Introduction

The growing level of globalization of the world economy is contributing to the escalation of scientific and technological competition among countries. At the same time, countries that until recently were underperforming in the area of new technologies and innovations are beginning to become leaders (Wang and Li–Ying 2014). Depending on the specifics of social and economic, as well as scientific and technological development, countries use different strategies to gain the lead in the technological race (Elia and Santangelo 2017; Thompson and de Wet 2017; Tian, Deng, and Wu 2020). Some countries succeed in achieving dynamic growth of the scientific and technological sector of their economy, while other countries struggle to achieve optimal development indicators in this area. In this regard, there is a problem in identifying the main weaknesses in the development of a country’s national scientific and technological system, which prevents it from achieving these goals (Abdi et al. 2014; Acs, Autio, and Szerb 2014).

This article considers academic research and industrial technology development as the foundation for technological progress in the real economy and for labour productivity growth, and combines them into a unified scientific and technological sector. The purpose of the article is to develop analytical tools that make it possible to compare the technological development levels of countries, as well as to identify bottlenecks in a country’s scientific and technological sector. For this purpose, the task was set of calculating the scientific and technological balance of the selected countries over a number of years. Practical approval of this approach on the basis of statistical data will allow the main trends and specifics of scientific and technological development of the selected countries, and their successes and failures to be tracked, as well as the factors that restrain their development to be identified. This will make it possible to formulate proposals for improving the scientific and technological policy implemented by national governments of the countries under study.

Literature review

Within the scholarly literature, there are various approaches to researching the S&T sector of the economy and assessing its effectiveness. Over the last decades, the innovation system approach has won the approval of an increasing number of academic researchers. This approach is based on the notion that essential part of the innovation process is the transfer of ideas and technologies between people, business enterprises and institutions. The central idea behind the innovation system approach is that innovation and diffusion of technology is both an individual and a collective act (Edquist 2001). Its focus is on analyzing the technology transfer between the different actors of the system (Geels and Schot 2007; Gordon et al. 2017). Instead of focusing solely on the number of introduced product and process innovations, it encompasses also research and development efforts by business firms and public actors as well as the determinants of innovation such as, for instance, learning processes, incentive mechanisms or the availability of skilled labour (Balzat and Hanusch 2004).

While research on innovation ecosystems has yielded an interesting roadmap, the theoretical foundations remain under–studied; there is a lack of theoretical consistency concerning innovation ecosystem terminology (Jacobides, Cennamo, and Gawer 2018; Suominen, Seppänen, and Dedehayir 2019). The innovation system approach has been described in academic literature as insufficiently flexible and requiring considerable refinement (Caracostas 2007; Flanagan, Uyarra, and Laranja 2011; Miettinen 2002). Researchers criticized it for the lack of comparability between studies on innovation systems (Edquist 2010; Liu and White 2001) as well as for not providing practical enough guidelines for policy makers (Klein Woolthuis, Lankhuizen, and Gilsing 2005). Nevertheless, it has been adopted by many regional and national authorities as well as by international organizations interested in stimulating innovation processes (OECD 2005).

A number of different innovation system concepts have been put forward in the literature, including: national systems of innovation (Acs et al. 2017; Freeman 1987); regional innovation systems (Cooke, Heidenreich, and Braczyk 2004) and related concepts such as the concept of industrial clusters (Cantner and Graf 2004; Porter 2000); sectoral innovation systems (Breschi and Malerba 1997; Dolata 2009; Malerba 2002); technological systems (Bergek et al. 2015; Bergek, Jacobsson, and Hekkert 2007; Carlsson and Stankiewicz 1991), i.e. socio–technical systems focused on the development, diffusion and use of a particular technology.

For our research, national innovation systems (NIS) are of primary interest. This concept, first proposed by Christopher Freeman in the late 1980s (Freeman 1987) and developed in the following years (Lundvall 1992; Nelson 1992; Niosi et al. 1993; Nelson and Nelson 2002), perceived the innovation system as a historically grown subsystem of the national economy in which various organizations and institutions interact with and influence one another in carrying out innovative activity. The degree of this interaction determines the effectiveness of the entire innovation system. One of most popular frameworks for analyzing NIS among scholars is the triple or quadruple and quintuple innovation helix framework, which explore the relationship between government, university, industry, civil society and environment (Carayannis et al. 2018; Etzkowitz and Leydesdorff 2000; Miller et al. 2016). In general, the methodology of research on NIS is not uniform: in different studies, NIS can be evaluated using different indicators.

At the outset, early NIS studies did not follow a formalized structure and concentrated on one country at a time. These studies were descriptive and usually did not conduct quantitative analysis of the country’s technological development based on a large amount of statistical data. In later studies, which evaluated and compared NIS of different countries (Chang and Shih 2004), benchmarking techniques started to appear. Although these studies also were descriptive and their main focus was still on qualitative analysis of the institutional environment, quantitative indicators were actively used to assess the effectiveness of the economies being compared (for example, the percentage of gross domestic expenditure on R&D or the percentage of corporate funds in this expenditure).

In contrast to descriptive NIS models, another approach was developed in the 1990s – early 2000s, which aim to create ranking of countries by their national innovative capacity (Furman, Porter, and Stern 2002; Patel and Pavitt 1994; Porter and Stern 2002). These studies introduced a more formalized way of making inter–country comparisons based on the analysis of large numbers of indicators. This approach was further developed by breaking down the innovation process into a network with a two–stage innovation production framework (knowledge production process and knowledge commercialization process) and using a data envelopment analysis (DEA) model for measuring the innovation efficiency of the NIS (Guan and Chen 2012). In addition to various statistic indicators such as the number of registered patents or the percentage of people with higher education, some studies indicate the importance of national and cultural context of NIS (Autio et al. 2014; Nasierowski and Arcelus 1999, 2000). Acs, Autio, and Szerb (2014, 2016) introduced the concept of ‘National Systems of Entrepreneurship’ (NSE) and provided an approach for characterizing them. This approach set the focus of their research on individual agents and their be–haviour in the innovation system. To characterize NSE, scholars proposed the Global Entrepreneurship and Development Index (GEDI), which draws on the global entrepreneurship monitor (GEM) data to compile a multi–item index for profiling NSEs in different countries.

Many scholars focus their research on different specific aspects of NIS, for example on social innovations (Rao–Nicholson, Vorley, and Khan 2017), the impact of innovation policies on the performance of NIS (Samara, Georgiadis, and Bakouros 2012) or the dynamic interdependence between regional innovation activities over different periods (Chen, Kou, and Fu 2018). Innovation studies are affected by the social and ideological contexts in which scholars are embedded. For example, as awareness of environmental problems increase, environmental issues emerged as subjects of innovation systems research (Doh, Tashman, and Benischke 2019; Voegtlin and Scherer 2017).

The research is based on the work of (Balatsky and Ekimova 2019), in which a series of extended innovation and technology matrices are built over a number of years. These matrices are tables of 16 quadrants, in which the technological level of the national economy is plotted on one axis (labour productivity relative to the United States), and on the other, relative innovation activity (unit costs for research and development relative to the United States). This approach revealed an interesting pattern – the growth of labour productivity is ahead of the growth of innovation activity. In other words, a country first achieves a technological breakthrough and moves to the next quadrant in terms of the labour productivity, and then pulls the innovation activity level to a new quadrant, i.e. uses a staged strategy. The opposite strategy is characteristic only of micro–states.

In this paper we develop ideas on measuring NIS efficiency: we proposed and tested a model that links the S&T complex with the innovation system and technology commercialization, which allows us to identify efficiency of the main phases of the innovation cycle: ‘generation of knowledge – development of technology – commercialization of technology’.

Methods

The main idea of the article is that the innovation cycle is a combination of fundamentally different phases of activity: funding for researchers and their workplaces, performing research, publishing articles and registering patents, introducing matured innovations into production, etc. At the same time, the general hypothesis is that different countries have their own weak links in the innovation cycle that do not coincide with other countries. For example, in some countries the research sector’s funding is rather modest, some countries are characterized by low level of activity of researchers, some countries focus on theoretical research topics that do not yield direct practical solutions, and some countries do not have a close connection between the research sector and the real economy. If this hypothesis is correct, then completely different strategies for reorganization of the innovation sector must be developed for different countries.

The second hypothesis, directly related to the first one, states that different countries and regions of the world are characterized by their own national models of the development of the innovation system, which have their own weak links in the innovation cycle.

This article proposes an approach to building the basic balance between effectiveness of the national economy, productivity of science and commercialization of innovations. This leads to a better understanding of NIS and its impact on the national economy as a whole. To build the STB that connects the S&T sector and real production sector of the economy, two linkages are examined: ‘The national economy efficiency – the generation of knowledge and technologies’ and ‘The national economy efficiency – the commercialization of technologies’.

The national economy efficiency – the generation of knowledge and technologies

A symbiotic relationship between the national technology sector, represented by the national output, and the science sector, representing R&D, is the first principle on which our approach is based. To give quantitative expression to these concepts, we assume that labour productivity is the key integral characteristic of the technology sector. This is the indicator, which exhibits the level of technological development of the national industry both in the most informative and concise way. More generally, the level of industrial technological capacity is reflected in such indicators as energy intensity of production, materials intensity, capital intensity and environmental performance (carbon intensity). However, for our analysis it is enough to have the value of labour productivity as the most important and verifiable.

Let’s consider the validity of the labour productivity as an indicator of a country’s technological level. This indicator characterizes not only the efficiency of workforce, but also the technological equipment in workplaces. Many researchers assume that the labour productivity is an exponential function of capital investment, which in turn gives a direct concept of the workplace saturated with corporate capital. A variant of this function was used in the Aghion–Howitt model for describing the technological progress mechanism (Aghion and Howitt 1992; Aghion and Howitt 1998). The applicability and validity of the labour productivity for measuring the level of national technological development were proved in different classical and reputable studies (e.g. Grossman and Helpman 1991; Lucas 1993; Lucas 1998). More modern works mention, along with labour productivity, cumulative factor productivity which, however, does not prevent the simultaneous use of both approaches (Huang 2019; Kalai and Helali 2020; Murray 2016).

The science sector, in its turn, produces such intellectual deliverables as scientific publications and patents. The list of deliverables can be expanded, but scientific publications and patents granted represent the scale of the science sector development in the most explicit way. Hence, for simplicity, we shall consider only two deliverables when constructing the STB equation – the number of scientific publications (A) and the number of patents granted (D). We assume that science has an impact on the level of efficiency of production through the delivery of scientific publications and patents in the market. Then we can write the following balance identity:

                                                                                                               (1)

where Y_i; represents country i’s GDP ($,000, PPP; source – World Bank Open Data); L_i – total employment in country i (,000 HC, source – ILOSTAT); J_i – budgetary (government) funding of science in country i ($,000, source – UIS Statistics); G_i – domestic expenditure on R&D performed in country i ($,000, source – UIS Statistics); A_i – number of publications of country i (source – Web of Science database); D_i – number of patents (legal documents) granted to residents of country i (in the national patent office and abroad) (source – Intellectual Property Statistics, WIPO); H_i – number of researchers in science sector of country i (,000 HC, source – ILOSTAT). All sources mentioned are among the most reliable international databases and are regularly used in academic research.

If we introduce the indicators of labour productivity T = Y/L, the cost–effectiveness of science Z = Y/J (that is, the production return on government funding of science), the share of budgetary funding of science B = J/G, the share of science sector in the total employment K = H/L, the productivity of science E = A/H, the average cost of one patent P = G/D (i.e. the unit cost of each official result of intellectual activity) and the coefficient of applied effectiveness of scientific activity M = D/A, the Equation (1) can be rewritten as follows:

                                                              (2)

The left side of the Equation (2) reflects the technological level of the national economy, whereas the right–hand side shows a set of characteristics of the science sector. At the same time, this set reflects both extensive (scale) factors, i.e. the relative amount of the government funding of science (B) and number of employed (K), and intensive factors: the average productivity of science (E), the return on investment in science (Z), the effectiveness of the process of transforming scientific articles into patents (M), as well as the average patent cost (P). The M indicator reflects the intermediate stage of the scientific process, i.e. the advancement from fundamental and cognitive studies (articles) to applied developments (patents) with subsequent introduction into production.

The Equation (2) represents the STB of a given country, linking the manufacturing sector (technological complex) and the research and development (scientific) sector. The presence of STB makes it possible to consider the mutually agreed changes of the two sectors through component analysis.

Given that the balance (2) is fulfilled for all the countries in question, it is advisable to choose a leading country among them in order to use it as a benchmark in international comparisons. The USA is typically selected as a reference standard. If we use index 0 for the U.S. indicators, then the structural identity can be written as follows:

                                                                                                               (3)

In this form, it is possible to perform a factor–by–factor comparison of the technological and scientific sectors of different countries. At the same time, the left side of the Equation (3) creates an opportunity to obtain the technological rating of the countries analyzed, while the right part shows this result through the comparative characteristics of the scientific sector. Hereinafter we will refer to all indicators (3) as relative scientific and technological parameters.

The national economy efficiency– the commercialization of technologies

At the technologies commercialization phase the level of technological development of i’s national economy (T_i) is analyzed in terms of commercialization of results achieved in the science sector. For this phase the STB identity looks as follows:

                                                                                                               (4)

where Y_i– i’s country GDP; I_i - – i’s country innovation output; D_i – number of patents (legal documents) granted to residents of country i (in the national patent office and abroad); Н_i – number of researchers in science sector of country i; L_i - – total employment in country i.

Each factor of equation (4) represents an indicator which can be measured at the phase of commercialization of the results achieved in the country’s science sector.

Let us denote by W = Y/I innovation capacity of economy, U = I/D – commercial performance of one patent, С = D/H – patent productivity of one researcher, K = H/L – science sector scope (number of researchers in percentage of the total employment). Then STB identity for i’s country looks as follows:

                                                                                                       (5)

As at the national economy efficiency – the generation of knowledge and technology phase, it seems reasonable to perform inter–country comparisons based on Equation (6). The indicators of the technological leader (the USA) are denoted with zero–indices:

                                                                                                               (6)

The identification of national models of S&T development and their ‘bottlenecks’

At the national economy efficiency – the generation of knowledge and technologies phase the component analysis (3) enables identification of science sector bottlenecks by applying a simple criterion:

                                                                                                               (7)

It should be noted that in criterion (7), the index for the patent cost appears in an inverted form. This is due to the fact that the higher competitiveness of the scientific sector of a given country implies that it has a larger scientific sector with higher efficiency and a lower cost of the generated scientific product. Thus, the identification of the worst parameters of a country in comparison with the leader (the USA) makes it possible not only to define the weaker zones of scientific and technological development, but also to use the Equation (3) to evaluate the potential effect of the increase of the corresponding parameters.

At the national economy efficiency – the commercialization of technologies phase the bottlenecks of the technology sector can be identified with the following criterion:

                                                                                                               (8)

In criterion (8) the indicator of innovation capacity of the economy appears in an inverted form and characterizes the share of innovation output in a country’s GDP.

A similar approach can be used in studying certain types of models of S&T development. For example, if one of the parameters on the right–hand side of the breakdown (3) or (6) in several countries is particularly different from the values of the other countries, then we can talk about the existence of a specific national strategy that emphasizes the corresponding aspect of process. It is desirable that the parameter in question is sufficiently significant for the final result. The strategic significance of a parameter can be determined depending on its volatility for various countries: the greater the dispersion, the more meaningful the differences in the respective policies.

Obviously, analysis of all possible models of technological development and their detailed description is beyond the scope of this study. However, this is more important to determine the most impressive and interesting strategies applicable to other countries.

Empirical findings

To assess the indicators set out in STB the international databases were used. To assess the dynamics of changes the years 2005 and 2015 were taken as the measuring points. The calculations for 17 countries were made on the basis of data for 2005 and 2015. The countries analyzed were assigned technology ranks based on the value of the labour productivity index (T = Y/L).

Based on comparative analysis it is possible to draw conclusions about the main features of the NIS development, as well as to compare the indicators of the selected countries with the indicators of the reference country (USA) that allows for factor comparison of their S&T sphere. Table 1 presents the indicators of the potential and performance of the S&T complexes, calculated in accordance with formula (3) according to data for 2005 and 2015.

Table 1: The relative parameters of the science and technology complexes of analyzed countries, 2005 (in brackets – 2015).

Source: Authors’ calculations using data from Federal State Statistics Service (n.d.), World Bank Group (n.d.), Clarivate (n.d.), World Intellectual Property Organization (n.d.), Eurostat (n.d.), UNESCO Institute for Statistics (n.d.).;* In order to determine scientific sector bottlenecks we used the reverse indicator of the cost of a patent;**In the U.S. and Israel, the national statistical agencies do not keep records of the number of researchers in humans. In order to obtain estimates for these two countries we used the average ratio of the number of researchers in humans to the number of researchers in the rates for the other countries; *** during polarization ratio calculation we excluded the extremely low value of the number of patents obtained in Tunisia (1 patent).

The countries were selected according to two factors. First, the availability of all the statistical data required for calculations, as well as their relative reliability. Second, for the sake of completeness, the sample must include countries that represent at least three types: developed, developing and post–socialist states. Our analysis will rest on three countries of the former socialist bloc, which today show different progress in building market economies and NIS: Russia, Poland and China.

Comparison of data on indicators for 2005 and 2015 leads to the conclusion that in 10 years the labour productivity in China increased by 11 percentage points relative to the same indicator in the U.S. In Russia during the same period labour productivity increased by 14 percentage points relative to the U.S. The technological level of the Polish economy increased by 12 percentage points compared to the USA. Thus, all three countries, having large differences in the national economies, made nearly the same leap in the increase of their technological level. The past decade has shown that technological differences between many countries of the world are rapidly declining. For example, Poland is already very close to South Korea, Russia – to Latvia, and China – to Brazil, whereas 10 years ago (in 2005, see Table 1) the gap in the efficiency of these economies was not just impressive, but also seemed almost insurmountable.

A comparative analysis of the selected countries for 2005 and 2015 in terms of potential and performance of S&T complex relative to the US allow us to understand the sources of this leap. For this purpose, we consider the polarization factor of the analyzed indicators, for example, for T_i:

                                                           (9)

Similarly, the polarization factor can be calculated for all indicators characterizing development of the scientific sector. Let us introduce the following notation: k_j is the polarization factor, j = {T,Z,B,P,M,E,K}.

Analysis of the polarization factor values for 2005 and 2015 (Tables 1 and 2) shows that the spread in the labour productivity values decreased significantly (from 13.83 to 7.66), indicating the development of catching–up trends in selected countries. The gap in terms of the indicator ‘Science sector’s share in total employment’ also significantly reduced (from 92.32 to 67.15). At the same time the effectiveness of the process of transforming scientific articles into patents showed record high values of polarization – in 2005 and 2015 its values were exponentially higher than for the labour productivity (13.83 against 78.53 in 2005 and 7.66 against 206.23 in 2015 respectively). Also, a significant polarization is observed for the average cost of a patent, but with a trend toward reduction (from 76.00 to 57.22). The reason for such a strong polarization of the indicator M is a slight increase in the number of patents obtained in the face of explosive growth in the number of publications in such countries as Morocco and Brazil. In addition, in Iran the rapid decline in the number of patents obtained (from 2608 to 31) is associated with a failure to receive submission data from the national patent office in 2015. The indicator ‘Science sector’s share in total employment’ remains one of the leading in the magnitude of polarization. The main contribution to its polarization made by both small and large countries in the sample (Israel with a high share of R&D sector in total employment; India, China, Mexico, Brazil with a relatively low number of researchers, taking into account the scale of economy).

Generally, the main difference between the national technological models consists of the ability of national economies to transform theoretical research in the form of articles into practical development in the form of patents, which determines the technological disparities between different states.

The example of the considered factor illustrates well the specifics of national development models. Thus, in 2015 in France the effectiveness of the process of transforming scientific articles into patents was 6% lower than in the United States, and in Germany – 28% higher, though back in 2005 Germany was ahead of the United States by 78%. One meaningful interpretation of this fact is that French scientists are more prone to basic research in comparison with their German colleagues, who are focused on the end result in the form of manufacturing innovation. In 2005, the cost of a patent in Germany was less than half that in the USA and 1.5 times lower in comparison with France, in 2015 – nearly 1.5 times less than in the USA and almost 1.1 times lower compared to France. Apparently, these differences are determined by the degree of scientific pragmatism of different nations and peoples. In any case the example of France and Germany corresponds well with existing ideas about the mentality of the inhabitants of these countries.

A closer examination of the transformation ratio allows us to detect the East Asian model of technological development, which is to achieve extremely high efficiency of coherence between the efficiency of the economy and the scientific sector. This type of model is observed in Japan, South Korea and from 2015 – in China. The success of this group of countries is determined by a relatively low publication activity of researchers in combination with high effectiveness of the process of transforming scientific articles into patents and a record low average cost of a patent compared to the USA and other countries. The main drawback of China’s scientific and technological system is its extremely small size in terms of the workforce involved in it. It is this factor that remains the most important potential of China, which may allow it to implement major progress in productivity growth in future decades. Experimental calculations show that providing the scale of employment in the scientific sector reaches the level of the USA, China will be able to increase labour productivity up to 112% relative to the US level; if it is able to increase the scientific sector to the level of South Korea, the relative labour productivity will be 140%. Thus, at the moment China has not yet used the opportunity of the extensive growth of the technological level of the economy, while continuing to prepare for this stage through intensive development of the university education system. From this point of view, China is in a rather favourable condition, having the opportunity to use almost unlimited human potential in the future. It is worth noting that the scale of the scientific sector is the second most volatile factor and it involves very large changes.

Given that publication activity reflects in some degree the productivity of basic science and the effectiveness of the process of transforming scientific articles into patents – applied science, we can say that in Asian countries basic (cognitive) research is relatively weak compared to the leading application–oriented development of inventions. This strategy is extremely effective for catching–up economies, but it is unacceptable for countries that have already joined the ranks of technological leaders. In this sense neither Japan nor China and South Korea can claim the role of scientific leaders. For this to happen, their publication activity will have to increase greatly.

The East Asian model of development is opposed to the African model, which is characterized by the extremely small effectiveness of the process of transforming scientific articles into patents. Thus, Tunisia and Morocco are developing according to a common scheme that doesn’t involve active research with output in the form of patents. Poland with its weak patent generation is close in some degree to this model too. It is here that the country has large potential for growth: providing the effectiveness of the process of transforming scientific articles into patents reaches the U.S. level, Poland could achieve a record high level of relative labour productivity – 299%. Of course, this is only a hypothetical possibility, since in reality in 10 years the country has managed to increase this indicator by only 10% relative to the U.S., indicating a very low rate of closing the gap with the leader. Currently, Poland compensates its systemic flaws through massive government funding of science. The same strategy but in an even more explicit form is typical for Russia, which also focuses on government funding of science. In this respect, China maintains an economical strategy for science funding – the government’s share in expenditure on science in China is only 21% to 42% in Poland and 69% in Russia in 2015.

The conducted analysis allows us to draw the general conclusion that the weak link in the NIS of China is the scientific sector scale, in Poland – the effectiveness of the process of transforming scientific articles into patents, and in Russia – publication activity. Ironically, Russia is 2.5 times behind Poland in terms of the number of published articles per researcher. It seems probable that Russia’s lag is linked with the weak orientation of the majority of researchers on international scientific journals as well as low activity on promotion of national journals in the international scientific space. In fact, internationalization of Russian science is carried out by force – via the respective requirements and constant pressure from the regulator represented by the Ministry of education and science of the Russian Federation. Most likely, Russia will need at least another 7–10 years to substantially increase the scientific productivity of its researchers.

Special mention should go to the model of small countries, which forms a sufficiently large segment of science. Development of Singapore, Israel, South Korea and Japan goes in line with this model. The small–sized economies and well–managed economies without rawmaterials factors of growth tend to have disproportionately large R&D sector, which allows them to maintain quite a high efficiency, and through this to ensure their presence in world markets. For example, the relative size of the scientific sector in Israel is 67 times larger than in India. Thus, here we face a typical case when the initial economic conditions of the country determine its S&T development model.

Some countries are able to develop non–typical economic benefits, such as the cost of a patent. For example, the relatively low cost of a patent is seen in Japan and South Korea, which is a direct consequence of active application–oriented development generating a stream of patents. On the contrary, patents are very expensive in Tunisia, Morocco and Brazil, which lack the resources to establish sustainable generation of a large–scale flow of patent applications.

The next step is to draw up the countries’ STB in terms of commercialization of results generated in the science sector.

Due to the lack of data on innovation output, the 11 countries for which this information was available (Table 3) were used in analysis of results generated in the science sector. It is important to note that the US methodology for measuring innovation output slightly differs from that applied in the Frascati Family of manuals. That’s why output of these goods and services in real terms may be a little higher than the figures reported in U.S. statistics.

The following tables present indicators, which characterize STB at the national economy efficiency – the commercialization of technologies phase. Table 2 provides data for 2005 and Table 3 – for 2015. The United States is used as a benchmark country. The other countries are ranked in descending order of labour productivity.

The comparison of technology commercialization indicators has yielded unexpected results.

Table 2: S&T Indicators for the phase ‘The Effectiveness of The National Economy – The Commercialization of Technologies’, selected countries (2005).

Source: Authors’ calculations using data from Federal State Statistics Service (n.d.), World Bank Group (n.d.), Clarivate (n.d.), World Intellectual Property Organization (n.d.), Eurostat (n.d.), UNESCO Institute for Statistics (n.d.).

Table 3: S&T Indicators for the phase ‘The Effectiveness of The National Economy – The Commercialization of Technologies’, selected countries (2015).

Source: Authors’ calculations using data from Federal State Statistics Service (n.d.), World Bank Group (n.d.), Clarivate (n.d.), World Intellectual Property Organization (n.d.), Eurostat (n.d.), UNESCO Institute for Statistics (n.d.).

First, the United States is not a leader in innovative production as a share of GDP, falling behind Russia, Poland and Mexico, which fail to catch up with the US in terms of labour productivity. This is obviously due to the fact that manufacturing of innovative products has been transferred from the United States to third countries while services and other con–commodity sectors represent a significant share of GDP.

Second, Mexico is surprisingly far ahead of all other countries in terms of revenue per patent. This is possible because innovative products manufactured in Mexico are not so much the result of the introduction of technologies developed by national science, but rather the result of foreign (e.g. the US or other developed countries) investments in high–tech Mexican companies. This effect is also true for Poland, Czech Republic and Estonia, which rank high in this component among the countries in the sample. The validity of this statement is demonstrated by the above–mentioned problems in technology development faced by Poland at the national economy efficiency – the commercialization of technologies phase and by the lowest values of revenues per patent among the selected countries: 13% of the U.S. level in Estonia, 17% in Czech Republic and Mexico, 23% in Poland (Table 2 and Table 3).

The conclusion that the level of innovation capacity of the national economy does not correlate with the level of labour productivity implies that there are limitations to the applied STB approach namely at the national economy efficiency – the commercialization of technologies phase. The main methodological disadvantage of the proposed tool is that STB is effective for relatively closed economic and innovation systems when the technologies, investments in the commercialization of technologies and other investments into the creation of innovative productions have the same national origin.

Germany has the most balanced indicators of all the countries in the sample. In 2005 and 2015 it scored the highest ranking of the selected countries in terms of a number of patents per researcher and in 2015 it was well far ahead of the United States in terms of commercial revenue per patent and in innovation capacity of national economy.

To assess the dynamism of the development of the scientific and technological complex of the countries analyzed, we take into consideration the coefficient of polarization of the values of the indicators, calculated from the data for 2005 and 2015 (Table 2 and Table 3).

The analysis of polarization coefficients kj, where j = {W, U, C, K}, shows that over the decade, the gap between the countries decreased to varying degrees, but this took place practically in terms of almost all indicators, except for the indicator ‘Share of the scientific sector in the total employment’, which indicates the same trends of catching up. Both in 2005 and in 2015 the revenue per patent exhibits the highest value of the polarization coefficient. The polarization coefficient of the ‘Patents performance per researcher’ has experienced the greatest decrease: k_c has decreased more than fourfold over the decade. Attention is drawn to the fact that the polarization of the countries in question in terms of the innovative level in the economies remains very stable. Among other things, this indicates the absence of any technological competition in the USA – Europe – Russia triangle in the past decade.

A limitation of this study is the lack of statistical data relating to innovative outputs in China. Therefore, a comparative analysis of technology commercialization indicators was conducted for Russia and Poland. During the years 2000–2015 the level of innovation capacity of the national economy against the US level increased in both countries: by 6 percentage points in Russia and by 16 percentage points. in Poland.

In terms of the indicator ‘Commercial performance of one patent’ the countries in the comparison have demonstrated trends in the same direction: in Russia it increased by 135 percentage points compared to the USA, in Poland – by 42 percentage points. In Russia the patent productivity of researchers decreased from 62 to 54%, while Poland reported an increase from 14 to 23%. Such shifts are due to a relatively small number of patents in Russia and Poland, as well as their weak connection with the generated innovative products, which are made on the basis of investments unrelated to R&D It should be noted that in both countries ‘Patent performance of researchers’ is a bottleneck. The share of the scientific sector in the total employment of the country (K_i / K₀) decreased both in Russia (by 14 percentage points from the US level) and in Poland (by 10 percentage points from the US level). These facts allow us to speak about some genetic relationship of the NIS of Poland and Russia.

Next, let us consider the specific financial indicators that characterize the effectiveness of the scientific and technological sectors of the NIS of the three countries selected as reference objects for the analysis (Russia, Poland and China) as well as the benchmark indicators of the USA. Below, the calculated values of the following indicators relative to the US level are presented for the selected countries:

— the cost of a scientific publication in journals indexed by the Web of Science analytical system: the ratio of R&D budget financing (J) to the number of the publications(A);

— the cost of one patent: the ratio of domestic costs for R&D (G) to the total number of patents granted per year (D);

— the cost of one researcher: the ratio of domestic costs for R&D (G) to the total number of researchers (H).

The data (Table 4) show the presence of structural distortions in the S&T policy. The most difficult situation in the sample is observed in Russia, where, against the background of the researchers’ extremely low financial security, the cost of publishing an article is extremely high. Such values of the indicators make it necessary to double the funding of science in order to achieve the proportionate increase in its efficiency in terms of published articles, or to reduce the number of unproductive researchers and thus to improve the performance of the remaining ones. In terms of the indicators under consideration, Poland’s position looks slightly better than Russia’s due to the relatively large flow of articles and their low unit cost. However, even the lower financial security of researchers and exaggerated cost of patents also make it necessary for Poland to take radical measures in terms of scientific and technical policy aimed at increasing R&D investment and improvement of the applied science performance.

Table 4: Financial indicators of S&T sectors of NIS (2015).

Source: Authors’ calculations using data from World Bank Group (n.d.), Clarivate (n.d.), World Intellectual Property Organization (n.d.), Eurostat (n.d.).

In terms of the specific indicators of the cost of an article and the financing of the human resources in science, China has approached the level of the United States, and a relative low cost of patents gives the country a competitive advantage, which was mentioned above. Over the last years China has definitely been concentrating on patents, which are quicker and cheaper to obtain. The number of patents for products with a relatively short lifecycle, such as electronics or communication devices, has increased due the efforts in creating a stimulus. The growth in patents partly explains such a low cost of one patent.

The proposed tools for dividing the innovation and technological cycle into separate phases provides the state regulator with information that makes it possible to more efficiently identify the problem areas of the national innovation system. Each country has its own unique history, advantages and disadvantages that cannot be ignored.

The analysis showed the unique advantages and disadvantages of the national S&T systems of the countries analyzed. Its results allow us to suggest different management strategies of S&T sector for these countries. For example, China should increase its scientific sector at an accelerated pace. In part, this is being done through the development of the university system, construction of university towns and the expansion of training programmes. However, China needs to intensify this process to become a truly economically developed country. This is already being done in some degree through the development of the university system and the enhancement of training programmes for qualified personnel, all the way to building of university towns and the launching of modern scientific laboratories. Over the long term, this will allow the creation of a national research sector corresponding to the scale of the national economy.

Poland, just like Morocco and Tunisia, needs to improve productivity of its applied science, the output of which will be increased flow of patents. This task requires the country to increase the scale of the scientific sector significantly while monitoring its effectiveness.

Russia needs to increase publication output of researchers and improve efficiency of applied science. Most probably, such underperformance of Russia is related to the weak orientation of the majority of researchers towards international scientific publications, as well as insufficient activity in representing national academic periodicals in the international scientific space. The Russian authorities seem to be aware of this problem: scientists in Russia are under unprecedented government pressure to increase the number and quality of their scientific publications. Apparently, to reduce the gap in these parameters, intensification of labour is not enough. Russia needs to revise the internal organizational structure of its scientific institutions.

Overall, the original idea of the article in the review of NIS was fully justified. In particular, the general hypothesis that different countries are characterized by their own weak links of NIS, which do not coincide with other countries, was fully confirmed. The second hypothesis regarding the existence in certain regions of the world of specific NIS models corresponding to the stage of development of states in the corresponding regions was less pronounced.

Conclusions

The analysis undertaken shows that the innovational and technological cycle should be broken down into different phases related to the creation and distribution of innovations. Consideration of all phases of the innovation cycle against the background of a certain ‘reference’ of the most technologically successful country, makes it possible to identify the weak links in this cycle. The information about weaknesses of the national innovation system allows the regulator to develop measures to eliminate problems in a timely and target–oriented manner, which is the key to effective public administration.

The proposed method of calculating scientific and technological balance makes it possible to identify weak areas of the national innovation system, but it does not automatically provide institutional markers to be achieved. This restriction is related not only to the unique culture of each country, but also to the country’s development stage at the time. For technologically lagging countries, the problems arise at the beginning of the innovation cycle; for dynamically developing economies, ‘failures’ occur in the middle of the cycle; for the leading countries, problems arise mainly at the end of the cycle. The regulator should pay the greatest attention to situations when the weak innovation link does not correspond to the technological development level of the country.

It should be noted that the use of the STB approach for the Efficiency of the economy – commercialization of technologies phase has its limitations. This is largely due to the ambiguity of the concept of innovative products – products/processes, which are recognized as innovations in certain countries, may be not reported as innovations in the others. Inter–country technology transfer complicates the analysis of this phase of the production cycle even further. In this context, the analysis of innovation processes should involve taking into account the national specifics or even transition from national to the sectoral level of system. In future improvement of statistics on innovations will lead to expanding the STB approach.

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Official link to the paper:

Balatsky E., Ushakova S., Malahov V., Yurevich M. Phases of STI Cycle: Countries Comparative Analysis // «African Journal of Science, Technology, Innovation and Development», 2025, Vol. 17, No. 3, pp. 435–446.