**4. Research methodology**

To answer the questions posed, we used econometric modelling based on data obtained by interviewing, consolidating information on companies from different databases, and carrying out statistical monitoring in order to test the hypotheses. The empirical analysis was based on cross data of Russian industrial companies. The stratified sample is represented by 252 Russian high-tech industry enterprises.

The limitations of the sample are that it is incomplete (the sample can be expanded during a more detailed research in the future) and biased toward companies located in Russia's largest cities because respondent companies were more readily available and had their own capabilities to produce and export high-tech innovations.

The tools used in this work make it possible to interpret exports of products, services, and technologies in terms of whether exports actually exist (export activities are carried out), scale (share of exports or, more precisely, of "foreign sales" in the firm's total sales), structure (technological services, finished products), and destination of exports (CIS and non-CIS countries; accordingly, CIS countries with a market similar to the Russian market and all other countries).

Learning-by-exporting effects were evaluated using information on different indicators of the levels of export activities, companies' efficiency and productivity (with the indicator being financial reporting metrics), and technological, product, organizational, and management innovations, including R&D expenditures. The principal body of data was taken from the Russian statistical database and questionnaires posted on the website of the analytical portal TAdviser (URL: http://www. tadviser.ru/index.php/Компании).

Apart from exports, there are other factors influencing innovational learning processes and development. In particular, "the industry to which an enterprise belongs and its size may affect propensity to innovate and implement new management technologies" [27]. An enterprise's innovation activity may be also associated with the age of the firm and characteristics of its owner (affiliation with a foreign holding company) [17, 20, 28]. A list of dependent variables and regressors is presented in **Table 3**.

If learning spillover effects are present in exports, then what is their nature? Perhaps, these are just some regularities; is the one who enters a foreign market (as a result of self-selection) originally more productive, organized, or more prone to innovation? To empirically evaluate the impact of these effects on productivity,

**9**

regarding their existence as such:

*Indicators of dependent variables and predictors*

*t*

 + ∑ *k*=1 3

= *b*<sup>1</sup> + ∑ *j*=1 4

ln*yi*

**Table 3.**

Predictors

*Knowledge Spillover Effects: Impact of Export Learning Effects on Companies' Innovative…*

**Model number Designation of dependent variable Dependent variables = indicators of** 

Y2 NewTech New technology implementation (takes

Y3 NewProduct Release of a new product, service (takes

Y4 Marketing Existence of marketing innovation expenditures

Y5 Exp Increase in the share of foreign sales (takes value

Size The firm's size (logarithm of the number of

Age The company's age (1, established before

Region 1, the company is located in the capital

Foreign Availability of an international office and/or

Exp\_period Classification of the organization into one of

Exp\_status Type of the company's principal sales market:

Y1 RD\_cost Existence of R&D expenditures (takes values

**companies' innovation behavior**

1 or 0 for each period)

employees)

in a region

the four groups

3—international

2003; 2, after 2003)

purely Russian company)

values 1 or 0 for each period)

values 1 or 0 for each period)

(takes values 1 or 0 for each period)

1 in case of an increase in the share of exports or 0 in case of its decrease for each period)

parent company abroad (1, otherwise 0—a

(Moscow, St. Petersburg, Moscow or Leningrad Region); 0, the company is located

(1, firms that exported their products in 2015–2017; 2, "new exporters" that did not have exports in 2015, but had exports in 2017; 3, "former exporters" that have left export markets; 4, firms that did not have exports in

1—local (market with a certain range of buyers in a part of the city, region, etc.) 2—national (Russia and CIS countries)

both periods of observation)

we constructed the following regression model based on an analysis of works that focus on exploring the phenomenon of external knowledge effects and the question

> *j*=1 3

We will use a common probit regression examining the dependencies of the value of the respective indicator in 2017 from its value in 2015, export status, and other characteristics of the organization to assess dummy variables (the variables are presented in

*bj*+1*Exp*\_*statusj* + *b*8*Foreign*1,0 + *b*<sup>9</sup> *Sizej*

*blSectorl* (1)

*bj*+1*Exp*\_*periodj* + ∑

*l*=1 2

*bk*+9*Agek* + ∑

*DOI: http://dx.doi.org/10.5772/intechopen.86255*


*Knowledge Spillover Effects: Impact of Export Learning Effects on Companies' Innovative… DOI: http://dx.doi.org/10.5772/intechopen.86255*

#### **Table 3.**

*Current Issues in Knowledge Management*

*growth in the company's productivity.*

**4. Research methodology**

tadviser.ru/index.php/Компании).

innovations.

even where there is a "self-selection" effect.

has been formulated.

Export orientation and innovation are alternative, competing investment projects. Perhaps, firms that have already entered a foreign market do not need additional investments in innovation development, since they are anyway borrowing the best, new things from abroad. To answer this question, the second hypothesis

*H2: Exporting companies are more likely to implement innovations (including organizational innovations) than firms oriented toward the local market (a positive learning effect of international interaction). Export activities, however, are not a linchpin of* 

The abovementioned hypotheses serve as a proof of the existence of a two-way link between export activities and innovation and effectiveness [13]. As a result of implementing innovations, stronger, more durable companies start to export (are self-selected in an attempt to expand abroad), which makes them even more competitive and productive through learning by exporting. Some researchers have proven [21] that companies' export orientation still leads to productivity growth

To answer the questions posed, we used econometric modelling based on data obtained by interviewing, consolidating information on companies from different databases, and carrying out statistical monitoring in order to test the hypotheses. The empirical analysis was based on cross data of Russian industrial companies. The stratified sample is represented by 252 Russian high-tech industry enterprises. The limitations of the sample are that it is incomplete (the sample can be expanded during a more detailed research in the future) and biased toward companies located in Russia's largest cities because respondent companies were more readily available and had their own capabilities to produce and export high-tech

The tools used in this work make it possible to interpret exports of products, services, and technologies in terms of whether exports actually exist (export activities are carried out), scale (share of exports or, more precisely, of "foreign sales" in the firm's total sales), structure (technological services, finished products), and destination of exports (CIS and non-CIS countries; accordingly, CIS countries with

Learning-by-exporting effects were evaluated using information on different indicators of the levels of export activities, companies' efficiency and productivity (with the indicator being financial reporting metrics), and technological, product, organizational, and management innovations, including R&D expenditures. The principal body of data was taken from the Russian statistical database and questionnaires posted on the website of the analytical portal TAdviser (URL: http://www.

Apart from exports, there are other factors influencing innovational learning processes and development. In particular, "the industry to which an enterprise belongs and its size may affect propensity to innovate and implement new management technologies" [27]. An enterprise's innovation activity may be also associated with the age of the firm and characteristics of its owner (affiliation with a foreign holding company) [17, 20, 28]. A list of dependent variables and regressors is presented in **Table 3**. If learning spillover effects are present in exports, then what is their nature? Perhaps, these are just some regularities; is the one who enters a foreign market (as a result of self-selection) originally more productive, organized, or more prone to innovation? To empirically evaluate the impact of these effects on productivity,

a market similar to the Russian market and all other countries).

**8**

*Indicators of dependent variables and predictors*

we constructed the following regression model based on an analysis of works that focus on exploring the phenomenon of external knowledge effects and the question regarding their existence as such:

$$\begin{aligned} \ln y\_i^t &= b\_1 + \sum\_{j=1}^4 b\_{j+1} \text{Exp}\_{-\text{period}\_j} + \sum\_{j=1}^3 b\_{j+1} \text{Exp}\_{-\text{status}\_j} + b\_8 \text{Foreign}\_{1,0} + b\_9 \text{Size}\_j, \\ &+ \sum\_{k=1}^3 b\_{k+9} \text{Age}\_k + \sum\_{l=1}^2 b\_l \text{Sector}\_l \end{aligned} \tag{1}$$

We will use a common probit regression examining the dependencies of the value of the respective indicator in 2017 from its value in 2015, export status, and other characteristics of the organization to assess dummy variables (the variables are presented in

**Table 3**). To eliminate the endogeneity problems "associated with the different direction of the cause-and-effect relationships between the size indicators and property parameters, the values of these predictors in the model are taken for the previous period" [27].

An attempt to use a linear regression to predict innovation activity of enterprises after entry into a foreign market does not make sense, as the linear form values are on a continuous quantitative scale, while the variable is measured discreetly [44]. Therefore, it is recommended that special regression models be constructed to investigate dependencies between binary variables (innovation indicators) and quantitative data (in our case, regressors).

There are two approaches that allow to construct such models. The first one involves building a linear probability model (using robust standard errors), which will not be used by us, while the second one involves building nonlinear models (logit and probit) [37]. These models capture dependencies between a variable and a data set as well as the probability that the *i* th value of a binary variable is equal to 1 if a certain condition is met [32].

The probit model differs from the logit model only in that the normal distribution density function is used instead of derivative logistic curve. In the other respects, probit and logit analyses are similar.

Their idea is that the likelihood function is maximized—there is a probability that what is present in our sample will be obtained randomly. In practice this means that we no longer pay attention to the sums of squares of the residuals and are interested in the behavior of the likelihood function.

We performed the required analysis of the collected data for 252 Russian companies, different in terms of affiliation with a variable, to construct a model.

In our sample, 55% of the respondents are located in the capital and in the Moscow Region (128 companies in the two capitals, Moscow and St. Petersburg, and nine companies in the Moscow Region).


**Table 4.**

*Descriptive statistics of inspected firms in the analyzed timeframe of 2003–2017, % of respondents.*

**11**

*Knowledge Spillover Effects: Impact of Export Learning Effects on Companies' Innovative…*

Most of the surveyed respondents (31%) worked in companies established before 1999; about 20% of the firms were established during 1999–2003, 2004– 2008, or 2009–2013 (about 65% during 1999–2013), and just 5% of the respondents

Exporting and non-exporting companies' characteristics are in **Table 4**.

We take 2017 as the "start of exports" for the purpose of dividing new and traditional exporting firms, while "former exporters" are understood to mean all those who left foreign markets in any year within the period under review.

To build probit models, we divided the companies into those established before

The export status, or the type of the principal sales market for Russian industrial companies (just as the other regressors), is fixed at the 2015 level to eliminate the endogeneity of factors, as the percentage of presence in international markets is

As regards the distribution of companies by the share of exports in total revenue, the picture in 2017 was as follows: 43% of the firms had a relative share of exports of <0.10, 13% between 0.11 and 0.25, and 22% over 0.75. Thus, about one-fifth of all

**Table 5** presents the results of the calculation of the relationship between the innovation behavior indicators and the export status of industrial companies. The hypotheses put forward by us on the selectivity of enterprises ("selfselection" for foreign markets), the existence of learning-by-exporting effects, and the influence of the duration of exports on the enhancement of learning spillover

Thus, "new" exporting companies, unlike "permanent" exporters, do not have a visible relationship between implementation of new products, technologies, and the start of exports (the significance of the coefficients was not confirmed, Ɓ < p, and Ha is not rejected, where Ɓ is the level of significance, Ha is the hypothesis on the absence of dependencies, or Ɓ\_*i* = 0). The coefficients themselves and the probabilities of the innovation behavior under study being exhibited are much lower than for similar traditional exporters. This can be explained by the fact that R&D investments which might have been initiated after or at the time of entry into foreign markets have not yet yielded results. That said, the status of "traditional" exporters increases the likelihood of investments in advanced research and develop-

For all innovation behavior indicators out of the five indicators considered for a group of traditional exporters, the sign in the models estimating regressor dependencies for a past period (2015) considered by us is positive, and the statistical significance (at the level of 1, 5 and 10%) was proven, indicating that stable export activities serve as an incentive for industrial companies to apply new technological, process, and marketing innovations, which previously were not included in the

Our research shows that the impact of external knowledge effects on the productivity of industrial companies depends on the geographical destination of exports: (a) markets in CIS countries plus Russia itself and (b) markets in non-CIS countries. In the case of exports abroad (primarily to West Europe and America), knowledge effects have a significant positive impact on Russian industrial companies, which begin to develop state-of-the-art technologies and increase R&D and marketing expenditures to boost sales of products and services and increase the

*DOI: http://dx.doi.org/10.5772/intechopen.86255*

and after 2003 (54.6 and 45.4%, respectively).

surveyed firms mainly generated revenue from exports.

effects were confirmed (the first hypothesis—partially).

ment by 38%. We believe that this statement is also true vice versa.

firm's plans, much more often compared to non-exporting firms.

were young novice exporters.

higher in 2016–2017 at about 22%.

**5. Research results**

*Knowledge Spillover Effects: Impact of Export Learning Effects on Companies' Innovative… DOI: http://dx.doi.org/10.5772/intechopen.86255*

Most of the surveyed respondents (31%) worked in companies established before 1999; about 20% of the firms were established during 1999–2003, 2004– 2008, or 2009–2013 (about 65% during 1999–2013), and just 5% of the respondents were young novice exporters.

Exporting and non-exporting companies' characteristics are in **Table 4**. To build probit models, we divided the companies into those established before

and after 2003 (54.6 and 45.4%, respectively).

We take 2017 as the "start of exports" for the purpose of dividing new and traditional exporting firms, while "former exporters" are understood to mean all those who left foreign markets in any year within the period under review.

The export status, or the type of the principal sales market for Russian industrial companies (just as the other regressors), is fixed at the 2015 level to eliminate the endogeneity of factors, as the percentage of presence in international markets is higher in 2016–2017 at about 22%.

As regards the distribution of companies by the share of exports in total revenue, the picture in 2017 was as follows: 43% of the firms had a relative share of exports of <0.10, 13% between 0.11 and 0.25, and 22% over 0.75. Thus, about one-fifth of all surveyed firms mainly generated revenue from exports.
