IC-based value creation process of firms: cluster approach Grigorii Teplykh Marina Oskolkova

1. IC-based value creation process of firms: cluster approach Grigorii Teplykh Marina Oskolkova The research is carried out in the framework of "Science Foundation HSE" program, grant № 13-05-0021

2. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Motivation for study • A lot of studies is devoted to analysis of Intellectual capital transformation to corporate value. • Researchers underline the importance of an analysis of separate firm. • Each company has own model of transformation of intangible assets into performance. • However an individual specific of firms is usually ignored or simplified. • This study is an attempt to look more deeply into heterogeneity of value creation process and reveal dissimilarities between different kinds of firms.

3. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Contradiction • Process of corporate value creation is very individual • Econometric analysis of value creation is based on sufficiently large set of firms Vs. Any empirical investigation is a compromise – we analyse common properties of large set of very heterogeneous firms

4. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Fight withHeterogeneity • There are several ways to treat with firm heterogeneity in studies of the IC topic: • imposing restrictions on the sample to ensure needed level of firms homogeneity; • introducing companies special features into regression equation as control variables; • dividing data into the sub-samplesand analyzing each of them separately.

5. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Imposing restrictions • Companies of one industry, country, firms of comparable size etc. • Simplest way to make comparable sample • Problem of choosing adequate restrictions • Differences in sample restrictions employed in different research make their results incomparable • Chosen restrictions may lead to substantial shortening of the sample • There could be sample unrepresentativeness and impossibility to spread obtained results to companies beyond the sample

6. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Control variables • Including control variables (size, ownership, industry dummies) into value creation model • Assumption that factors influence the performance indicator as shift • The approach doesn’t imply hard sample restriction. • Strong assumption about independence between control variables and other tangible and intangible factors that are included into the value model. • The correct model that reflects interaction between independent value factors should be chosen. If their interaction is ignored then regression estimation is going to be biased and therefore distorts understanding of corporate growth drivers.

7. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Sub-samples • Dividing of a sample into a more homogeneous sub-samples and analyze the value creation process in each of them separately • Unusual way (Youndt et al., 2004; Cheng, 2004 implemented cluster analysis). • Clusterisation should be made on the base of some distinguishing indicators, that may affect the value creation process. So it’s necessary to justify these variables and boundaries of firm’s group.

8. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital The key idea of the study • Current study aims to reveal companies’ clusters and investigate IC-based value creation process for each of them. It is supposed that some factors, such as size and activity of IC implementation influence the process of IC transformation into firm value. • The authors expect to receive different results for different clusters that are tied with importance and significance of particular IC assets: • H1. Size of companies influences value creation model. • H2. Companies’ IC size influences value creation model. • Until the gap is closed researchers will may regularly face with great discrepancy of findings about relationships between IC and corporate value.

9. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Methodology • Cluster analysis (k-mean method) • Grouping of firms into comparable sets • Firm size (market capitalization) • Intangible capital involvement (IA book value) • Factor analysis (PCA) • Accounting integral indices for each group of intellectual assets (human, structural and relational capitals) at the base of proxy’ set • Regression analysis (2SLS) • The endogeneity problem is taken into consideration • Modeling of value creation • For all firms and separately for each cluster • For all periods and two sub-periods apart

10. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Final centers of clusters Final centers of clusters • Cluster 1 – large-size companies with greater involvement of IC; • Cluster 2 – middle-size companies with greater involvement of IC; • Cluster 3 – large-size companies with less involvement of IC; • Cluster 4 – middle-size companies with less involvement of IC.

11. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Indexes for IC assets (loadingsof first principal components)

12. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Model • MCap is market capitalization; • BV is company’s assets book value; • NE is company’s number of employee; • HC is human capital index; • SC is structural capital index; • RC is relational capital index; • DC – dummy variable that has the value “1” if the company operates in Great Britain and “0” otherwise; • DI – dummy for industry (1 if service or 0 if manufacturing) •  A, a1…a7 are regression coefficients; t is a time period; ɛ is error.

13. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Regressions results (all periods)

14. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Regressions results before the crisis (2005-2007)

15. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Regressions results during the crisis (2008-2009)

16. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Results • Importance of tangible resources (labour and capital) declined and the significance of intellectual resources (HC, SC, RC) increased during the crisis – for all the clusters • Different value creation model for each cluster either before and after the 2008 crisis • Different response for each groups of firms to the 2008 crisis • These conclusions are just visual. We have not yet tested models for structural shits.

17. National Research University Higher School of Economics - Perm 2nd International Summer School on Intellectual Capital Conclusions • We proposed a cluster approach to analysis of firm heterogeneity in value creation model • We revealed that firm heterogeneity is more important that it was assumed earlier • We found the value creation depends on such factors as firm size and amount of intangibles. • They impact on effects of value drivers (both tangible and intellectual assets) • They impact on change of effects of tangible and intellectual assets during the crisis