovative development and the level of competitiveness of national innovation systems in Russia and world countries, an experimental decision was made to implement statistical analysis using multiple linear regression models [15].

The empirical objectives of the study favored the determination of the following hypotheses, which reflect the meaning of the updated research questions:

• First null hypothesis (H₀-1)—there are no statistical effects of socioeconomic, scientific, technical, innovation, export, and regulatory factors on the innovation index of Russian cluster regions.

• The first alternative hypothesis (H₁-1) is a statistical effect of socioeconomic, scientific, technical, innovation, export, and regulatory factors on the innovation index of Russian cluster regions.

For the implementation of statistical analysis, 29 linear regression models were created with the participation of 119 variables, which were constructed from 9 databases containing 6656 values. Data on Russian innovation clusters were obtained from the Internet portals of the Russian Cluster Observatory [6], the values of innovation subindices and the Global Innovation Index (GII) were found on the World Intellectual Property Organization (WIPO) page, and the competitiveness ratings of states were found in the data from IMD World Competitiveness Center [12]. All data came out in 2021 and represented the most recent information provided by the publishers. This decision was made due to the need for alternative options found in the free Internet access.

To avoid the adverse effects of autocorrelation, multicollinearity, heteroscedasticity, and the size of outliers on the objectivity of the obtained statistical results, tests are carried out on the assumptions of the quality of the used models [16]. The Durbin-Watson test was used, the coefficients were studied by the test on variance inflation factor, and the graphical analysis was produced. For this reason, 23 models were assessed and established, which leveled out the conflicting interactions of variables in the implementation of regression analysis [17]. The workflow of the operations performed in the context of regression analysis can be seen in Figure 3.

Figure 3.

Methodical thinking behind the regression analysis. Source: The figure and research were designed and carried out by the authors.

The threat of unreliability of the obtained results is potentially present due to the limited number of observations, which eliminates the possibility of removing every cause of the statistical outliers when improving the quality of regression models. Another source of doubt about the validity of the obtained results is the indices from the Russian Cluster Observatory, WIPO, and IMD, on which databases the analysis was executed [13]. Also, the adequacy of using the WIPO’s data regarding the countries’ innovation achievements to represent the success of the countries’ clusters can come under scrutiny. However, after conducting the qualitative analysis, the author believes this approach can be justified since the innovation clusters significantly impact the countries’ innovation development.

In the first section of the regression analysis, 20 regions were selected to uncover the impact of variables on the innovation index of clusters in Russia. These regions were initiated according to pilot projects of the country’s innovative cluster development [3].

To confirm or reject the first hypothesis, the relationship among the leading macroeconomic indicators, the educational potential of the population, the financing of research and development, the effectiveness of research and development, the export of goods and services, the regulatory and legal framework for innovation policy, the organizational support for innovation policy, budget expenditures on science, and innovations on the dependent variable—innovation index of cluster regions of Russia—is explored. This procedure is presented in Table 8.

Multiple linear regression on generalized indicators of Russian clusters
lm(formula = i_index ∼ main_macro_indic_1_1 + educ_pot_pop_1_2 + fund_res_dev_2_1 + res_dev_ef_2_3 + exp_gds_srvs_4_1 + norm_lgl_frmwk_innov_pol_5_1 + org_sup_innov_pol_5_2 + bdgt_spndg_scien_innov_5_3, data = clustdata_0_1)
Residuals:	Min	1Q	Median	3Q	Max
	−0.037898	−0.017679	−0.005385	0.017936	0.052210
Coefficients	Estimate	Standard Error	t-value	Pr (>|t|)	Significance
main_macro_indic_1_1	0.156983	0.158810	0.988	0.34416
educ_pot_pop_1_2	0.103351	0.093309	1.108	0.29166
fund_res_dev_2_1	0.085849	0.066333	1.294	0.22211
res_dev_ef_2_3	0.364625	0.100133	3.641	0.00388	**
exp_gds_srvs_4_1	0.217058	0.108300	2.004	0.07029	.
norm_lgl_frmwk_innov_pol_5_1	0.034244	0.044993	0.761	0.46261
org_sup_innov_pol_5_2	−0.011056	0.024747	−0.447	0.66372
bdgt_spndg_scien_innov_5_3	0.155944	0.082524	1.890	0.08543	.
Significance codes	0 ‘***’	0.001 ‘**’	0.01‘*’	0.05‘.’	0.1
Residual standard error:	0.03394 on 11 degrees of freedom
Multiple R-squared	0.8297		Adjusted R-squared	0.7058
F-statistic	6.698 on 8 and 11 DF		p-Value	0.002566

Table 8.

First variables’ influence on the innovation index of clusters in Russia.

Source: Table compiled by the author based on the summary from RStudio.

According to the analysis of all variables, the effectiveness of research and development has a high statistical significance (0.00388) with the regional innovation index. On average, each change in research and development performance is accompanied by an increase in the innovation index by 0.364625, while other variables remain the same.

The export of goods and services (0.07029) and budget expenditures on science and innovations (0.08543) have the minimum significance. The first variable provides a positive effect of 0.217058, and the second is 0.155944 on the performance indicator of the statistical model.

For these variables, p-values allow us to move away from the first null hypothesis (H₀-1) to accept that the variables are related to the innovation index.

Based on the adjusted R-square value (0.7058), the fitted model explains 70.58% of the statistical relationships of the linear regression variables. Also, the model’s p-value (0.002566 < 0.05) demonstrates the rejection of the null hypothesis about the absence of effects of variables on the result, which confirms the statistical reliability of this regression analysis.

Like the last time, a new group of variables was compiled to accept or refute the first hypothesis. It aims to explore the relationship between the leading macroeconomic indicators, the potential of digitalization, scientific personnel, the innovative activity of organizations, small innovative businesses, the cost of technological innovation, the effectiveness of the innovative activity, export of knowledge, and participation in federal scientific, technical, and innovation policy with the same dependent variable. This procedure is presented in Table 9.

Multiple linear regression on generalized indicators of Russian clusters
lm(formula = i_index ∼ main_macro_indic_1_1 + pot_digit_1_3 + scien_pers_2_2 + innov_act_org_3_1 + sml_innov_bus_3_2 + tech_innov_cost_3_3 + effect_innov_3_4 + know_exp_4_2 + part_fed_scien_tech_innov_pol_5_4, data = clustdata_0_2)
Residuals:	Min	1Q	Median	3Q	Max
	−0.036253	−0.005312	0.002120	0.007110	0.033507
Coefficients	Estimate	Standard error	t-value	Pr (>|t|)	Significance
main_macro_indic_1_1	0.10712	0.10206	1.050	0.3186
pot_digit_1_3	0.10810	0.06133	1.763	0.1084
scien_pers_2_2	0.04794	0.12276	0.391	0.7044
innov_act_org_3_1	0.11851	0.08768	1.352	0.2063
sml_innov_bus_3_2	−0.04066	0.06137	−0.663	0.5226
tech_innov_cost_3_3	0.08538	0.03785	2.256	0.0477	*
effect_innov_3_4	0.01163	0.07793	0.149	0.8843
know_exp_4_2	0.01354	0.08315	0.163	0.8739
part_fed_scien_tech_innov_pol_5_4	0.12489	0.08778	1.423	0.1852
Significance codes	0 ‘***’	0.001 ‘**’	0.01 ‘*’	0.05 ‘.’	0.1
Residual standard error:	0.02217 on 10 degrees of freedom
Multiple R-squared	0.9339		Adjusted R-squared	0.8744
F-statistic	15.7 on 9 and 10 DF		p-Value	0.0000884

Table 9.

Second group’s variables’ influence on Russia’s innovation index.

Source: Table compiled by the author based on the summary from RStudio.

According to the analysis of all predictors, the moderate statistical significance (0.0477) with the innovation index of the region is observed when looking at the cost of technological innovation. On average, each change in the cost of technological innovation is accompanied by an increase in the innovation index by 0.08538 when other variables remain unchanged. Also, the p-value allows us to move away from the first null hypothesis (H₀–1) to accept that the predictor has a relationship with the output indicator.

Based on the adjusted R-square value (0.8744), the fitted model explains 87.44% of the statistical relationships of the linear regression variables. Also, the model’s p-value (0.0000884 < 0.05) demonstrates the rejection of the null hypothesis about the absence of effects of variables on the result, which confirms the statistical reliability of this regression analysis.

A review of the subindices of the new group of variables is carried out to accept or reject the first hypothesis. It is done to see if there is any impact on the dependent variable from the share of organizations implementing technological innovations, the share of organizations implementing nontechnological innovations, the share of organizations developing technological innovations on their own, the share of organizations participating in scientific cooperation, the share of small enterprises implementing technological innovations, the intensity of costs for technological innovations, the share of innovative products, the share of new innovative products, and the share of organizations that have reduced material and energy costs as a result of innovation. This procedure is presented in Table 10.

Multiple linear regression on detailed indicators of Russian clusters
lm(formula = i_index ∼ shre_org_tech_innov_3_1_1 + shr_org_impl_nontech_innov_3_1_2 + shr_org_dev_tech_innov_own_3_1_3 + shr_org_part_sci_coop_3_1_4 + shr_sml_entrp_tech_innov_3_2_1 + cst_int_tech_innov_3_3_1 + shr_innov_prod_3_4_1 + shr_innov_prod_new_markt_3_4_2 + shr_org_rduc_mat_enrg_csts_bcs_innov_3_4_3, data = clustdata_1)
Residuals:	Min	1Q	Median	3Q	Max
	−0.052539	−0.008196	0.000218	0.008474	0.030817
Coefficients	Estimate	Standard error	t-value	Pr (>|t|)	Significance
shre_org_tech_innov_3_1_1	0.135564	0.075956	1.785	0.104614
shr_org_impl_nontech_innov_3_1_2	−0.050623	0.097501	−0.519	0.614910
shr_org_dev_tech_innov_own_3_1_3	0.042541	0.075620	0.563	0.586119
shr_org_part_sci_coop_3_1_4	0.119038	0.051268	2.322	0.042635	*
shr_sml_entrp_tech_innov_3_2_1	−0.092058	0.050555	−1.821	0.098626	.
cst_int_tech_innov_3_3_1	0.126316	0.047231	2.674	0.023314	*
shr_innov_prod_3_4_1	−0.005347	0.031848	−0.168	0.870012
shr_innov_prod_new_markt_3_4_2	−0.032560	0.030977	−1.051	0.317939
shr_org_rduc_mat_enrg_csts_bcs_innov_3_4_3	0.046774	0.051330	0.911	0.383605
Significance codes	0 ‘***’	0.001 ‘**’	0.01 ‘*’	0.05 ‘.’	0.1 “
Residual standard error:	0.02576 on 10 degrees of freedom
Multiple R-squared	0.9108		Adjusted R-squared	0.8306
F-statistic	11.35 on 9 and 10 DF		p-value	0.0003683

Table 10.

Third group’s variables’ influence on Russia’s innovation index.

Source: Table compiled by the author based on the summary from RStudio.

Looking at the analysis results of all variables, the moderate statistical significance (0.042635) and (0.023314) can be observed in the share of organizations participating in scientific cooperation and the intensity of costs for technological innovation. These variables increase the innovation index of the region by 0.119038 and 0.126316 when other variables remain unchanged. For these variables, the p-value allows us to move away from the first null hypothesis (H₀-1) to accept that the variables are related to the output variable.

The minimum significance (0.098626) with an impact (−0.092058) on the innovation index also has the share of small enterprises that carried out technological innovations.

Based on the adjusted R-square value (0.8306), the fitted model explains 83.06% of the statistical relationships of the linear regression variables. Also, the model’s p-value (0.0003683 < 0.05) demonstrates the rejection of the null hypothesis about the absence of effects of variables on the result, which confirms the statistical reliability of this regression analysis.

Going forward, the detailed group of variables is reviewed to see if they influence the innovation index of Russian cluster regions. The group includes the export of goods, nonresource exports of goods, exports of services, the share of exports in the volume of innovative products, patent activity abroad, the export of technologies, and the share of foreign students of higher education programs. This procedure is visualized in Table 11.

Multiple linear regression on detailed indicators of Russian clusters
lm(formula = i_index ∼ exp_gds_4_1_1 + non_commty_exp_gds_4_1_2 + exp_srvs_4_1_3 + shr_exp_vol_innov_prod_4_1_4 + pat_act_abrd_4_2_1 +exp_tech_4_2_2 + shr_int_stud_hi_ed_prog_; 4_2_3, data = clustdata_1)
Residuals:	Min	1Q	Median	3Q	Max
	−0.078164	−0.015184	−0.000364	0.018768	0.065586
Coefficients	Estimate	Standard error	t-value	Pr (>|t|)	Significance
exp_gds_4_1_1	0.06137	0.06471	0.948	0.36162
non_commty_exp_gds_4_1_2	−0.20444	0.16858	−1.213	0.24857
exp_srvs_4_1_3	0.12507	0.05726	2.184	0.04952	*
shr_exp_vol_innov_prod_4_1_4	0.07178	0.06463	1.111	0.28852
pat_act_abrd_4_2_1	0.10925	0.05808	1.881	0.08445	.
exp_tech_4_2_2	0.03801	0.04454	0.853	0.41021
shr_int_stud_hi_ed_prog_4_2_3	0.03211	0.08702	0.369	0.71857
Significance codes	0 ‘***’	0.001 ‘**’	0.01 ‘*’	0.05 ‘.’	0.1 “
Residual standard error:	0.04404 on 12 degrees of freedom
Multiple R-squared	0.6871		Adjusted R-squared	0.5046
F-statistic	3.765 on 7 and 12 DF		p-value	0.02154

Table 11.

Fourth group’s variables’ influence on Russia’s innovation index.

Source: Table compiled by the author based on the summary from RStudio.

There is visible a moderate statistical significance (0.04952) of the export of services. This variable increases the dependent variable by 0.12507 when other variables remain unchanged. Also, patent activity abroad has a minimum significance (0.08445) and influence (0.10925) on the output variable.

For these variables, the p-value allows us to accept that the variables are related to the innovation index.

The fitted model explains 50.46% of the statistical relationships of the linear regression variables. Also, the model’s p-value (0.02154 < 0.05) demonstrates the rejection of the null hypothesis about the absence of predictor effects on the final result, confirming the moderate statistical reliability of this regression analysis.

Further, the review of the variables from the new group of subindices is carried out. The model tests if there is an impact on the innovation index of clusters in Russia coming from the innovation development strategy, allocated territories for innovation development, the regional law on innovation, the innovation support program, the coordinating body for innovation policy, the regional institute for innovation development, the share of allocations for science in the regional budget, the share of the federal budget in the costs of technological innovation, the share of the regional budget in the cost of technological innovation, the number of innovative projects that received federal support, the number of federal development institutions that support innovative projects, the federal funding of innovative projects, the number of territories for innovative development with federal status, and the number of objects of innovative infrastructure to support SMEs. The results of the analysis are shown in Table 12.

Multiple linear regression on detailed indicators of Russian clusters
lm(formula = i_index ∼ innov_strtg_dev_5_1_1 + ded_ars_innov_dev_5_1_2 + reg_innov_law_5_1_3 + sup_prog_innov_5_1_4 + coord_bdy_innov_pol_5_2_1 + reg_instit_innov_dev_5_2_2 + apprp_shr_sci_reg_; budg_5_3_1 + shr_fed_budg_tech_innov_csts_5_3_2 + shr_reg_bdg_rech_innov_csts_5_3_3 + num_innov_; proj_recid_fed_sup_5_4_1 + num_fed_instit_dev_sup_innov_proj_5_4_2 + fed_fund_innov_proj_5_4_3 + num_innov_terris_dev_fed_stats_5_4_4 + num_obj_innov_infra_sme_sup_5_4_5, data = clustdata_1)
Residuals:	Min	1Q	Median	3Q	Max
	−0.061067	−0.008884	−0.002866	0.010845	0.037298
Coefficients	Estimate	Standard error	t-value	Pr (>|t|)	Significance
innov_strtg_dev_5_1_1	0.004517	0.027741	0.163	0.8760
ded_ars_innov_dev_5_1_2	−0.016090	0.028542	−0.564	0.5934
reg_innov_law_5_1_3	0.046156	0.053114	0.869	0.4182
sup_prog_innov_5_1_4	0	0	0	0
coord_bdy_innov_pol_5_2_1	0.031588	0.026327	1.200	0.2754
reg_instit_innov_dev_5_2_2	0.017134	0.028138	0.609	0.5649
apprp_shr_sci_reg_budg_5_3_1	−0.033758	0.057385	−0.588	0.5778
shr_fed_budg_tech_innov_csts_5_3_2	−0.015759	0.046658	−0.338	0.7471
shr_reg_bdg_rech_innov_csts_5_3_3	0.832111	0.815242	1.021	0.3468
num_innov_proj_recid_fed_sup_5_4_1	0.277054	0.131118	2.113	0.0790	.
num_fed_instit_dev_sup_innov_proj_5_4_2	0.095848	0.092846	1.032	0.3417
fed_fund_innov_proj_5_4_3	−0.092737	0.118653	−0.782	0.4642
num_innov_terris_dev_fed_stats_5_4_4	0.054240	0.095728	0.567	0.5915
num_obj_innov_infra_sme_sup_5_4_5	−0.047130	0.057579	−0.819	0.4443
Significance codes	0 ‘***’	0.001 ‘**’	0.01 ‘*’	0.05 ‘.’	0.1 “
Residual standard error:	0.03635 on 6 degrees of freedom
Multiple R-squared	0.8934		Adjusted R-squared	0.6624
F-statistic	3.868 on 13 and 6 DF		p-Value	0.05324

Table 12.

Fifth group’s variables’ influence on Russia’s innovation index.

Source: Table compiled by the author based on the summary from RStudio.

Of all the predictors of this group, the potential minimum statistical significance of 0.0790 has the number of federal development institutions that support innovative projects. This variable raises the variable used as the output by 0.277054 when other factors remain unchanged. It is also decided to accept that the predictors are associated with the dependent variable based on the p-value.

When looking at the adjusted R-squared value (0.6624), it can be observed that the fitted model explains 66.24% of the statistical relationships of the grouped variables of the linear regression. However, the model’s p-value (0.05324 > 0.05) does not demonstrate confidence in rejecting the null hypothesis about the absence of predictor effects on the final result, which casts doubt on the statistical reliability of the model.

5. Conclusion

The authors revealed the features of the transformation of the innovation policy of developed countries under the influence of multiple predictors in the economy and developed directions for improvement of the cluster policy in Russia. This work also concretizes the fundamental theoretical and methodological provisions that reveal the essence of clusters and their advantages and identify factors for transforming industry clusters into global clusters. Also, the innovative development of high-tech industries in the international economy was assessed through statistical and qualitative analysis methods.

A qualitative analysis made it possible to identify the features of Russia’s innovative clustering and its role in increasing the country’s global competitiveness. After that, the status was determined, and the tools for forming high-tech industries in Russia were investigated. Despite the growth of clusters in certain regions of the country, it was shown that the high-tech cluster policy in Russia has assertive imperfections. The difficulty of launching individual cluster initiatives is ever-present with the limited globalization of innovative products by management bodies and business organizations. In addition, technological cooperation between Russian and foreign enterprises is even more critical.

The authors assessed the influence of cluster elements and management organizations on the innovative development of national innovation systems in Russia and the world countries. The sources of a positive impact on the productivity of the Russian innovation policy were identified: a high level of education of the population and a high fundamental scientific potential. The interdisciplinary research system of the institutions could be excellent with the improvement of the policies and proper equipment for the innovation projects.

Appendix

Group	Variable	Explanation
Group 1. Generalized variables’ influence on the innovation index of clusters in Russia.; Variables represent the following fields: Macroeconomics, educational potential of the population, financing of scientific research and development, efficiency of scientific research and development, export of goods and services, regulatory legal framework for innovation policy, organizational support for innovation policy, and budget costs for science and innovation.	main_macro_indic_1_1	GRP per employee; the coefficient of renewal of fixed assets; Share of employed in high-tech industries; share of people employed in knowledge-intensive service industries.
	educ_pot_pop_1_2	The proportion of the adult population with higher education; The number of students in higher education programs per 10 thousand people; the number of students in higher education programs per 10 thousand people; coverage of the employed population with continuing education; number of students in secondary vocational education programs per 10,000 people; share of students in mid-career STEM programs.
	fund_res_dev_2_1	Share of research and development costs in GRP; share of research and development costs in GRP; business share in research and development funding; salary in science as a percentage of the average in the region.
	res_dev_ef_2_3	Publication activity of researchers; patent activity; development of advanced manufacturing technologies.
	exp_gds_srvs_4_1	Export of goods; noncommodity export of goods; export of services; share of exports in the volume of innovative products.
	norm_lgl_frmwk_innov_pol_5_1	Innovative development strategy; allocated territories for innovative development; regional law on innovation; innovation support program;
	org_sup_innov_pol_5_2	Coordinating body for innovation policy; regional institutes of innovative development.
	bdgt_spndg_scien_innov_5_3	The share of allocations for science in the budget of the region; The share of the federal budget in the cost of technological innovation; the share of the federal budget in the cost of technological innovation.
Group 2. Variables’ influence on Russia’s innovation index.; Variables represent the following fields: Macroeconomics, digitalization potential, science personnel, activity in the field of technological and nontechnological innovations, small innovative business, costs for technological innovations, innovation performance, export of knowledge, participation in federal scientific, technical, and innovation policy.	main_macro_indic_1_1	GRP per employee; GRP per employee; share of employed in high-tech industries; The share of people employed in knowledge-intensive service industries.
	pot_digit_1_3	The share of organizations using broadband access with speeds above 100 Mbps; percentage of organizations providing digital skills training to staff; percentage of organizations providing digital skills training to staff.
	scien_pers_2_2	Share of employed in research and development; share of young researchers; percentage of researchers with advanced degrees
	innov_act_org_3_1	Share of organizations that carried out technological innovations; percentage of organizations implementing nontechnological innovations; Percentage of organizations that developed technological innovations on their own; share of organizations participating in scientific cooperation
	sml_innov_bus_3_2	Share of small enterprises implementing technological innovations
	tech_innov_cost_3_3	Technological innovation spending intensity
	effect_innov_3_4	Share of innovative products; share of innovative products new to the market; percentage of organizations that have reduced material and energy costs due to innovation.
	know_exp_4_2	Patent activity abroad; export of technologies; percentage of foreign students in higher education programs.
	part_fed_scien_tech_innov_pol_5_4	A number of innovative projects that received federal support; number of federal development institutions supporting innovative projects; federal financing of innovative projects; number of territories for innovative development with federal status; number of innovative infrastructure facilities to support SMEs.
Group 3. Variables’ influence on Russia’s innovation index.; Variables represent the following fields Technological and nontechnological innovation activity, small innovative business, technological innovation spending, and innovation performance.	shre_org_tech_innov_3_1_1	Percentage of organizations implementing technological innovations
	shr_org_impl_nontech_innov_3_; 1_2	Percentage of organizations implementing nontechnological innovations
	shr_org_dev_tech_innov_own_3_1_3	Percentage of organizations developing technological innovations in-house
	shr_org_part_sci_coop_3_1_4	Share of organizations participating in scientific cooperation
	shr_sml_entrp_tech_innov_3_2_1	Share of small enterprises implementing technological innovations
	cst_int_tech_innov_3_3_1	Cost intensity for technological innovation
	shr_innov_prod_3_4_1	Share of innovative products
	shr_innov_prod_new_markt_3_4 _2	Share of innovative products new to the market
	shr_org_rduc_mat_enrg_csts_bcs_innov_3_4_3	The share of organizations that reduced material and energy costs as a result of innovation
Group 4. Variables’ influence on Russia’s innovation index.; Variables represent the following fields: Export of goods and services and export of knowledge.	exp_gds_4_1_1	Export of goods
	non_commty_exp_gds_4_1_2	Noncommodity export of goods
	exp_srvs_4_1_3	Export of services
	shr_exp_vol_innov_prod_4_1_4	Share of exports in the volume of innovative products
	pat_act_abrd_4_2_1	Patent activity abroad
	exp_tech_4_2_2	Technology export
	shr_int_stud_hi_ed_prog_4_2_3	Share of foreign students in higher education programs
Group 5. Variables’ influence on Russia’s innovation index.; Variables represent the following fields: regulatory legal framework for innovation policy, organizational support for innovation policy, budget expenditures on science and innovation, and participation in federal science, technology, and innovation policy.	innov_strtg_dev_5_1_1	Innovative development strategy
	ded_ars_innov_dev_5_1_2	Allocated territories for innovative development
	reg_innov_law_5_1_3	Regional innovation law
	sup_prog_innov_5_1_4	Innovation support program
	coord_bdy_innov_pol_5_2_1	Coordinating body for innovation policy
	reg_instit_innov_dev_5_2_2	Regional institutes of innovative development
	apprp_shr_sci_reg_budg_5_3_1	The share of allocations for science in the budget of the region
	shr_fed_budg_tech_innov_csts_5_3_2	The share of the federal budget in the cost of technological innovation
	shr_reg_bdg_rech_innov_csts_5_3_3	The share of the regional budget in the cost of technological innovation
	num_innov_proj_recid_fed_sup_5_4_1	Number of innovative projects that received federal support
	num_fed_instit_dev_sup_innov_proj_5_4_2	Number of federal development institutions supporting innovative projects
	fed_fund_innov_proj_5_4_3	Federal financing of innovative projects
	num_innov_terris_dev_fed_stats_5_4_4	Number of territories for innovative development with federal status
	num_obj_innov_infra_sme_sup_5_4_5	Number of innovative infrastructure facilities to support SMEs

Table A1.

Indices used from Russian ranking innovation scoreboard [18].