Research and development (R&D) are the summary of works, that are oriented on technological development. R&D is measured by the investments that are made. The main aim is to increase the company's Intellectual capital and gain additional competitive advantages, which will have a positive impact on the company's performance. However, R&D investments are associated with risk, since it is impossible to predict the results of research in advance, and therefore the cost recovery.

(Junarsin, 2011)

Patents/licenses/trademarks

Companies use a patent to assert its exclusive and unique right, authorship, and priority of an invention or design, protecting its intellectual property and preventing other companies from gaining the same competitive advantage. There is no doubt that a company's need for patents depends on the industry in which it operates, and the complexity of the procedure for obtaining a patent also plays an important role. Authors use the number of patents as a proxy indicator of a company's intellectual property and conclude that the number of patents is positively related to the added economic value of different companies. At the same time, the results of some studies show that in firms that intensively use Intellectual capital, patents and Research and development costs show a positive relationship, although an increase in the number of patents does not affect the company's financial performance. In our case, it will be interesting to check whether the effect of patent activity is observed in terms of the economic value of the company, and if so, in which sectors it is most powerful for Russian companies.

(Weatherly, 2003)

Organizational structure

It reflects the relationships between all levels of organization. Authors study the number of departments in the company, their functions and connections with each other.

(Li-Chang Hsu & Chao-Hung Wang, 2010)

Organizational performance

Organizational performance has always been an interesting research field for different researchers and management of organizations. The term organizational performance is defined by numerous concepts and it is connected with competitiveness, efficiency and productivity. Nowadays, diverse determinations explain the performance of the company and there is lack of one common and established interpretation.

Michel Lebas determines the performance of the firm as the process of achieving goals that are setting by company's management. It is claimed that performance can directly impact on capability and future (Lebas, 1995). Performance is also considered as strong relationship which involves six dimensions: innovations, efficiency, quality of operating processes, effectiveness, productivity, profitability (Chang & Hsieh, 2011). Therefore, it can be said that organizational performance is the achievement of these dimensions, which are measured as indicators of performance. Nevertheless, it can be a problem to create general approach to evaluate these indicators, as all dimensions are latent and cannot be measured by common indicators.

Measurement of the organizational outcomes is a significant and necessary technique for any company (Zaim, 2006). Performance includes three main types: market performance, including amount of sales, features of particular market, earning per share; financial outcomes connected with profit, return on equity, return and assets, interest rate of return, return in interest, investments; shareholder return assessed by economic value added, number of shares, dividends. (Richard, Yip and Devinney, 2009). It is important to measure organizational performance on different levels: the company level, operational process level, employee unit level. Dyer and Reeves emphasized human capital outcomes in organizational performance measurement, consequently four types of performance measurement are presented: financial results that are evaluated by return on investments, net profit, return on equity, return on assets, return of interest rate, profit margin, etc.; operational outcomes of organization which include quality and variety of products or services, efficiency, developed structure of the company; human resources that can be measured by level of personnel turnover, number of trainings, share of wage costs; market results which are presented by level of investments, price of stocks, market growth level. Finally, according Jardon and Martos M. (2012), the most widely used types of companies' performance measurement are financial performance indicators and market-based performance indicators.

Financial performance is used as a fundamental base for company's performance evaluation. Accounting or financial indicators are effective indicators of company's profitability (Hubbard and Bromiley, 1995). According to Singh and Perlmutter (2000), financial measurements indicate the results of management working activity, operational process, level of business model productivity. In addition, financial indicators are the most conventional and accessible means of evaluation performance of the organization. The strong connection between financial indicators returns and economic returns confirms the reliability to apply financial indicators in order to assess organizational performance. Correlation indicator between financial rates of return and economic rates of return is 0.75. Based on previous studies, the most frequently used financial indicators are determined and presented in Table 5.



Table 5. Summary of financial performance indicators.

Indicator	Measure/meaning	Authors
Return on investment (ROI)	Presented as the dividing net operating profit by net book value of assets	(Westhuizen & Kok, 2006); (Tikhomirov & Gorushkina, 2016); (Watson & Stanworth, 2015)
Return on assets (ROA)	Evaluated as ratio of the net operating profit to the assets of the company indicated in the beginning of the year	(Neumann & Tome, 2006); (Wudhikarn, Chakpitak & Neubert, 2018); (Lizbetinova, Hitka & Caha, 2017)
Return on equity (ROE)	Can be evaluated as dividing the net profit by the book value of shareholder's equity.	(Shamari, Nour & Sharabati, 2016); (Saaed, Sami & Lodhi, 2013); (Klock & Megna, 2000)
Return on sales (ROS)	Assessed as the ratio of net operating profit divided to the total amount of sales in the recorded period.	(Ghahroudi, 2011), Millet-Reyes and Zhao (2010), Singh and Gaur (2009),
Net operating profits (NOP)	Measured as the organization's revenue minus the cost of goods sold and operating, administrative expenses.	Millet-Reyes and Zhao (2010), (Richard, Yip and Devinney, 2009).
Operating cash flow (OCF)	It is evaluated by the difference between cash flow and earnings. It is equal to net operating profit plus noncash expenses minus noncash sales.	(Shamari, Nour & Sharabati, 2016); (Saaed, Sami & Lodhi, 2013); (Richard, Yip and Devinney, 2009).
Growth in sales (GRO)	It is calculated as the difference between current sales and previous year's sales to volumes by previous year.	Herri (2011), Uadiale (2010), Singh and Gaur (2009), (Welbourne & Pardo-del-Val, 2009)
Earnings before interest and taxes (EBIT)	It indicates the company's profit, that calculated by difference between revenue and costs of goods sold and selling costs.	Rouf (2011), Ehikioya (2009), (Richard, Yip and Devinney, 2009).
Earnings before interest, taxes, depreciation, and amortization (EBITDA)	Evaluated as the total amount of the profit plus interest, depreciation and amortization.	Rouf (2011), Bozcuk (2011), Ehikioya (2009), Ting (2008)
Profit margin (PM)	Measured by the ratio of the net operating profit by the total amount of sales.	(Shamari, Nour & Sharabati, 2016); (Saaed, Sami & Lodhi, 2013); (Klock & Megna, 2000)

Market-based performance measurement, which is one more type of organizational performance measurement, is defined as a long-term measure. The most significant benefit of this type of indicators is creating the future perspective for a company and representing of the discounted present value of future cash flows. (Chaney & Philipich, 2002). It is important to highlight that the market-based performance indicators of the organization impact on the company's success, especially organizational productivity (Wahla, ShahSyed & Hussain, 2012). According to previous researches, the most widely used market-based performance indicators are defined and presented in Table 6.

Table 6. Summary of market-based performance indicators.

Indicator	Measure/meaning	Authors
Easrnings-per-share (EPS)	It is evaluated as ratio of net operating profit minus dividends paid to preference shares divided by the number of common stocks issued	(Chaney & Philipich, 2002), (Richard, Yip and Devinney, 2009)
Tobin' Q Ratio	It is calculated as the market capitalization divided by the total assets of the organization.	(Wahla, ShahSyed & Hussain, 2012), (Welbourne & Pardo-del-Val, 2009)
Price of the stock	The total price of stocks presented on the market	Ting (2008), Bauwhede (2009), Wei (2007)
Market Value Added (MVA)	It represents the difference between the market value of an organization and the book value of its equity.	Bozcuk (2011), Ehikioya (2009), (Richard, Yip and Devinney, 2009).

Organizational performance management is a crucial factor that leads to the increasing effectiveness of company's strategies and management. It is an important fact that performance measurement allows a company to succeed through the instrument of monitoring, communication, problem identification. Moreover, measurement of performance gives an opportunity to determine and set the company's strategies and next steps for organizational development, which will affect the advancement of a company. It is significant to discuss fundamental roles of organizational performance measurement. First of all, it is monitoring and control business progress that can manage the goals achievement and focus on organizational outcomes in order to determine the current and future position of the firm on the market. Second important aspect is monitoring the established strategies. So that the efficiency of all operational processes and long-term goals of an organization can be achieved based on analyzing organizational strategic plan. The third fundamental role is diagnostics. In the case of business declining, the organizational performance measurements can indicate the possible reasons and appropriate solutions. Consequently, companies will find and analyze problems, protect the future development, reducing the amount of any challenges. Moreover, performance measurement provides supporting decision making, that allows a company to emphasize stages and causes which influence the company's success. In addition, the opportunities, which the company can use for future progress and development, are highlighted during the performance measurement. (Welbourne & Pardo-del-Val, 2009) Finally, performance measurement shows the level of personnel motivation and communication processes among all levels of organizational structure.

Tobin's Q Ratio has been widely used in the studies related to intellectual capital. The relationship between intellectual capital indicators and Tobin's Q Ratio was confirmed in several studies. According to Wang (2013) the significant positive relationship between intellectual capital, measured by VAIC, and Tobin's Q is confirmed. In 2016, correlation between structural capital and Tobin's Q is proven, indicating a medium strong relationship, which is equal to 0.48 (Welbourne & Pardo-del-Val, 2009). In 2017, an increase in Tobin's Q is associated with an increase in relational capital (correlation coefficient of 0.424) and human capital (coefficient of 0.281) (Welbourne & Pardo-del-Val, 2009). Moreover, there is a significant impact of intellectual capital on organizational performance, presented by Tobin's Q.

Overall, the main benefit of Tobin's Q is that it is a crucial indicator of organizational performance for investors, which can influence their decisions. It is also important to highlight that Tobin Q indicates long-term opportunities for future development. One more advantage is that Tobin's Q Ratio reveals the present value of future company's earnings (Wu, Lee, & Wang, 2012).

7. Research question statement

The analysis of the literature review show that the majority of the Russian studies are devoted to restrictions for direct use of foreign management theories in Russia. It can be said that the question related to the role of the intellectual capital inside Russian organizations remains open (Dumay & Garanina, 2013). There are few studies which are related to the problem of using intellectual capital and its influence on organizational performance (Cabello-Medina, Loґpez-Cabrales, & Valle-Cabrera, 2011). Consequently, based on literature review, it can be said that intellectual capital has been mostly analysed from the theoretical perspectives, discussing its features, functions and structure of intellectual capital. Nevertheless, the importance of measuring the impact of intellectual capital is increasing (Inkinen, 2015).

Foreign studies present results regarding relationship between intellectual capital and performance, however the results obtained often differ from Russian researchers due to the market and cultural difference (Wahla, Shah, & Hussain, 2012). It is important to highlight that the review of foreign studies allows to sum up that intellectual capital in developed countries plays a crucial role in creating organizational value (Buallay, 2017). However, intellectual capital implementation in developing countries, Russia market especially, should be researched more, as the majority of Russian companies do not have a common approach for measuring and effectively using of intellectual capital (Dumay & Garanina, 2013).

It is important to discuss the methods of measuring the impact of intellectual capital components on organizational performance. First of all, most previous researchers analyse the role of intellectual capital by one of its components. For example, the influence of human capital on the performance is confirmed (Chang & Hsieh, 2011). However, the intellectual capital is a complex of many different indicators, which should be taken into account, and analysing it from one perspective cannot bring the full review. Therefore, in this study three components of intellectual capital will be analysed in terms of their influence on performance.

In order to increase the competitiveness of Russian business it is necessary to understand the methods and tools of managing and evaluating intellectual capital (Dumay, Garanina, 2013). To do this, the corporate management needs to understand what elements of intellectual capital directly affect the performance of companies. The research question that was dedicated for consideration in the research: What is the impact of intellectual capital on the organizational performance?

Methodology

Sample of the study

The design can be determined as explanatory, due to the existed models of evaluating the intellectual capital are used, with the deeper analysis and focusing on Russian market. The purpose of the paper is to evaluate the impact of three components of intellectual capital (human capital, relational capital, structural capital) on organizational performance of Russian modern companies. An appropriate sample should be determined in order to find the results. The sample of the study consists 70 Russian companies from 5 chosen industries: oil and gas, telecommunication, retail, IT and transport and logistics. Top public companies from each industry are taken. It is done due to two reasons. First of all, in general, large companies develop intellectual capital and make investments based on the appropriate budget. Secondly, to conduct the analysis open data is necessary, such as financial and annual reports. It is clear that this type of data is provided generally by large publicly traded companies. Therefore, the main factor of sample design is the availability of the data. (Hejazi, Mehrdad Ghanbari & Alipour, 2016). In addition, this method of sample design was implemented in majority of foreign studies, that analyzed the role of intellectual capital. The reason to choose these particular industries can be explained by the importance of developing intellectual capital and are discussed below.

Oil and gas companies were among the first to realize the importance of developing intellectual capital inside the companies. Oil sector is adapting to transforming social and economic conditions, increasing intellectual capital. High level of competition in the oil and gas industry, regarding unique natural conditions, in order to decrease oil production expenses, motivate to use new innovative technologies and knowledge. In order to incline the level of competitiveness and become the leader on the market, oil companies grow the intellectual capital base, applying long-term innovative development programs. The share of oil refining and its primary production on companies' equipment and innovative solutions is 30% and 35% respectively.

Telecommunication industry is mostly based on the soft skills, particularly networking and creative thinking and flexibility of employees. All these characteristics are included in intellectual capital, especially in human capital. Therefore, it becomes more important to provide education and trainings for employees' efficiency that will allow to increase productivity. The features of the human capital are clearly manifested in situations in the cases of engaging in intellectual work and the development of modern innovations that it is most typical for intellectual companies, especially telecommunications business. The analysis of the obtained data allowed the author to conclude that telecommunications are the most "learning" sectors of the economy.

Retail industry is chosen due to several reasons. First of all, there is an increasing level of competition on the Russian retail market for last years. According to these conditions, companies need to find new solutions in order to be competitive. In general, innovative solutions are provided not by tangible assets, but intangible ones, especially intellectual resources - human, relational, structural. Moreover, retail market is one of the most significant markets for Russian economy. Its level of development impacts on economic and social region condition, grow of customer market, dynamic of economy development. Effectiveness of this industry provides increasing use of innovations, defines the level of pricing and productivity of business.

The development of intellectual capital in transport and logistics is a key success factor, as innovative technologies allow to optimize the use of resources and increase the productivity of transport companies. So, in the market, modern modes of transport are becoming computers to the same extent as computers. Sensors will soon be able to track vehicle characteristics and notify the driver of the need for maintenance. Intelligent engines will be able to switch between different fuel sources depending on the driving conditions. And self-repairing software can detect and fix failures before they occur, thus avoiding a completely new type of "crash". Completely new industries may arise to service and protect the next generation of smart devices. In the field of logistics, the main trend is the robotization of warehouses and the use of artificial intelligence in online monitoring and customer-oriented service.

It is clear that the main competitive advantage in IT companies is implementation and development of the intellectual capital. Consequently, through increasing the value of intangible assets organizations can improve the performance and provide future development. IT companies invest in intellectual capital in order to set strong relationships with employees, to increase the level of customer loyalty, to generate the value added based on innovations and technologies creating This competitive advantage, which is necessary for operating on the modern market, will be transformed into the economic value, increasing profit in the future. It can be said that IT companies are the leaders in intellectual capital development, therefore it is quite important to include them in the sample and analyze the impact of intellectual capital on the organizational performance.



Table 7 The five chosen industries

Industry	Research
Oil and gas	(Dzenopoljac & Shahnawaz, 2009); (Rehman, Ashgar & Rehman, 2014)
Telecommunication	(Shamari, Nour & Sharabati, 2016); (Saaed, Sami & Lodhi, 2013); (Klock & Megna, 2000)
Retail	(Westhuizen & Kok, 2006); (Tikhomirov & Gorushkina, 2016); (Watson & Stanworth, 2015)
IT	(Ericson & Rothberg, 2015); (Zhang & Lv, 2015)
Transport and logictics	(Neumann & Tome, 2006); (Wudhikarn, Chakpitak & Neubert, 2018); (Lizbetinova, Hitka & Caha, 2017)

8. Data collection

This study is quantitative with the secondary data analysis. The analysis of study is considered to be cross-sectional. This type of analysis is used due to analyzing unique indicators, such as net promoter score (NPS), website quality, number of patents, which are evaluated and analyzed once. All indicators are collected manually. The main sources of data are the official web sites of the chosen companies, annual and financial reports, presentations for investors, news and articles regarding to particular company. Annual and financial reports are taken from 2018 due to the last presented year in most of the chosen companies. Financial reports are presented in several types. IFRS is chosen as the accounting standards based on following reasons. Firstly, IFRS financial statements are choosing by investors and other stakeholders in order to evaluate the company's value and determine appropriate solutions about future investments. Secondly, IFRS unites all subsidiaries of the company and evaluates the performance of the whole group of the company. Data for intellectual capital's indicators is found find separately for human, relational and structural components. Based on literature review, independent variables for each component of the intellectual capital are chosen and presented in Table 7. Dependent variable is presented by Tobin's Q. It is the value of market capitalization divided by total amount of company's assets. Market capitalization of 2018 for the from the research sample is obtained from Moscow Exchanged Site, while total assets are collected in the financial statements.

The variables of the research are divided into two parts: independent and dependent ones. Independent variables include 9 indicators, 3 per one component of intellectual capital. Moreover, there are 3 control variables: number of employees, leverage and EBITDA.

Table 8. Variable Descriptions

TWC	Total share of wage costs (in percentage)
Productivity	Employees productivity (in millions per employee)
Trainings	Total share of trained employees (in percentage)
NPS	Net promoter score (in scores from 0 to 100)
Mcosts	Total share of marketing costs (in percentage)
Website	Website quality (in 5-point scale)
Patents	Number of patents, licenses, trademarks
R&D	Total share of R&D costs (in percentage)
IA	Intangible assets
Employees	Number of employees
Leverage	Financial leverage (Debt/Equity)
Ind. IT	1 if IT company, 0 otherwise
Ind. Oil	1 if oil and gas company, 0 otherwise
Ind.Retail	1 if retail company, 0 otherwise
Ind.Telecom	1 if telecommunication company, 0 otherwise

Starting with, human capital indicators:

· Total share of wage costs

This indicator shows the ratio total amount of personnel costs to operating expenses. It usually fluctuates from 5% to 40%. The main benefit of this measurement is the ability to compare and analyze companies with different age, number of employees, profit, size. It is calculated by dividing personnel expenses by operating expenses. It will be done based on financial statements of the company, especially part of operating, administrative, commercial expenses. Hence, this indicator can be seen as relative one and provides an opportunity to get objective results, examine which of the companies spend more resources on the employees.

· Labor productivity

The output of one employee and the labor intensity of a unique production. The first way to estimate labor productivity is to show output which is presented by one employee per unit of time. It is measured by the unit of production by time spent on producing a unit of production. The increase in the in-labor productivity is affected by key three factors: saving and investment in tangible capital, research and development and human capital. There is another evaluation method, which is used in this study. To find labor productivity in this research, the revenue of the companies was divided by the number of employees. intellectual capital regression pearson

· Total share of trained employees

Trainings are an important part of investing and development staff. They increase qualification of employees, that in turn raise the efficiency and motivation of staff. The share was calculated by the number of trained employees divided on the total number of employees on the end of the 2018. These indicators help to examine which of the companies pays more attention on the staff development and the contribution in the human capital. The indicator is taken from the annual reports in the block personnel.

Relational capital variables are:

· Net Promoter Score (NPS)

This indicator is collected mostly from annual reports, however, in some cases presentations for investors, articles and news published on the official company's website are used to find NPS indicator. This measure is calculated by difference between number of loyal customers and number of disaffected customers. It can fluctuate from -100 % to 100 %. It can be said that NPS more than 50% is valued as great indicator. Hence, this index shows the level of customer loyalty of the company.

· Marketing costs

Marketing costs are related to selling expenses. They can be divided into several major parts: online marketing, public relations, events, advertising campaign. These instruments help company to set strong relationships with customers, to provide high quality communicational channel and to promote the company. These costs are presented in financial reports, part of operating & commercial expenses.

· Website quality

This indicator is assessed by the chosen criteria and is presented by the five-points scale, 1 point for one criterion. Overall, it can be five points maximum and for this indicator.

- Information for investors (Annual reports, Financial reports, Presentations)

- Multi-language content (English and Russian)

- Design of the website (images, videos, usability, easy navigation, readability)

- Customer service (communication channels, contact information)

- Multi-platform adaptation (website is adaptive for different platforms: PCs, tablets, mobile phones)

Structural capital indicators:

· Intangible assets

Intangible assets are identifiable non-monetary assets that do not have a physical form; they are part of non-current assets. Intangible assets are one of the most significant and valuable indicators in the intellectual capital as it is presented in financial statement. This indicator shows the financial value of the intellectual capital, it is included in assets of the company and presented in the financial reports of the company.

· Patents

This is the exclusive right to the innovation. Patents give an owner the opportunity to choose and secure who and how can use this innovation. Nevertheless, the patent owner provides the detailed technical information related to the invention in the special patent documents. Patents play an invaluable practical role in our lives. As a form of rewarding creative ideas, a patent encourages the development of innovation and new technologies in any field. The number of patents show how intellectual property is developed in the company. The indicator is taken from annual reports and the patent base.

· Research & Development

R&D (research and development) includes the set of all activities of the organization, which are directed to create and develop new products and innovative solutions of the company, or from theoretical perspective, scientific researches of the company. R&D indicator in the research is calculated by the ratio of R&D expenses to total operating expenses of the company. It is done in order to have an opportunity to compare companies with different sizes. The indicators of R&D costs and operating costs are collected from financial statements.

Dependent variable: Tobin's Q

Organizational performance of the company is analyzed based on Tobin's Q Ratio. It is calculated as the ratio of market capitalization of the firm to total assets of the firm.

In general, Tobin's Q indicates the relationship between market value and intrinsic value. It can be said that Tobin's Q Ratio can show overvaluation or undervaluation of the company. If Tobin's Q evaluation is equal to the number between 0 and, the value of company's stocks is less expensive than the costs of total assets replacement. It means that the market undervalues company's stocks. However, when Tobin's Q is higher than 1, the market value is greater than assets' value. In this case the company is overvalued, and profit of the company is higher than the costs to replace company's assets.

It is important to discuss the application of Tobin's Q Ratio. Hence, it was analyzed that there are two results of ratio interpretation: undervalued company or overvalued company. Undervalued company, if Tobin's Q is less than 1, can be an object of investors' interests. It can be attractive solution as the purchase of such company will be less expensive than creating a new similar one. Hence, demand of such company purchase will increase and, as the result, it will lead to stock price increasing. Finally, Tobin's Q Ratio will increase with the grow of market value respectively. Tobin's Q which is higher than 1 represents the overvalued company. Such companies can enhance the market competition as their business models are becoming successful on the market. In order to generate higher profit, other companies use similar tools to create market value. Therefore, due to huge number of competitors the market value of overvalued companies will decrease, reducing market stock price. Finally, Tobin's Q will decline in this way.

Tobin's Q Ratio is chosen as dependent variable, as it has been widely used in the studies related to intellectual capital. The relationship between intellectual capital indicators and Tobin's Q Ratio was confirmed in several studies. According to Wang (2013) the significant positive relationship between intellectual capital, measured by VAIC, and Tobin's Q is confirmed. In 2016, correlation between structural capital and Tobin's Q is proven, indicating a medium strong relationship, which is equal to 0.48. In 2017, an increase in Tobin's Q is associated with an increase in relational capital (correlation coefficient of 0.424) and human capital (coefficient of 0.281). Moreover, there is a significant impact of intellectual capital on organizational performance, presented by Tobin's Q.

Overall, the main benefit of Tobin's Q is that it is a crucial indicator of organizational performance for investors, which can influence their decisions. It is also important to highlight that Tobin Q indicates long-term opportunities for future development. One more advantage is that Tobin's Q Ratio reveals the present value of future company's earnings (Wu, Lee, & Wang, 2012).

Control variables

Control variables are required in this research model in order to define and analyze their effect on the dependent variable. The research model of this study requires several control variables such as number of employees and financial leverage.

· Financial leverage

Financial leverage is the ratio of debt capital to equity (in other words, the ratio between debt and equity). Also, financial leverage or the effect of financial leverage is the effect of using borrowed funds to increase the size of operations and profits, without having enough capital for this. The size of the ratio of borrowed capital to its own characterizes the degree of risk and financial stability. The indicator is taken for controlling the explanatory variable and calculated manually. The indicator of equity and debt were taken from financial statements, balance sheet.

· Number of employees

The size of the company is should be taken in control variables to make the results objective. Number of employees is one of the most common variables that is taken to identify the size of the company. The number of employees is taken from annual report.

9. Methods of analysis

After gathering data is preprocessed in Excel into format which is appropriate for further export in (Appendix 1). Data base is created in the table format filtered by the industry. There are 9 independent variables, grouped by three components of intellectual capital: human, relational, structural. Also, three control variables are presented. The dependent variable, presented by Tobin's Q, is calculated as the ratio of market capitalization to total assets. Then, database with all the variables is processed by several steps.

As a starting point, descriptive statistics for variables are conducted. Such indicators as min/max values, mean, standard deviation is evaluated. Descriptive statistics allow to understand that all variables are between minimal and maximum indicators. It allows to quickly analyze the data, make general review of the data sample. As a result, we have got a table with these indicators for each variable.

Table 9. Summary Statistics

Variable	Mean	Standard deviation	Minimum	Maximum
Wage costs (%)	22.939	11.017	4	61
Labour productivity	11.969	13.494	1.460	78.400
Trainings (%)	56.588	21.589	17	88
NPS (loyalty)	56.000	14.569	32	80
Marketing costs (%)	36.469	12.398	17.57	65,42
Website quality	4.280	0.757	3	5
Patents, licenses, trademarks (number)	358.340	331.699	30	1348
R&D costs (%)	0.131	0.062	0.020	0.260
Intangible assets	21945500000		132000	161205000000
Number of employees	74354.860	137724.878	250	752200
Leverage	1.302	16.349	-93.344	61,149
Tobin's Q	0.756	0.849	0.073	4,924
Number of observations	70

In order to determine the methods of analysis, the data should be tested for normal distribution, in other words, data is distributed based on the normal distribution law. There are two possible variants. The first one is related to the situation when the data is distributed according to the normal distribution law. In this case parametric methods of analysis will be chosen. However, if the data does not correspond to this law, nonparametric methods will be used. Therefore, the next decisions about the research design will depend on the results of normal distribution tests.

Variable distribution - is the regularity of occurrence of the variable's various values. Parameters of distribution are related to the numerical characteristics that shows the average location of variable's values; how these values are changing; whether there is a predominance of variable's values appearance. Normal distribution is also called the Gauss distribution and represents the situation when extreme values of the variable are rather rare, and they occur more often when the values are becoming closer to the average value.

A normal distribution is presented by a bell-shaped form, X-axis indicates the values of the variable and the Y-axis is the probability to meet particular indicator of the variable. In this case, the indicator that corresponds to the highest point is called mathematical expectation. It is decided to check the normal distribution of the dependent variable - Tobin's Q. The results of test shows that Tobin's Q is not normally distributed.

Figure 3 Distribution of the Tobin's Ratio before the logarithm

In order to transform the variable's distribution to normal, logarithm of this variable is taken. Logarithm (logarithm calculation) is applicable in various research fields. It is widely used in econometrics -- the science that studies quantitative and qualitative economic relationships between phenomena and processes. The relevance of logarithmization is explained by the unique properties of logarithms, which have determined their widespread use for significantly simplifying labor-intensive calculations. The study of the plot of logarithm multiplication is replaced by a much simpler addition, division -- by subtraction, and raising to a power and extracting the root are converted, respectively, to multiplication and division by the exponent.

Linear model: Yi = б + вXi + еi

Linear-log model: Yi = б + вlogXi + еi

In the linear-log model, the definition of the coefficient в€ is that a one-unit raised in logX will show a raise in Y of в€ units. The aim of the logarithm in regression is to make data more appropriate for the normal distribution, it is an essential point for parametric research. Logarithm converts skewed (asymmetric) data to more symmetrical data, since the scale is stretched near zero, and small values grouped together are distributed along the scale. At the same time, logarithm gathers together large values at the right end of the scale. The most commonly used decimal and natural logarithms. Equal distances on the logarithmic scale correspond to equal percentage increases on the original scale, rather than equal increases in values. It is checked out by visual evaluation of distribution plot.

Figure 4 Distribution of the Tobin's Q after the logarithm

As a result - normal distribution of dependent variable was substantiated. If it would not be done - the model consistency and its results could be questioned. Therefore, parametric analysis can be done.

After work with dependent variable, Pearson correlation coefficient is used to study the relationship of two variables measured in metric scales on the same sample. It allows to determine the level of proportion of the two variables' variability. Pearson correlation analysis was employed for reaching two goals: 1) for exploration of correlation between dependent variable and independent variables; 2) for revealing correlation between independent variables which can cause multicollinearity problem in further modelling.

Preliminary work with variables is followed directly by performing multiple regression analysis. The goal of this analysis is to examine independent variables' ability to predict changes in dependent variable. Before analysis we select independent variables on basis of previous correlational analysis: independent variables are checked for possibility of multicollinearity and those ones, which produce such effect are excepted. Then, multiple linear regression is exploited for obtaining the model. This tool is used for this research in order to define the impact of the components of intellectual capital, which are independent variables and organizational performance, analyzed by Tobin's Q Ratio (dependent variable).

Multiple linear regression, that is also called multiple regression, is a statistical tool where there are independent variables to create the result of an independent variable. The aim of multiple linear regression is to examine the linear relationship between the independent variables and dependent variable. The advantage of multiple linear regression over a simple linear regression is that using various independent variables in the model it that increases the proportion of explained variance of the dependent variable, and thus improves the model's compliance with the data. In other words, new added variables increase the coefficient of determination in the model.

The equation for the multiple regression in the research is the following:

=

Where:

= Tobin's Q

= Total share of wage cost

= Labor Productivity

= Total share of trained employees

= NPS (Net Promoter Score)

= Total share arketing costs

= Website quality

= Intangible assets

= Number of patents

= Total share of investments in technologies

= Number of employees

= Financial leverage

= The error of the model

Moreover, to check the impact of three components of intellectual capital separately there will be done three models, where will be analyzed the impact of human, relational and structural indicators appropriately on the performance of organization presented in Tobin's Q.

Firstly, the multiple linear regression model for human capital will be made:

,

Secondly, the multiple linear regression model for relational capital will be made:

Thirdly, the multiple linear regression model for structural capital will be made:



,

Consequently, independent variables, which distort model results are excepted. For final check of presence of multicollinearity VIF (Variance Inflation Factor) test is carried out. The VIF test evaluates the level of the variance of a coefficient overestimation in regression model due to multicollinearity in the model. It is considered that for each independent variable VIF should be less than 5. If VIF is equal to 1, it means that there is no correlation between analysis.

Figure 5 The conceptual framework of the research



Results

The correlation matrix is constructed based on Pearson correlation. As it can be seen, independent variables are tested on the multicollinearity factor in order to get rid of biased coefficient estimates in the regression model. Correlation matrix includes correlation coefficients between all variables, which show the strength of the relationship between them.

Table 10 Pearson correlation between variables

	Tobin's Q	TWC	Productivity	Training	NPS	Mcosts	Website	Patents	R&D	IA	Employees	Leverage
Tobin's Q	1.000
TWC	0.542**	1.000
Productivity	0.222	0.210	1.000
Training	0.216	0.089	0.368	1.000
NPS	0.720**	0.531*	0.228	0.055	1.000
Mcosts	0.461**	0.347	0.188	-0.44	0.447*	1.000
Website	0.453**	0.269	0.329	0.291	0.442	0.248	1.000
Patents/	0.676**	0.480	0.362	0.204	0.420	0.348	0.302*	1.000
R&D	0.573**	0.390	0.160	-0.106	0.462*	0.401	0.291	0.469	1.000
IA	0.015	0.322	0.017	0.003	0.107	0.198	0.156	0.150	0.216	1.000
Employyes	0.172	0.037	0.031	0.164	0.223	0.158	0.064	-0.166	0.306	0.410*	1.000
Leverage	-0.089	0.008	-0.028	0.010	-0.251	-0.103	-0.132	0.116	0.123	0.050	0.008	1.000

It was created Pearson correlation coefficient to study the relationship between nine independent variables and dependent variable measured in metric scales on the data sample. It allows to determine how proportional the variability of indicator is. Moreover, it helps to check the multicollinearity between variables. The value of the correlation coefficient varies from 0 to 1 depending on the strength of the relationship.

It can be seen from the table that there is strong significant correlation coefficient between total share of wage costs (TWC) of the companies and the performance, measured by Tobin's Q. Net promoter score has the strongest relationships with the Tobin's Q in comparison with other variables. Marketing costs, website quality, total share of R&D expenses has positive and statistically significant relationship with the independent variable. Labor productivity, the percentage of trained employees and intangible assets show weak correlation with the Tobin's Q. Control variable financial leverage show indirect weak relationship, it can be explained by the definition of total leverage, that is presented in the form of the total debt divided by the total equity. Hence, the higher the financial leverage, the more debt company has. That could negatively affect the performance of the company and Tobin's Q appropriately.

There is also the weak correlation between independent variables that shows that there is no multicollinearity between variables. However, there is significant relationship between the share of total wage cost and the net promoter score, the net promoter score and the share of marketing costs, the website quality and patents/licenses/trademarks, the net promoter score and the research and development. The correlation coefficients between independent variables are in the acceptable range. Hence, the regression model could be done as the next step.

The next step is to construct multiple linear regression. There are several regression models which are presented. First of all, three models for estimating impact of each intellectual capital components on Tobin's Q are created. The results are shown in Table 9,10,11.

Table 9 includes the result of the regression model which is created based on the indicators of human capital. It is important to highlight several indicators in this model. It should be noticed that logarithm of dependent variable - Tobin's Q - is taken. Hence, beta-coefficient should be multiplied by 100 in order to get the appropriate interpretation. It can be seen from the regression model that total share of wage costs has a positive and significant impact on Tobin's Q Ratio. Particularly, a rise of the share of wage costs from total company's costs by 1 percentage point leads to the rise of Tobin's Q Ratio by approximately 2.71 percentage. Moreover, it is revealed that the labor productivity indicator is significantly and positively connected with Tobin's Q. In detail, the increase of labor productivity indicator by 1 percentage is related to the increase of company's performance by 1,99 percentage. Furthermore, the impact of total share of trained employees seems to be significant and positive. The grow of total share of trained employees by 1 percentage will affect the grow of Tobin's Q Ratio by 1.66 percentage. In this regression model the control variable number of employees is excluded, as the independent variable involves this indicator. Labor productivity is calculated as total revenue divided by number of employees. Financial leverage as a control variable has a negative influence on organizational performance, presented by Tobin's Q, which can be explained as negative influence of debt on the investors' decisions and as the result on the market capitalization.

F-statistics and Probability>F are used in order to define the statistical value of this regression model and show that this regression model is significant. R-squared - the coefficient indicates the level of the model accordance to the data. The closer it is to 1, the stronger the dependence is. The normal indicator of R-squared should be more than 50 %. There is a limitation while using R coefficient - it grows with any number of factors, so a high value of R coefficient can be deceiving when there are many factors in the model. In order to reduce this problem from the coefficient, the adjusted determination coefficient was used. Therefore, in this model R-squared adjusted is equal 51.54 %, which means that the regression model explains the 51.54 % of observations.

VIF (variance inflation factor) test is done in order to detect multicollinearity, the relationship between independent variables, in the regression model. In this model VIF of the variables are at an acceptable level. In general, VIF should be less than 10. Therefore, the strong positive significant relationship between independent variables of this regression model is not found, as the result there is no multicollinearity between them.

The formula for the regression model of human capital:

,

Table 11 Regression Model (Human capital)

Variable	Beta	Std. Error	t value	P - value
(Intercept)	-2.750337	0.478523	-5.748	0.0000***
Total share of wage costs	0.027126	0.011277	2.406	0.0207 *
Labor productivity	0.019964	0.009800	2.037	0.0481 *
Total share of trained employees	0.016587	0.006511	2.548	0.0147 *
Financial leverage	-0.010858	0.006091	-1.783	0.0821
Industry - IT	0.866988	0.370796	2.338	0.0243 *
Industry - Oil & Gas	-0.407038	0.330334	-1.232	0.2249
Industry - Retail	0.910390	0.383292	2.375	0.0223 *
Industry - Telecom	0.359436	0.356943	1.007	0.3198
N	70
F-statistics	5.352
Probability > F	0.000***
R2 adj.	0.4154

VIF - test

TWC	Trainings	Productivity	Leverage	Ind_it	Ind_oil	Ind_retail	Ind_telecom
1.750040	2.240116	1.982932	1.124567	2.138049	2.166565	2.719735	2.358663

Table 10 shows an analysis of relational capital indicators and Tobin's Q Ratio. According to the results, there is strong significant and positive influence of the Net promoter score that shows customers loyalty on the performance of the company - Tobin's Q. In the case, when the customer loyalty of the company increases by 1 percentage, Tobin's Q will grow in approximately 5.28 percentage. Total share of marketing costs has not significant results in the regression model, that could be explained by the business model type of the companies. Moreover, the sample size consists of the biggest and most successful companies in Russia, that are already well-known. Hence, the marketing cost in comparison with other indicators could be not so significant. Moreover, the website quality has not significant and strong impact on Tobin's Q. The value mean of website quality is 4.280 out of 5, this means that the points for the websites of the companies are rather high and approximately fluctuated between 4 and 5, based on the data in the biggest companies there is high quality website. Hence, there is no significant differences between point, that in the result show weak relationship with the Tobin's Q Ratio. The R-squared adjusted is 0.882 that means that data sample fits the line plot and regression model explain the majority of possible cases and create strong predictions.

The results of the VIF - test shows that the values are appropriate, under 10. Hence, there is no multicollinearity in this regression model.

The formula for the regression model of relational capital:

,

Table 12 Regression Model (Relational capital)

Variable	Beta	Std. Error	t value	P - value
(Intercept)	-3,929	0,267500	-14.689	0.000***
NPS (customer loyalty)	0,052840	0,005903	8.952	0.000***
Total share of marketing costs	0,004173	0,006293	0.663	0.511
Website quality	0,032470	0,082730	0.392	0.697
Number of employees

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