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 01.09.2011

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

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

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 фото фото

Research design: Research Framework

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

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

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.

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 фото фото

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 фото фото

Regression Results for Russian Companies Empirical Results: Regression Results for Russian Companies

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.

Empirical Results: Regression Results for European Companies

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

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.

Empirical Results: Complementarity for the IC Components - Regression Results

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.