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2CONSISTENT MANAGEMENT OF THE NATIONAL INTELLECTUAL CAPITAL AS A FACTOR OF STATE COMPETITIVENESS
Anna A. Chub
Financial University under the Government of the Russian Federation, Russia. E-mail: AACHub@fa.ru
Pavel Y. Makarov
The Russian Presidential Academy of National Economy and Public Administration, Russia. E-mail: makarovpu@ya.ru
Abstract. The study focuses on the ways to manage the national intellectual capital and its structure to ensure economic growth in a digital economy. The main goal of the study is to identify a group of countries with efficient development of the national economy due to systemic management of the national intellectual capital by establishing the relationship between its elements and gross domestic product. The study uses the methods of correlation and cluster analysis. It also uses the systematic approach and the approach of Edvinsson, L. and Lin, K. to the structuring and assessment of national intellectual capital. According to their approach, the intellectual capital includes human, market, process, and renewable capital. Correlation analysis revealed a high positive correlation between the available national intellectual capital and the level of economic development for developed countries, and no correlation for developing countries. The identified pattern for developed countries can be explained by the inherent emergence of intellectual capital, which these countries exploit to manage all its structural elements which, in turn, consolidates and fuels the development of the national economy. The cluster analysis identified a group of developed countries (Denmark, Norway, USA, Finland, Switzerland, Sweden), with leading positions in GDP due to the systemic management of national intellectual capital and prioritization of its process and human components.
Keywords: national intellectual capital, economic development, systemic effect, correlation analysis, cluster analysis. JEL codes: C21, Е61, G34, M12, O21
For citation: Chub, A., & Makarov, P. . (2020). Consistent management of national intellectual capital in digital economy. Journal of regional and international competitiveness, (1), 35-46. Retrieved from http://jraic.com/index.php/tor/article/view/6
Introduction
The current stage of development of the world community is defined by many contradictory trends, the more interesting of which are:
- globalization, which has sped up even more due to digitalization and IT development of society, and, in turn, has deepened the international division of labor and tightened the competition in the world markets;
- an increased growth of inter-country differentiation, because social resources are distributed unevenly, and that entails the aggravation of international relations due to opposing geopolitical and economic interests of states and nations;
- transition to innovative development and formation of knowledge economy, in which the main source of competitive advantage is the dynamic capabilities, representing the ability to create, integrate, and reconfigure external and internal competencies in order to ensure a rapid response to dynamic changes in the business environment through the implementation and/or use of innovation (Teece, Pisano, Shuen, 1997).
Under these conditions, highly qualified labor resources become the main source of development of socio-economic systems of any level, while their most important part - intellectual capital (IC) becomes of paramount importance as the most valuable and much more significant factor for the state economy than natural resources or accumulated wealth.
Thus, current national IC is the most significant parameter of economic development of the majority of developed countries. It is the main component of added value. Due to this, these countries invest more
and more into education, science, social support and welfare. At the same time, the role of IC in it only increases over time. In 1980s, intangible assets of developed markets accounted for up to 38% of the market capitalization of companies. By the early 2000s, their share increased to 84% (Molnar, 2004). The situation is similar at the macroeconomic level: as of 2015, the contribution of IC to the gross domestic product (GDP) of developed countries ranged from 52% to 72% (Stahle, Lin, 2015).
At the same time, despite Russia planning to «achieve the level of economic and social development suitable for Russia as a leading world power in the XXI century» and the efforts of state authorities during the last decade, according to a number of studies, the national IC contribution to GDP in Russia is 36% (Edvinsson, Yeh-Yun Lin, 2011), which is similar to developing countries, and most parts of IC in Russia have low level of development. Unfortunately, fuel and raw materials are still the cornerstones of Russian economy. The extremely weak competitive position of Russia in the global market of knowledge-intensive technologies, which are dominated by the G7 countries that control about 2/3 of the total turnover of these products, confirms that Russia has not yet created the necessary conditions for effective implementation of innovative projects aimed at the development and use of products that meet global standards.
The Russian authorities are aware that the control of IC at all levels of the national economy management is becoming increasingly important, and this is reflected in the management decisions of the country's leadership in recent years. This issue is therefore reflected in the 2018-2025 plans of Russia to develop scientifically and technologically. An additional Russian program regarding digital economy also addresses IC issues.
Russian scientists are also quite actively involved in solving the problems of managing the development of national IC. We have analyzed E-Library indexed (https://elibrary.ru) publications ranging from 2010 to 2018. The average number of studies devoted to the issues of IC development at the micro-, meso-, and macrolevels is about 330 (Fig. 1).
The data presented in Figure 1 shows that every two years, in the period from 2010 to 2016, the number of papers devoted to various aspects of development, management, and evaluation of IC grew by 20-40%. At the same time, the highest volume of such studies was in 2016 and declined in later years. We believe that this decline is not a decline in interest, but rather an increase in the quality and depth of these studies (including the need to match the level of publications to the expectations of international indexers), which is naturally reflected in their number.
500
450
400
350
300
250
200 179
150 -f
100
50
0 ■
2010
463
419
357
391
339
38
2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Figure 1. Dynamicsofstudieson intellectual capitalindexedbyE-Library
Source: composedbyauthors
We have analyzed the headlines of the mentioned papers (Fig. 2) and reviewed their contents and discovered that:
- there is a shift in emphasis of research topics from general theoretical issues of the concept and
structure of IC to practical issues of its assessment and improving the efficiency of management;
- the greatest number of studies presented on E-Library (48% avg.) is devoted to the issues of the concept, structure, and management of IC at the micro level, while the issues of IC management in the regional and sectoral aspect are studied the least actively (10% avg.);
- the share of studies on general issues of IC has decreased. Note that we have included papers with the titles such as: «The concept of intellectual capital: Prerequisites of formation and methodological specificity», «Knowledge and intellectual capital management», etc. A study of their contents, which are available on E-Library, showed that their authors discuss IC without considering one of the three levels of the economic system. It should also be pointed out that over time and probably due to the advances in establishing the main points of the intellectual capital theory in the Russian science, the authors began to identify the object of research more clearly, which is reflected in the titles of papers — the wording of the title allows to assign the study to the appropriate subject group without studying the content;
- there is a growing number of studies that assess the role of IC in the socio-economic development of the state. Thus, while in 2010 the number of articles on this topic was 15% of the total amount of work, in 2018 it increased to 22%. In our opinion, this is a response of academic community to modern economic environment and needs of society, when the issues of state management of national IC become one of the basic strategic goals of the country, which once again emphasizes the relevance of the research presented in this paper.
2018 49 9 22 20
2017 53 J—J— 10 20
2016 49 9 26 16
2015 45 12 22 21
2014 48 8 19 25
2013 48 8 15 29
2012 44 13 13 30
2011 45 10 14 31
2010 49 10 15 26
0 10 20 30 40 50 60 70 80 90 100
■ Organizational IK ■ Regional / industry level IK ■ National IK ■ General questions
Figure 2. Structure of topics of papers on intellectual capital indexed by E-Library
Source: composed by authors
Based on the above, it seems relevant and appropriate to study the issues of intellectual capital management at the macroeconomic level. One of the key points in this case is the problem of consistent management of intellectual capital. It is the contradiction between its multi-component and multi-subject nature (Tatarkin, 2010) and the need to ensure the balanced development of all its structural elements.
This study builds on an idea that intellectual capital management is emergent and therefore should be consistent in nature in order to enhance the economic growth of the national economy.
Based on this idea, the study aims to identify a group of countries for which the systemic impact on national intellectual capital ensures the effective development of the national economy. The following objectives have been set:
1. To select a methodology for assessing national IC that allows to analyze the relationship of economic
development of the state with IC and its structural components.
2. To conduct a correlation analysis to establish the relationship between components of IC and GDP as an indicator of growth of a national economy.
3. To conduct a cluster analysis to identify a group of countries that have experience of systemic management of national IC in order to assess the possibility of adaptation and further use of this management experience in Russia.
Study basis
To date, the concept of intellectual capital has become an important category in management and economy studies. At the same time, there are relatively few studies on stimulation of national IC. This leads us to believe that it is preferable to begin the formation of research methodology with clarification of the general approach to IC management. Let us analyze the views of leading experts on this issue (Table 1).
Table 1 — Examples of interpretations of «intellectual capital»
Emphasis on components Emphasis on integrity
1. Intellectual capital is a combination of human and structural capital (Edvinsson, 2005) 2. All non-monetary and non-physical resources fully or partially controlled by the enterprise and contributing towards the creation of value (Roos, 2010) 3. The sum of everything everybody in a company knows that gives it a competitive edge in the marketplace. This is the intellectual material (knowledge, information, intellectual assets, experience) that can be used to create wealth. This is the knowledge of employees, research team of experts or manual workers who have developed thousand different ways to improve the company's efficiency. Intellectual capital is knowledge as a dynamic human process, transformed into something valuable for the company (Stewart, 1991) 4. People and the knowledge they possess, as well as their skills, connections and everything that helps to use them effectively (Kozyrev, 2006) 1. Knowledge and information that act as a «collective brain» and combine into a single whole organizational structure, information networks, intellectual property, knowledge of employees, experience, image, and reputation of the enterprise (Inozemtsev, 1995) 2. The intellectual wealth of the enterprise which predetermines its creative potential to create and implement intellectual and innovative products (Seleznev, 2007) 3. The system of relations regarding the production of new or enriched (updated) knowledge and intellectual abilities of individuals, collectives, and society as a whole (Tatarkin, 2010)
Source: composed by authors
When comparing the definitions of intellectual capital, the collective nature of this term is clearly traced. This is evidenced by the fact that most authors define intellectual capital as a set of certain components. The opposite approach to the definition of IC is to understand it as a kind of integrity. It should be noted that the latter is less frequently represented in the literature.
Also, from the definitions given in Table 1, it can be seen that they can be applied to the concept of «intellectual capital» both at the macro- and microeconomic levels of the organization of the economy.
Thus, the concept of «national intellectual capital» should be interpreted as a certain system of intangible resources presented in the form of abilities, knowledge, databases, organizational structures, relationships, etc., which act as sources of national welfare and can be used in the activities of economic actors at the micro, meso and macro levels of the national economy.
It should be noted that in the process of determining the concept of the IC category, we sought to emphasize the thesis of its collective nature because such a position is most relevant to the studied issue. In
this case, we understand IC not only as a logical superstructure over a set of elements, but also as a system of relations reflecting the dynamics and synergy of their interaction. Thus, our position is consistent with most interpretations of IC (e.g., Tatarkin, 2010) and does not align with a number of studies questioning the existence of systemic effects of intellectual capital (Stahle, 2008).
Methodology of intellectual capital assessment and empirical basis of research
The theoretical section did not provide the element-by-element composition of intellectual capital because while defining its structure, it is necessary to be expedient and emphasize the components that make its application useful for solving a specific research problem, provided that it does not contradict the established understanding of its essence (Stahle, 2008).
To date, there is no generally accepted methodology for assessing national intellectual capital. At the same time, numerous existing tools give correlating results and are partially interchangeable (Makarov, 2016). Under these circumstances, the development of a new methodology seems justified only for conducting analysis that is not possible with the existing approaches.
To test the working hypothesis of this study, we have set the following requirements for the assessment
tool:
- needs to be able to assess not only the intellectual capital of the country as a whole, but also its individual components;
- the resulting estimates, in turn, should be comparable both between the countries in question and over
time.
Compliance with the specified conditions will let us reveal the structural differences of national intellectual capital of different countries and analyze the dynamics of their change within the study period.
To achieve the goal of this paper, it seems rational not to overload the existing methodological toolkit with new development, but to test the hypothesis on the already formed database. Among the methodologies that have yielded data that is internationally recognized and consistent with the research conditions outlined above are the following: three versions of the National Intellectual Capital Index (NICI) model developed by N. Bontis (2004), D. Vezek, L. (2007), Edvinsson and K. Lin (2011), and the model «The Intellectual Capital Monitor» created by A. Andriessen and K. Stam (2008).
There is no detailed comparison of these methodologies due to limited number of pages, but we note that the National Intellectual Capital Index (NICI) model, as interpreted by Edvinsson and Lin, is preferred for the following reasons:
- studies using this model are widely presented in international publications and, despite a number of critical comments, NICI is recognized as a reliable methodology for assessing national intellectual capital;
- the model provides the most extensive database with panel data for 40 countries over 12 years, while other techniques produce either spatial estimates for smaller samples of countries (10 Arab countries in the Bontis model, 25 European countries in the Vezek model) or panel data on a smaller scale (16 countries over two years in the Andriessen and Stam model).
The limitation of the chosen methodology is purely technical and related to the relevance of the time series: 1995-2007 data and 2008-2010 partial data are available to study, while a number of indicators used in the calculation are based on expert estimates, which prevents obtaining comparable data independently. However, in our opinion, this limitation does not hinder the goals of this study, since only the total length of the time series and the sample size are relevant for hypothesis testing.
After justifying the chosen method, let us briefly describe the indicators presented in it.
Intellectual capital is assessed by calculating four indices ranging from 0 (minimum) to 10 (maximum) and characterizing the level of development of its components. The element-by-element composition of each of the structural components used in the formation of the indices is presented below:
- Human Capital (HC): Skilled labor force*, Skill development of the working population*, Literacy rate, Population with higher education, Ratio of teachers to students, Number of internet users, Education costs;
- Process Capital (PC): Competitive environment*, Government efficiency*, Intellectual property
rights protection*, Access to capital*, Number of personal computers per capita, Conditions for starting new businesses*, Number of mobile phone users;
- Market Capital (MC): Tax rates*; International venture capital share*; Openness to a foreign culture*; Globalization*; Transparency for analysis*; Country image*; Export and import of services;
- Renewal Capital (RC): Private R&D expenditure; Fundamental studies*; R&D expenditure relative to GDP; Number of researchers*; University-business cooperation*; Science papers*; Number of patents per capita.
In addition to the structural elements listed above, the NICI includes a composite index of national IC formed by adding up the above components, and a Financial Capital (FC) index, which is an estimate of GDP per capita (at purchasing power parity) put into a comparable form with other indices (scores from 0 to 10).
To test the hypothesis of this study, we have used the experimental base obtained using this methodology, which consists of panel data of the five listed indices for 40 countries in 1995-2007. Descriptive statistics of the experimental base is shown in Table 2. To improve representativeness, the sample is divided into developed and developing countries according to UN and World Bank classification.
Study tools, procedure, and results
The study was conducted in two stages, and data analysis was performed using IBM SPSS Statistics.
At the first stage, we have used the correlation analysis to assess the correlation of both the SC as a whole and its components with GDP. Due to the fact that the original variables do not fall into the category of normally distributed (a necessary condition for using Pearson correlation factor), and do not represent any type of monotonic sequence (a necessary condition for using Kendall correlation factor t), we have analyzed the data by calculating Spearman factor p.
As a result, we have found a strong correlation over the period under study for both the total IC indicator (Fig. 3), and its structural elements with GDP (Table 3) for the group of developed countries and weak for developing countries. The confidence level of the results obtained for developed countries is 0.000 - 0.003. This indicates that the identified patterns are not random and can be used for further analysis. In turn, the confidence of the calculated correlation for developing countries in all cases is above 0.05, which rejects the hypothesis that the estimates obtained are true and significant.
The data in Table 3 lets us draw the following conclusions regarding the relationship between IC and GDP for developed countries:
First, in our opinion, the fact that the average correlation of IC and FC is higher than similar values for individual components of IC indicates the structural elements of IC have a synergetic effect on the development of the national economy. It seems that this can be explained by the emergent nature of IC due to being a complex system. Thus, we can conclude that the management of the production and reproduction of national intellectual capital at the state level should be systemic and consistent. Second, the average correlation indicators show that among the structural elements of IC, process (PC) and human (HC) capitals have the strongest correlation with GDP (0.679 and 0.673, respectively).
Third, in our opinion, the component composition of process capital in the Edvinsson and Lin approach lets us interpret it as an institutional environment focused on creating institutions that overcome the spatial, functional, informational separation of management subjects and objects through an introduction of integrative processes aimed at defragmenting economic space by enhancing the coherence of objects, goals, knowledge, and actions (Kleiner, 2011). Thus, conditions («rules of the game») set and, what is especially important, actively supported are essential to utilize the economic potential of IC in developed countries by government agencies.
Table 2 — Descriptive statistics of the experimental base of the study
d <u All countries (40) Developed (26) Developing (14)
« « ^ S min max Avg Deviation min max Avg Deviation min max Avg Deviation
HC 3.160 8.800 6.089 1.277 1.216 8.478 4.082 2.096 3.160 6.851 4.885 0.770
d <u All countries (40) Developed (26) Developing (14)
s s ^ s min max Avg Deviation min max Avg Deviation min max Avg Deviation
PC 1.575 8.436 5.333 1.611 2.666 8.436 6.239 1.116 1.575 5.617 3.656 0.868
MC 3.019 8.727 5.665 1.003 3.786 8.727 5.961 0.927 3.019 7.140 5.117 0.906
RC 0.949 8.478 3.731 2.024 1.449 8.478 4.773 1.758 0.949 3.387 1.802 0.470
FC 6.759 10.00 9.137 0.729 9.067 10.00 9.587 0.209 6.759 9.074 8.299 0.597
Source: calculated by the authors
—•—developed countries developing countries
Figure 3. Dynamicsofcorrelation indicators of ICandGDPfordeveloped anddeveloping countries
Source:composedbytheauthors
Table 3 — CorrelationofICindex and itscomponentswith GDPovertheyears
Year Developedcountries Developingcountries
HC-FC PC-FC RC-FC MC-FC IC-FC HC-FC PC-FC RC-FC MC-FC IC-FC
1995 0.537 0.596 0.614 0.359 0.729 0.736 0.051 -0.13 0.424 0.49
1996 0.522 0.601 0.575 0.316 0.662 0.657 0.371 -0.073 0.455 0.499
1997 0.558 0.595 0.599 0.314 0.687 0.503 0.411 0.007 0.437 0.468
1998 0.59 0.584 0.52 0.418 0.694 0.371 0.165 -0.095 0.156 0.064
1999 0.563 0.694 0.505 0.569 0.743 0.477 0.358 0.103 0.235 0.433
2000 0.631 0.671 0.521 0.666 0.743 0.363 0.336 0.231 0.191 0.495
2001 0.683 0.676 0.509 0.624 0.747 0.449 0.275 0.121 0.209 0.319
2002 0.698 0.71 0.549 0.574 0.726 0.67 0.226 0.354 0.086 0.358
2003 0.662 0.686 0.521 0.62 0.706 0.675 0.095 0.257 -0.191 0.218
2004 0.591 0.695 0.506 0.558 0.693 0.64 0.253 0.134 -0.02 0.174
2005 0.614 0.752 0.545 0.662 0.706 0.767 0.134 0.108 -0.099 0.196
2006 0.582 0.77 0.57 0.692 0.76 0.723 0.292 0.301 -0.152 0.297
2007 0.603 0.791 0.532 0.614 0.610 0.798 0.363 0.323 -0.288 0.354
Avg 0.603 0.679 0.544 0.537 0.708 0.602 0.256 0.126 0.111 0.336
Source: calculated by the authors
Fourth, the identified patterns of high importance of HC confirm the conclusions obtained earlier by other researchers (G. Becker, T. Schultz, M. Blaug, M. Kritsky). They stated that investment in education, professional development, etc. is one the most important factors of economic development of the state because
of digital development of innovative economy.
At the second stage, in order to deepen the findings and identify specific countries whose experience in the future can be applied to Russia to ensure an appropriate level of national IC development, we have used cluster analysis to identify a group of countries that have achieved the greatest success in using total IC (IC-FC clustering), and then used it to test the assumption that they have achieved leadership by prioritizing process and human capitals.
Figure 3 presents a grouping of countries obtained by hierarchical clustering according to IC-FC indicators in 1995 (Fig. 3a) and 2005 (Fig. 3b). The year data are taken as an example, the graphs obtained for the entire study period (1995-2007) look similar. Clustering the group of developed countries according to the structural elements of PC and HC also yielded similar results.
fc
Figure 3a. Results of IC-FC cluster analysis of developed countries, 1995
Source: composedbythe authors
Finland " " •• .
Denmar^B
{ o° X
Sweden
Switzerland
'••.. Iceland "'' •._.
'■•••. ■ Singapore
° A
Australia "■•■••. Norway'
-. j « Netherlands
Japan '•
Taiwan Germany °"lada |re|and .
O ° O /
New Zealand Belgium
South Korea France
f 0
Czech Republic
Portugal Spain
O Italy
Greece
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—i-1-1-1-1-1—
9.DOO 9:200 9.400 OfOO 9.000 10.000
fc '
Figure 3b. Results of IC-FCclusteranalysisofdevelopedcountries,2005
Source: composedbytheauthors
Based on the results shown in Table 4, we have identified the countries whose experience supports the hypothesis of gaining leadership by prioritizing the development of process and human capital.
Table 4 — Leading countries in terms of IC and economic development
Country 1995 2005
IC Leader PC Leader HC Leader IC Leader PC Leader HC Leader
Denmark ✓ ✓ ✓ ✓ ✓ ✓
Norway ✓ ✓ ✓ ✓ ✓ ✓
USA ✓ ✓ ✓ ✓ ✓ ✓
Finland ✓ — ✓ ✓ ✓ —
Switzerland ✓ ✓ — ✓ ✓ ✓
Sweden ✓ — ✓ ✓ ✓ ✓
Singapore — ✓ — ✓ ✓ ✓
Canada — — ✓ — ✓ ✓
Iceland — — — ✓ ✓ ✓
Australia — — — — ✓ —
England — — — — ✓ —
Netherlands — — — — ✓ —
Ireland — — — — ✓ ✓
Austria — — — — ✓ —
Source: composed by the authors based on cluster analysis
Despite no significant correlation between the level of IC development and GDP for developing countries, the results of clustering are interesting both as an overall assessment of their progress in the development of national IC, and to identify specific country aspects, especially for Russia.
The most significant findings and patterns are presented below.
1. We have clustered both the total IR and its individual structural components. As a result, three clusters have formed in all cases - countries with high, medium, and low indicators of national IC or its structural elements and GDP.
2. When grouping countries according to FC-IC parameters, some countries such as Chile and Malaysia have been clustered with the best results. Since 1997, Hungary also joined this group, further steadily maintaining this position until the end of the survey period.
The bulk of countries except China, India, and the Philippines have been clustered in the group with average results. The results of most countries swing between better and worse over the study period, but they usually stay in this group. Russia occupies an average position in this cluster, lagging behind Mexico and Poland, and between 1995 and 2001, behind Argentina and Turkey.
The cluster of countries with the weakest performance in both GDP and IC included China, India and the Philippines. It should be noted that while at the beginning of the period under study China occupied an intermediate position and followed the leader of this group — the Philippines, by 2006-2007, it steadily overtook the leading position, rapidly approaching the results of the group with average GDP and IC.
3. In the FC-HC clustering between 1995 and 2002, Hungary was the unconditional and only member of the best performing group. In 2002, Poland and Malaysia also joined the cluster.
When clustering the remaining countries, we have obtained the same groups of countries as with the FC-IC clusters.
In some periods (1995, 1997, 2003-2005, 2007) Russia gets closer to the group of leaders, but still does not join it.
4. Clustering by FC-MC and FC-PC yielded the following results. The steady leaders are Malaysia, Chile, and Hungary. Constant outsiders are India, China, and the Philippines.
However, there are some particular aspects regarding the structure of the group of countries with average results.
A distinctive feature of FC-MS clustering is the fact that the list of countries has not changed relative to the list in the group with average indicators identified in the previous classifications, but the cluster is less dense (countries are more distant from each other and are «stretched» along the MS axis).
Russia was the worst performer in the group from 1995 to 1999, then somewhat improved its position from 2000 to 2007, moving ahead of Argentina and Poland.
The distinguishing feature of the change in the list of the average-performing countries in the FC-PC clustering is that since the beginning of the study period it represents a fairly dense and distinct cluster. Starting 2002, however, it begins to «stretch». The positions of South Africa and Thailand are gradually weakening, followed by Turkey and Poland. As a result, this cluster is actually divided into two sub-clusters in 2007: Argentina, Mexico, Brazil, and Russia; Poland, Turkey, Africa, and Thailand.
Here is what we can say about Russian position in this group: The country had a very weak position until 2000, then, starting 2001, there was a positive trend, and by 2007, overtaking Argentina, Mexico, and Brazil, Russia became one of the leaders of the group.
5. When clustering by FC-RC, no change was observed in the average- and low-performing groups compared to previous results. At the same time, throughout the study period, Russia has been a constant and almost the only leader of the group with the highest indicators. In 2004, Hungary joins the group, Malaysia follows suit in 2005.
Limitations and directions for further research
We believe that the following circumstances may be the limitations and debatable issues of this study:
The sample analyzed is limited to the period 1995-2007. In this regard, the issue of compliance with the identified patterns remains unresolved, considering the advances of crises in 2008 and 2015. To this end, the extrapolation of the obtained results by making regression or predictive models may become a further line of research.
An issue of the uneven distribution of data analyzed also remains unresolved. Given that the sample consists of 26 developed and 14 developing countries, it is possible that the smaller data set for the developing country group did not let us identify any patterns.
Many studies (D. North, O. Williamson, A. Auzan, V. Inozemtsev, etc.) have shown that the successful experiences of some countries do not always lead to similar results in others. In this regard, the tools and mechanisms for the development of national IC of the given group of developed countries should be considered by taking into account the need to adapt them to Russia.
Here is one example. The goal of the previously mentioned Russian development program states that «an effective system of balanced reproduction of scientific, engineering, and entrepreneurial staff and increasing their competitiveness at the world level must be formed» (Kleiner, 2011). This basically means that the concept of intellectual capital is narrowed down to the human capital of certain types of workers and contradicts the existing international meaning of this concept. Regarding other measures affecting the components of intellectual capital, the logical question is how balanced and consistent they are with each other.
Addressing such issues requires the development of an appropriate methodology which is beyond the scope of this study, but it can become a promising area for further research.
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© Anna A. Chub, Pavel Y. Makarov, 2020
Received 19.08.2020
Accepted 20.10.2020
