1. Anna Bykova Elena Shakina NRU HSE - Perm 01.09.2011
Intellectual Capital Evaluation: Relationship between Knowledge Management Implementation and Company’s Performance
Anna Bykova
Elena Shakina
NRU HSE - Perm

2. Key Points of the Presentation
1. Theoretical Framework
2. Research Design
3. Data and Methodology
4. Empirical Results

3. Theoretical Framework Resource-based approach Value-based approach
Intellectual capital involves specific resources of the company, like human capital, marketing capital, intellectual property etc.
Intellectual capital is a source of the value growth, it is expected to enhance tangible assets value
Theoretical
Framework
Resource-based approach
Value-based approach
Roos, Bontis, Ramelt, Dragonetti, Jacobsen etc.
Stewart, Stern, Fernandez, Edvinsson, Malone, Zeghal, Maaloul etc
Transformation of the intellectual capital in company’s vale
Intangibles complementarity and optimal structure

4. Theoretical Framework: IC in the Resource-based Approach
RBA provides:
Intellectual capital is heterogeneous.
We need to split the intellectual capital into components and analyze each of them separately.
An analysis of the intellectual capital structure
An identification of the fundamental characteristics and features of intellectual resources
An identification of the intellectual capital inputs (expressed in proxy indicators) SC RC HC
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5. Research design: Research Framework

6. Research design: Hypotheses
Hypothesis 1
Economic value added, future growth value and Q-Tobin are proxy indicators of intellectual capital outcomes
Hypothesis 2
Intellectual capital inputs can be described by proxy indicators, based on public available information about a company from its annual financial and statistical reports
Hypothesis 3
There are internal (IC components configuration of the particular company and age) and external (country, industry, location) factors that influence transformation of intellectual capital in companies’ performance
Hypothesis 4
There is a complementarity between intellectual capital components that impacts company performance

7. Research design: Indicators
Independent variables: intellectual capital inputs and factors of transformation
Intellectual capital components: structure capital
R&D investments
Intangible assets
Patents, licenses, trademarks
ERP, quality management systems implementation (dummy)
Stable turnover growth
Intellectual capital components: relational capital
Commercial expenses share
Foreign capital employed (dummy)
Presence of subsidiaries
Well-known brand (dummy)
Citations in search engines (categorical 0-8)
Integrate indicator of the site quality (categorical 1-4)
Intellectual capital components: human capital
Share of wages in costs
Earnings per employee - proxy indicator of human capital quality
Board of directors qualification (categorical 0-2)
Corporate university (dummy)
Common information
Age - years of presence on the market
Belonging to industry (dummy) - manufacture membership
Belonging to country (dummy) - Germany membership
Belonging to developed country (dummy)
Dependent variables: intellectual capital outcomes
EVA
FGV
Q-Tobin’s

8. Research Design: Core Econometric Specification
Perf = α + (β1, ..., βn) HC + (δ1, ..., δm) SC + (δ1, ..., δl) RC + (λ1, ...,λk) Dummy + ε
Perf is an indicator of the performance of companies (EVA; Q-Tobin; FGV as independent variables);
HC is a vector of variables responsible for human capital component;
SC is a vector of variables responsible for structural capital component;
RC is a vector of variables responsible for relational capital component;
Dummy is a vector of dummy variables introduced in the analysis.

9. Data and Methodology: Criteria Belonging to the country
According to KEI presented by World Bank (Russia, Serbia, Great Britain, Ukraine, Turkey, Finland, Denmark, and Spain)
Belonging to the country
According to the predominance of varied intellectual capital components (financial services, wholesale and retail trade, machinery and equipment manufacture, chemical, and transport and communications)
Belonging to the industry
no less than 500 and no more than 20,000 people.
Number of employees
A company should refer to the listed company
Belonging to the public companies
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10. Data and Methodology: Sample фото фото
Listed Russian companies (111 companies from 2005 to 2009)
Databases FIRA PRO and SPARK-INTERFAX
Listed European companies (420 companies from 2005 to 2009)
Database Bureau Van Dijk (Ruslana, Amadeus)
Common indicators – form and structure of ownership, company age, industry, location, patents and licenses.
Economic indicators – costs, export, R&D expenditures, capital investments, and working capital
Financial indicators – operating profit, EVA, FGW and Q-Tobin coefficient, etc.
Specific intellectual capital indicators – VAIC
Quantitative data based on the financial statements of the companies
Quality of the relational capital
Quality of the human capital
Quality of the structural capital
Qualitative data based on the information from web-sites, magazines, citation bases, data from patent bureaus, etc
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11. Regression Results for Russian Companies
Empirical Results:
Regression Results for Russian Companies

12. Empirical Results: Results Interpretation for Russian Companies
Negative statistical significant link with
R&D investments(long-term return and high risks in emerging markets)
Positive statistical significant link with
earnings per employee, intangible assets and number of patents, trademarks and licenses.
The explanatory models’ power is
32.5% for the first equation
71.8% for the second one.
Hypotheses 1 and 2 are confirmed.

13. Empirical Results: Regression Results for European Companies

14. Results Interpretation for European Companies
Specific features associated with higher intellectual capital outcomes
industry-(manufacture)
country- (Germany)
IC indicators determine company’s IC configuration:'
Earnings per employee (human capital).
Intangible assets (structural capital).
Well-known brand (relational capital).
Equation are significant on 1% probability level
Hypotheses 1-3 are confirmed
Indicators associated with EVA and FGV positively:
well-known brand, ERP system presence as well as qualification of BD and citations in search engines

15. Research Design: Complementarity for the IC Components - Specification
Perf = α *β1HC* δ1SC * λ1RC * ε
lnPerf = α + β1lnHC + δ1lnSC + λ1lnRC + ε
Perf - EVA; Q- Tobin; FGV;
HC – vector of a human capital component;
SC – vector of a structural capital component;
RC – vector of a relational capital component.

16. Empirical Results: Complementarity for the IC Components - Regression Results

17. Conclusions
H 1
The high explanatory power of EVA and FGV indicators as indicators of the intellectual capital outcomes was confirmed.
Q-Tobin indicator seems to be not so adequate in explaining the transformation of the intellectual capital inputs in the company's value even in developed markets.
H 2
A validity of intellectual capital proxy indicators use was proved.
Many of the selected indicators are obviously interpreted in terms of theory and practice of knowledge management.
H 3
Some significant internal and external factors of the intellectual capital transformation were identified. For instance: company age, country and industry.
Significant differences between developed and developing markets were found out.
The relational and human capital showed higher significance in developed countries, while in Russia the structural characteristics present a growing point in most of corporations.
H 4
Higher complementarity of the intellectual capital components should be noticed.
The combinations of interconnected elements are different for Russia and Europe.