Innovative activity of the enterprises in Kazakhstan: economic and statistical analysis

Abstract

Object: the purpose of the presented scientific article is to conduct an economic and statistical analysis of the innovative activity of the enterprises in Kazakhstan; the object is the enterprises of Kazakhstan, characterized by innovative activity, introducing and using in their activities the results of intellectual labor, represented by new technologies, technical objects, patents for inventions, utility models, industrial designs, etc.

Methods: to achieve the goal of the research, general scientific methods were widely used, in particular, the method of analysis, which made it possible to determine the entire set of parameters that characterize the innovative activity of enterprises in Kazakhstan; a generalization method aimed at establishing existing relationships between the considered economic objects and phenomena; method of graphic interpretation, which made it possible to visualize the results obtained; economic and mathematical methods, represented by correlation and regression analysis, methods of checking the constructed model based on the Student's, Fisher's test, the coefficient of determination and the Darbin-Watson test; forecasting methods based on the constructed multiple regression.

Results: within the framework of the research, the authors built a multiple regression model for the innovation activity of the enterprises in Kazakhstan, the adequacy and correctness of which was verified using the Student's t-test, Fisher's test, the coefficient of determination and the Darbin-Watson test. At the same time, using the Gauss method, the normal system of equations was solved, which made it possible to obtain a standardized regression equation, taking into account the calculated coefficients. It is also important to note that the article provides a comprehensive analysis of the development of the theory of innovation and key approaches to the interpretation of the concept of “innovative activity of an enterprise” based on the research of various scientific materials, including those widely represented in scientometric databases - Scopus, Clarivate Analytics, Google Scholar and RSCI.

Conclusions: using of correlation-regression analysis in terms of constructing multiple regression models is the optimal method that allows not only to effectively and comprehensively describe the innovative activities of the enterprises in Kazakhstan, but also to use it for the subsequent forecasting of the analyzed base indicator (dependent variable). The most important aspect remains the choice of parameters, which should be built on a deep and comprehensive understanding of the economic phenomenon under consideration, while their selection should be based on their strict economic interpretation, in particular by constructing matrices of pair correlation coefficients and taking into account multicollinearity.

Introduction

In the context of modern economic development, the transition of industrial enterprises to a qualitatively new level is identified as the main determinant - an innovative update associated with the process of innovative production, the advantage of which is the effective using of material, technical, production, intellectual and organizational and managerial resources that make up the innovative potential (Krawczyk- Sokolowska et al., 2019), the growth of which determines the further development of an industrial enterprise,

*Corresponding author.

E-mail address: larisatash_88@mail.ru its competitive position in the markets, as well as the production of products with high added value (Vinogradova et al., 2017).

Without the process of creating and introducing innovations that define the essence of innovation, it is impossible to resist the forces that change market conditions and activate the forces of competition.

Thus, in the context of modern competition, shortening the life cycle of goods and services, the development of new diverse technologies, the merger of enterprises into clusters, the transition of countries to the fourth industrial revolution, one of the main conditions for the formation of a competitive strategic perspective of any enterprise is increasingly becoming its innovative activity.

In the scientific literature, the concept of innovation is given special attention, a large number of scientific studies, including in the international databases Scopus, Clarivate Analytics, Google Scholar, RSCI, are devoted to the topic of managing the innovative and digital potential of an enterprise, in particular those operating in the field of industrial production.

At the same time, it should be noted that there is no generally accepted definition of the concepts of “innovative activity” and “innovative potential” as an economic category. The analysis showed that the structure of innovation potential, which is the result of innovation, has not been fully investigated. Now, there are several options for the structure of innovation potential, often contradicting each other. A similar scientific problem is also typical for determining the key components of the components of innovation for its comprehensive analysis, assessment and subsequent forecasting, including through the using of multiple regression models.

Literature Review

Innovation activity is one of the most important factors in the development of the modern world. The ability to produce and perceive all kinds of innovations in our dynamic time determines the fate of individual subjects, organizations, peoples and societies. Innovations as a tool of transformation and a form of management of production development have become the object of independent study in all industrialized countries. A whole field of science has emerged - innovation science, which solves the problems of the formation of innovations, their spread, studies the reasons for resistance to innovations, etc. In the center of innovation is the process of change, i.e. transition and transfer of the system under consideration from one state to another.

As noted above, the issues related to the research of the creation, commercialization of innovations and the characteristics of innovative activities of enterprises are relevant, but, at the same time, not fully studied from the standpoint of determining the key components of the studied economic phenomenon.

In order to understand the specifics and essence of innovation, it is necessary to turn to the concept of “innovation”.

In modern scientific literature, many definitions are given to the concept of “innovation”, various options for classifications are proposed, built on various classification features.

It is generally accepted in the scientific community that the conceptual apparatus of innovations was mainly developed abroad by such scientists as I. Perlaki, J. Schumpeter and many others (Perlaki, 1985; Schumpeter, 1982; Schumpeter et al., 2002). They consider “innovation” in terms of the object and subject of the research being conducted.

Such prominent figures as J. Mansfield, J. Rogers, R. Foster, B. Twiss, F. Nixon also played an important role in the development of the theory of innovation. For example, Nixon defines “innovation” as a set of activities, the result of which leads to the emergence of new / improved business processes or equipment (Nikson, 1990). Also interesting is the point of view of B. Twiss, who interprets “innovation” as a process in which the idea of creating an invention or some kind of innovation acquires economic meaning, potential economic efficiency (Twiss, 1992; Twiss, 1993).

A number of American and European scientists consider innovation as inventive activity, during which there is an intersection of two previously unrelated systems - the individual and innovation. This approach is undoubtedly interesting, but it is limited by the absence of a subsequent implementation initiative, that is, the very idea of commercialization.

An important point is also that many scientists, both foreign and domestic, identify the concepts of “innovation”, “novelty” and “novation”, defining the concept of “innovation” as an innovation used in the production or management of an organization, like an economic unit.

In general, the development of the theory of innovation in the historical and scientific context can be represented in the form of the Figure 1.

One of the most important aspects of innovation is the diffusion of innovations, which is understood as the process of diffusion of innovations in the business cycles of scientific and technical, production and organizational and economic activities.

Diffuse processes are very important, since they contribute to the inflow and outflow of capital, an increase in the number of producers and consumers, and a change in their quality characteristics.

J. Rogers believed that diffusion is a process in which innovations are transmitted through certain channels over a certain period of time among members of a social system (Rogers, 2003).

Innovation diffusion theories are diverse and span multiple disciplines.

In 1952, the Swedish geographer T. Hägerstrand examined the process of diffusion of socio-economic phenomena in rural areas, in particular, the spread of agricultural technology, and carried out its modeling using the Monte-Carlo method (Hägerstrand, 1952).

Diffusion of innovations is a space-time process. L. Suarez-Villa (Suarez-Villa, 2002) presented the conceptual framework of the process in the broadest view. Its essence lies in the fact that within the framework of macroeconomic and regional development associated with the change in the leading industries during the N.D. Kondratieff's “long waves”, the emergence of innovation centers and the rate of their diffusion in the economic space play a crucial role (Kondratieff, 1984).

Thus, based on the analysis carried out, it can be concluded that innovation is the result of inventive activity, originating from a novation that is the result of scientific research; at the same time, pronounced features of commercialization characterize novation, in contrast to innovation, with subsequent economic efficiency.

In turn, the innovative activity of an enterprise can be understood as actions aimed at generating and activating an intellectual component, with the aim of creating, using and subsequently commercializing the results of intellectual work, represented by inventions, industrial designs and utility models.

The issues of innovation activity have been repeatedly considered in the works of scientists around the world, in particular, for example, from the standpoint of the effective development of innovation in small and medium-sized enterprises (Harel et al., 2019); as part of the study of issues related to the peculiarities of the using of technology by SMEs (Bagheri et al., 2019; Brigic et al., 2019; Caldas et al., 2019), marketing and product innovations (Quaye et al., 2019; Ramadani et al. ., 2019). A special role in publications is assigned to issues of support for innovation activities both from the state and within the framework of the publicprivate partnership (Goraczkowska et al., 2019; Hutahayan et al., 2019; Lopes et al., 2019; Yu et al., 2020). In addition, the issues of the innovation component are also studied within the framework of classical economic science (Jakimowicz et al., 2019), as well as taking into account the definition of the role of business incubators and other participants in the analyzed economic phenomenon in building effective regional and national innovation systems (Baskaran et al. , 2019; Perez-De-Lema et al., 2019; Siqueira et al., 2019; Liu et al., 2015).

It should be noted the special role of the universities in the formation of an effective innovative regional system, which is an integral component in the creation (Bellucci et al., 2016; Brochner et al., 2016; Cui et al., 2016) and the subsequent commercialization of innovative products (Dehghani et al., 2015; Kesting et al., 2016; Mamrayeva et al., 2012).

It should also be noted that a number of scientists consider innovation activity through the prism of sustainable entrepreneurship development and the role of R&D (Soltanzadeh et al., 2019; Soltysik et al., 2019; Mamrayeva et al., 2018).

In the context of the Fourth Industrial Revolution, the transition of a number of economies to a digital basis, a special role is assigned to the using of various digital tools in the structure of enterprises' innovation activities (Krykavskyy et al., 2019; Mahmood et al., 2020). In particular, a number of authors pay attention to the specifics of the using of blockchain, big data, cyber-physical systems and new-type laboratories in the process of building digital potential by companies, industrial enterprises and integrated structures (Galvin et al., 2020).

Despite of the significant contribution of these scientists, the research of the problem posed cannot be considered exhaustive. The works of the listed authors have created theoretical and methodological foundations for the formation of a system of innovative activities of enterprises and the innovative infrastructure of regions, various approaches and aspects of building a mechanism for the diffusion of innovations into production and consumption are proposed. However, the accumulated experience needs to be rethought and the development of such combinations that would correspond to the ongoing changes in the economy, the emergence and strengthening of an import substitution strategy aimed at enhancing domestic innovative developments and their introduction into the country's economy, taking into account the characteristics of the potential of the regions, their specific capabilities.

Methods

To achieve the goal set in the article and related to the economic and statistical analysis of the innovative activities of enterprises in Kazakhstan, the following methods were used:

1. general scientific methods, including:

 an analysis method that allows us to determine a complex set of indicators characterizing the innovative activity of enterprises in Kazakhstan (15), as well as to highlight the assumed dependent variable, which the selected parameters can influence (in our case - “the volume of innovative products (goods, services), billion tenge”);

 a generalization method aimed at establishing the existing relationships between the analyzed economic objects and phenomena, as well as allowing to determine anomalous points within the considered dependent variable;

 the method of structuring and content analysis will allow organizing all the information received related to the assessment of innovative activities of enterprises;

 the method of functional and structural research of objects will make it possible to build all possible options for the implementation of the goal and tasks related to the economic and statistical analysis of the scientific problem under consideration;

 a method of graphic interpretation, which contributes to the visual presentation of the results obtained using the MS Office application package and the Corel Draw graphic editor in terms of creating a picture reflecting the key stages of the development of the theory of innovation in the structure of scientific knowledge;

2. economic-mathematical and economic-statistical methods presented by:

 Irwin criteria, which allows checking the presence of anomalous points in the considered trend (in our case - according to the dependent y variable - “the volume of innovative products (goods, services), billion tenge”);

 the method of constructing multiple regression, including on the basis of a detailed analysis of the choice of regression coefficients based on the construction of a matrix of paired correlation coefficients, their assessment, studying the presence of multicollinearity between the values under consideration;

 methods of checking the constructed model based on:

• Student's t-test, taking into account the confidence level = 0,05;

• Fisher Criterion, understanding the peculiarity and number of factors of the constructed model, the number of observations included in the analysis at α = 0,05, and also taking into account that Ffact> Ftable, which will indicate the significance of the constructed regression equation;

• Coefficient of determination: at the same time, taking into account that to assess the adequacy of the model, R must be more than 85%;

• Durbin-Watson Statistic, taking into account the range fact 1,5 <DW <2,5, calculated on the basis of the obtained residuals;

 forecasting methods based on the constructed multiple regression;

 Gaussian elimination, which allows solving a system of equations and building a standardized form of regression equations.

It is also important to note that the sequence and stages of the research presented in this scientific article comply with the standards, algorithms generally accepted in scientific circulation and reflected, including in scientific and periodical literature.

The reliability of the data is ensured by the reliability of the calculations and measurements carried out, as well as the nature of their subsequent interpretation; the reproducibility of the data is due to the verification of the results obtained in MS Excel.

Results

The innovative activity of enterprises is a complex economic phenomenon, since today, as noted earlier in the article, there is still no consensus among scientists regarding the set of indicators that can be included in the analysis and used for subsequent research.

The presented author's approach is based, first of all, on the experience of studying the issues of innovation, certain aspects of the features of the commercialization of the results of intellectual work, as well as on conducting many years of research in the field of studying the features of the functioning of industrial enterprises and integrated structures, including those represented by innovative-active industrial clusters, constituting their innovative and digital potential, the study of which, of course, in the context of building a new type of economy, is especially relevant and significant.

Considering all of the above, the following indicators were selected for analysis:

1) Dependent variable:

– the volume of innovative products (goods, services), billion tenge;

2) Independent variables:

x1 – number of enterprises with innovations, units;

x2 – number of innovative-active enterprises, units;

x3 – the level of activity of enterprises in the field of innovation, in percent;

x4 – costs for innovations, billion tenge;

x5 – the number of organizations that have created and using new technologies and equipment, units;

x6 – the number of created and used new technologies and equipment, units;

x7 – internal costs for research and development work (R&D), billion tenge;

x8 – number of organizations that carried out R&D, units;

x9 – number of employees who performed R&D, thousand people;

x10 – average monthly nominal wages of employees by type of economic activity “Research and Development”, thousand tenge;

x11 – investments in fixed assets by type of economic activity “Research and Development”, billion tenge;

x12 – granted patents for inventions, units;

x13 – granted patents for utility models, units;

x14 – granted patents for utility models, units;

x15 – granted patents for breeding achievements, units.

All initial data are presented in table 1.

ECONOMY Series. № 4(100)/2020 147

Innovative activity of the enterprises...

Graphical interpretation of thdynamics of factor Y - “the volume of innovative products (goods, services), billion tenge” to conduct a visual analysis that allows us to determine the presence or absence of abnormal points in the structure of the series under consideration (Figure 2).

Visual analysis of the trend made it possible to assume that there are no abnormal points, but to confirm the hypothesis put forward, it is advisable to use the Irwin criteria. For n = 12, the threshold value of the Irwin criteria should not exceed λcr = 1,3.

In order to calculate the value of the Irwin criteria for the indicators under consideration, it is necessary to determine:

1. mean value (calculated in table 2);

2. unbiased estimate of variance (D (y) - formula 1);

3. standard deviation based on unbiased variance estimate - formula 2;

4. directly the value of Irwin criteria (using formula 3).

According to the data obtained, it can be concluded that no anomalous values are observed.

To construct a matrix of pairecorrelation coefficients and a matrix of interfactor correlations, the Microsoft Office software package waused (Table 3)

Table 3 shows that the analyzed Y – “the volume of innovative products (goods, services), billion tenge” is really influenced by factors, 11 of which (X1-X7, X9-X10, X12-X13) significantly exceed 50%. At the same time, it should be noted that at the initial stage of building the model, the author decided to include factors in the model, according to which the correlation coefficient, according to the Chaddock scale, is not lower than 0,5 (which corresponds, at least, to the presence of a noticeable connection strength).

The table also shows that there is multicollinearity between the above factors, as a result of which only 4 factors were selected for building the model: X3, X10 and X12-X13 (further in the analysis, these indicators will be designated as X1, X2, X3 and X4, respectively).

Using the add-in “Data Analysis”, we will carry out calculations to estimate the indicators of the future regression model (Tables 4-6).

Table 7. Calculation of the Durbin-Watson Statistics

Observation

Predicted value

Residuals

Calculated values

1

66,71227807

15,88772193

-

-

2

137,5694792

4,630520816

21,44172303

126,724577

3

302,7059158

-66,80591583

4463,03039

5103,164481

4

428,6799705

-49,67997055

2468,099474

293,2980018

5

451,9106767

126,3893233

15974,26104

31000,39623

6

447,6976484

132,7023516

17609,91411

39,85432626

7

438,0422429

-60,84224293

3701,778524

37459,51006

8

734,8602652

-289,0602652

83555,83689

52083,46567

9

781,5762357

63,12376433

3984,609623

124033,5906

10

973,561133

205,638867

42287,34363

20310,5545

11

1063,284154

-81,98415448

6721,401586

82727,00249

Final total values:

180787,7

353177,6

Note – calculated by the authors

Thus, the Durbin-Watson coefficient in our case is 1,95.

It is generally accepted that if the obtained coefficient lies in the range 1,5 <DW <2,5, then there is no autocorrelation. Consequently, the constructed econometric model is effective and can be used in further research.

Using the obtained regression equation, the forecast for Y for 2020-2022 will be: 2020 – 1072,13 billion tenge; 2021 – 1183,25 billion tenge; 2022 – 1281,61 billion tenge.

To calculate the standardized variables and build a standardized regression equation, we use the previously obtained pair correlation coefficients and construct a normal system of equations (Formulas 6-9):

Discussions

Despite of the broad reflection of the scientific problems considered in this article in the publications of the scientific community, the issues of determining the component composition of innovative activity in order to form and effectively manage the innovative potential of enterprises are still controversial. At the same time, there is no clear opinion between scientists regarding the allocation of the role and place of “innovative potential”. It is important to note that very often there is an identification of concepts related to innovation and digital activity, especially industrial enterprises and clusters, which are actively moving to digital platforms and unifying various processes: from production to marketing and logistics. In our opinion, it is advisable to separate these concepts.

Future scientific research of the authors will be related to the study of the features of the functioning of backbone innovative-active industrial clusters, combining both innovative and digital components, actively meeting the realities of modern economic development and the transition to building a new type of economy in the context of the Fourth Industrial Revolution.

Conclusions

The results obtained show that the selection of factors for assessing the innovation activity of enterprises by describing using economic and statistical methods, in particular those presented, for example, by multiple regression equations, is advisable to carry out taking into account the calculated pair correlation coefficients, as well as multicollinearity, excluding those factors that do not have an effect on the dependent variable. At the same time, the most important aspect of conducting a study of this kind is to check the adequacy of the constructed model using the coefficient of determination, Fisher Criterion, Student's t-test and the Durbin-Watson coefficient. If the model is correct, then it can be used later to predict the analyzed indicator. 

 

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Year: 2020
City: Karaganda
Category: Economy