Intellectual capital practices:a four-region comparative study

George TovstigaHenley Management College, Henley-on-Thames, UK, and

Ekaterina TulugurovaSt Petersburg State Polytechnical University, St Petersburg, Russian Federation

Abstract

Purpose – This paper aims to present the findings of an empirical study that examines and comparesthe competitive impact of intellectual capital on enterprise performance in small innovative enterprises(SIEs) situated in four geographical regions: St Petersburg in the Russian Federation, the Black Forestregion of Germany, the “Medicon Valley”, situated between Copenhagen in Denmark and Malmo inSweden, and Silicon Valley in the USA.

Design/methodology/approach – The research seeks to investigate potential differences inintellectual capital practices across the four regions while comparing the impact of these relative to thefirm’s external factors (socio-political, technological, and economic) for enterprise performance. To thatend, the research reported builds on and extends earlier research focused on Russian SIEs.

Findings – The research findings suggest that intellectual capital practices and their impact onenterprise performance are more similar than different across the regions studied, and that the firms’intellectual capital constitutes the more important determinant of enterprise performance relative toexternal factors. This does not preclude differences in intellectual capital practices between the regionsstudied. These differences are marginal, however, and reflect socio-economic and national culturalfactors unique to those regions investigated.

Research limitations/implications – The key questions of the research address the relativeimpact of intellectual capital practices (internal factors) and prevailing socio-political, economic, andtechnological factors on the performance of small innovative enterprises. The work presents anextended sample size of Russian companies (also from St Petersburg) and an extension of the earlierstudy to include the three additional geographic regions. The notion of the knowledge-based theory ofthe firm forms the conceptual basis of the framework that is developed to relate intellectual capital(human capital and structural capital) and key external factors (socio-political, economic andtechnological ) to enterprise performance in SIEs.

Practical implications – The study compares the perceived importance of intellectual capital andexternal environmental factors for enterprise competitiveness of companies in the four regions –Russia, Germany, Denmark, and the USA. The research provides evidence that intellectual capital isperceived to be the most important factor driving competitive performance in all the regions.

Originality/value – Similar to the research reported in an earlier paper by the same authors, thepaper represents work in progress. As such, the outcomes and conclusions reported should be viewedas preliminary findings.

Keywords Intellectual capital, Innovation, Small enterprises, Human capital

Paper type Research paper

1. IntroductionThe premise of this paper is that the firm’s internal resource base – and foremost itsintellectual capital – is a determining factor of competitive performance in smallinnovative enterprises. Intellectual capital manifests itself in a variety of forms.Common to all forms of intellectual capital are that they are largely intangible (Bontis,

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Journal of Intellectual CapitalVol. 10 No. 1, 2009pp. 70-80q Emerald Group Publishing Limited1469-1930DOI 10.1108/14691930910922905

1999). The enterprise’s strategic capabilities, for example, are a form of intellectualcapital. Capabilities per se can be thought of as a manifestation of the enterprise’sknowledge (Birchall and Tovstiga, 2005). As strategic resources they enable theenterprise to differentiate itself competitively in its markets; competitive advantage isachieved by those firms that succeed in mobilising their intellectual assets in the formof knowledge, technological skills and experience, and strategic capabilities.

In addition to the questions raised in our earlier work (Tovstiga and Tulugurova,2007), this paper explores how enterprises in different geographical regions of hightechnological activity compare with respect to their intellectual capital exploitation andinnovation capability.

2. Intellectual capital and small innovative enterprises (SIEs)The conceptual framework used in this work draws on the knowledge-based view ofthe enterprise. Intellectual capital, unlike numerous external factors such as theenterprise’s socio-political or economic environment, represents a set of internal factorsthat the small innovative enterprise can influence directly (Tovstiga et al., 2004). Theenterprise can be viewed to be a dynamic repository of knowledge. Its capabilities are amanifestation of its strategically relevant knowledge. Dynamic and continualconversion and recombination of the various forms of the enterprise’s knowledge leadsto the creation of new knowledge and this contributes to the enterprise’s ability todifferentiate itself competitively in their markets (Nonaka and Takeuchi, 1995).

Although it is very difficult to accurately measure and evaluate intellectual capital,its competitive impact of the firm’s intellectual capital remains undisputed. Stewart(1997) defines intellectual capital as the firm’s intellectual material – its knowledge,information, intellectual property and experience base that can be put to use to createwealth. For our research, we assume that the firm’s intellectual capital constitutes theinternal determinant of competitiveness in the SIE. It is broadly broken down into twosub-categories – human capital and structural capital. Human capital embodiescompetence, attitude and intellectual agility. Structural capital captures theinfrastructural factors that constitute the firm’s intellectual capital as represented byrelationships, organisation, and renewal and development.

Numerous factors external to the firm conceivably have an impact on the firm’scompetitive position. For this research, we have restricted ourselves to three, i.e.socio-political, technological and economic factors. The conceptual framework isshown as a causal map in Figure 1.

2.1 Conceptual framework and underlying hypothesesWith reference to Figure 1, the internal factors are represented by human capital andstructural capital.

2.1.1 Human capital. This subcategory is further subdivided into three contributingcomponents, i.e. competence (represented by capabilities, which are taken to bemanifestations of the firm’s strategically relevant knowledge and its skill sets),attitudinal (the behavioural dimension of people-embodied knowledge includingmotivation and mindset), and intellectual agility (encompassing the firm’spredisposition to move quickly and flexibly, to imitate and to adapt in the face ofchanging competitive environments).

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2.1.2 Structural capital. This subcategory relates to the infrastructural dimensionsof the firm’s intellectual capital. These include the firm’s network relationships(relational ), its organisational mechanisms (both hard- and soft-wired) contributing tovalue creation (processes, structure and culture), and the mechanisms the firm employsto sustain these (renewal and development).

Factors external to the firm are represented by the relevant socio-political,technological and economic factors in the firm’s competitive environment.

The internal and external factors constitute sub-factors in the construct.Collectively, they are assumed to be the determinants of enterprise performance.Enterprise performance in turn is expressed in terms of a set of direct measures ofperformance ( performance outcomes) and a comparative set of performance measures(comparative competitiveness). The conceptual framework (Figure 1) shows how theindependent factors, human and structural capital, and the external factors relate to thetwo dependent measures of enterprise performance. The objective of this research wasto determine the relative importance of the internal and external factors with respect toenterprise performance. The underlying hypothesis was that the firm’s internal factorsare more important than its external factors for the firm’s competitive performance.

3. Research designThe survey instrument developed for the empirical field research consists of aquestionnaire that was formulated in English (and used in this form in the regionsDenmark/Sweden, Germany and the USA) and in the Russian translation version forthe field research in St Petersburg. The survey instrument consists of a total of 62items; a five-point Likert-type scale (strongly agree to strongly disagree) was employedfor the scales relating to intellectual capital, external factors and performanceoutcomes. Comparative competitiveness was measured on a ten-point scale.

Figure 1.Conceptual frameworkshowing internal andexternal factors, andenterprise performance

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The survey was set up in the form of an e-survey whereby respondents were invitedto access and respond to the survey via an electronic link. No exact numbers ofresponse rates are available since the invitations to participate in the study weredistributed in various formats – including notices in electronic newsletters in anumber of networked communities.

4. Research analyses and discussion of research findings4.1 Exploratory factor analysisTable I summarises the outcome of the statistical analysis for the Russian data set(updated from our earlier research reported with additional data). Table I is shown as arepresentation of the analysis that was carried out in a similar manner for the threeother regions. The analysis was carried out using SPSS for the MS Windows platform.

4.2 ReliabilityThe results for the reliability test (Cronbach’s a) indicate a very high degree ofconsistency for each of the factors. We attribute this high degree of reliability to thefact that one of the authors (ET) personally administered the survey questionnaire withthe responding participants in their native language.

4.3 Pearson correlationAs would be expected, we see relative high association between the various intellectualcapital factors, such as between human capital and structural capital (case in point: thestrong linkage between the attitudinal dimension of human capital (HC2) and therenewal and development dimension of structural capital (SC3)). We attribute this to thefact that intellectual capital integrates a complex combination of organisation culture,knowledge, innovation and organisation mechanisms relating to learning and renewal.Often these are inextricably linked.

4.4 Regression analysisThe regression analysis is a much more direct measure of impact of one variable onanother. Table II summarises the regression analyses carried out on the data gatheredfrom the extended Russian SIE sample. In keeping with the high degree of reliabilityindicated by the Cronbach’s a values, the analysis shows very high values of thecorrelation coefficient, R 2. From this analysis, we find that the internal,intellectual-capital related factors predominantly determine enterprise, as capturedby the direct performance measure EP1. The outcomes for EP2 are not as obvious. Forthis outcome we find the strongest dependence between human capital (HC1:competence) and enterprise performance. We suspect that the more highly subjectivenature of the comparative measures of performance, EP2, leaves a greater margin foruncertainty and hence variability in the data. From the analysis of the averagedfactors, we find the earlier (Russian) findings substantiated; internal factors relating tothe firms’ human and structural capital are the most important determinants ofenterprise performance.

4.5 Principal component analysisThis was carried out on the data, pooled and for each individual region. This analysisidentifies individual factors that contribute and coalesce to form higher-level factors

Intellectualcapital practices

73

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(principal components) that in turn better explain the variance observed in the datasample.

4.6 Sample descriptionThe research draws on the sampling of enterprises shown in Table III.

4.7 Comparative analyses4.7.1 Correlation analysis. Table IV summarises the extended findings of thefour-region comparative study. Table IV shows only the Pearson correlation valuesthat are highly significant (at least two-star). The purpose of this representation is firstand foremost to show the pattern of correlations. We find strong correlation clusteringin the factors linking the intellectual capital factors for all regions; not surprisingly,these are found to exist between human capital and structural on various levels.

The US Silicon data is unique in that it suggests a significant correlation (albeitnegative) between EF2 (technology as external factor) and IC factors HC2 (attitudinal),

Region Description Sample size

St Petersburg, Russia Small technology-intensive enterprises foundedpost-Perestroika (1990 and onwards); mostly in thescientific devices/equipment manufacturing sectorand IT-related industry. A number of these arealready actively involved in cross-border businessactivities; for most, however, internationalisation is astrongly pursued aspiration 42

Medicon Valley, Denmark Highest concentration of life sciences firms in theworld. The region counts 95 biotech, 125 medicaltechnical, 71 pharmaceutical firms, 25 venturecapital firms, 26 hospitals, 11 universities –employing a total of 340,000 employees and 125,000students 22

Black Forest, Germany This region typifies the German SME sector,employing 70.2 per cent of all employees acrossGermany’s private business sector. Very successfuland strongly export-active, this sector has broughtforth a number of so-called “hidden champions”.They are often in family- or closely-held ownership 40

Silicon Valley, USA Southern part of the San Francisco Bay area, thisleading high-tech hub has drawn on its concentrationof engineering capability, education and venturecapital to establish its leading position in spawningnew technology-based business over the past fivedecades 18

Table III.Description of regions

and enterprises

Dependent Factors Adjusted R 2Standard errorof the estimate Significance

EP1: performance outcomes SC3 0.367 0.422 0.000EP2: comparative performance HC1 0.311 1.365 0.000

Table II.Regression analysis: all

factors considered (again,for the extended Russian

enterprise sample)

Intellectualcapital practices

75

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Table IV.Comparison of Pearsoncorrelation outcomes forall four regions

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SC2 (organisational), and SC3 (renewal and development). In the US analysis we alsofind a correlation between EF2 and EP1 (enterprise performance). This suggests a highlevel of awareness regarding dependence on the external environment for impulses fornew technology development, significantly stronger so than in the other three regions.

The German sample suggests significantly less uncertainty with respect tocorrelations relating to comparative enterprise performance assessment (EP2). Perhapsthis reflects their advanced firm maturity stage relative to the other regions.

4.7.2 Regression analysis. Table V summarises the regression analyses of theregional clusters. We find a high degree of consistency across all regions. Here again, notsurprisingly – since we are focusing on technology-intensive enterprises, we find a highlevel of dependence of enterprise performance on SC3 (renewal and development), withthe exception being Denmark, which exhibits a strong dependence on HC2 (attitudinal).

To account for the variations in dependence on the differing forms of intellectualcapital across the regions, we propose examining external factors unique to the specificregions studied. For example, we find renewal and development and competenceappearing for the Russian enterprise sample. Russia has traditionally had a stronglegacy in scientific and technological excellence, even in the Soviet era. Employees inthe Russian enterprises of the type examined in this study exhibit a high degree ofscientific and technical expertise and professionalism reflecting a high level ofcompetence. Many of the enterprises had their roots in research establishments,reflecting the focus on renewal and development.

Denmark/Sweden, on the other hand, indicate a statistically relevant dependence onthe attitudinal dimension of human capital. Hofstede’s (1991) studies have shown thatDenmark and Sweden exhibit clear traits suggesting low uncertainty avoidance and lowpower distance. He suggests that the village market model – in which neither hierarchynor rules determine what happen, rather demands of the current situation – is mostappropriate for explaining decision-making in this cultural environment. Consensus andharmonisation of collective opinion and interests are important, thereby possiblyexplaining the strong dependence on the strong link to the attitudinal dimension.

Germany represents another interesting case. Here we find a dependence on anexternal factor (technological ) coupled with intellectual agility. The German enterprisesstudied are all from the German Mittelstand sector. Traditionally, these firms are in thespecialty machine and equipment manufacturing sector. This sector constitutes theeconomic powerhouse of the German economy; it exports the bulk of its production

Dependent (performance)Independent factors (factor; degree of varianceexplaineda)

Russia EP1 0.370 SC3 (renewal and development)EP2 0.311 HC1 (competence)

Denmark EP1 0.492 HC2 (attitudinal)EP2 –

Germany EP1 0.45 SC3 (renewal and development)EP2 0.271 [HC3 þ EF2] (intellectual agility, technology)

USA EP1 0.569 SC3 (renewal and development)EP2 0.266 SC3 (renewal and development)

Note: aAdjusted R 2; all factors considered exhibited a significance level of 0.000

Table V.Regression analysis:

regional comparison ofcompetitive determinants

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abroad. In fact, of the world’s economies, only Germany and China have increased theirshare of world exports since 2000. Germany, not China, passed the USA to become theworld’s leading exporter of merchandise in 2003. The German Mittelstand is at theheart of this economic growth development. In 2006 German companies exported $1.11trillion worth of merchandise versus America’s $1.04 trillion. Much of this is going tofast-growth regions such as China. German economist Hermann Simon has suggestedthat “China may be the world’s leading factory, but German companies are building it”(Theil, 2008).

It has been suggested that while Germany may not be at the forefront ofuniversity-based research, German companies nonetheless are masters at taking thehigh technology others have developed and finding innovative ways to embed it intheir products. We believe that the heightened awareness and sensitivity toward new,innovative technological developments in the German firms’ environment is what weare picking up in our research finding; that is, we find the technological external factor,EF2, coupled with an internal human capital factor, (HC3: intellectual agility). Thiswould suggest not only an emphasis on continual scanning for new technologicaldevelopments in the external environments in the German firms, but also an emphasison developing an internal capability for capturing and exploiting these newtechnological opportunities for commercial purposes.

Finally, the USA results in Table V suggest a strong dependence on renewal anddevelopment; this is not surprising, since the firms studied in the Silicon Valley sampleall had strong research and development linkages and traditions.

The principal component analysis (summarised in Table VI) largely confirms thefindings of the earlier analyses: Enterprise performance is determined primarily by theenterprise’s intellectual capital. This is evident from both the pooled data across allregions as well as from a comparison of the analysis of the individual regions, whichshows clear clustering of the intellectual capital factors and the external factors,whereby the analysis of this analysis suggest that the firms’ intellectual capital factorsexplain most of the variance in the case of all regions. The pooled results show thismost clearly: the first factor in this analysis captures all of the six intellectual capitalfactors; the second factor all of the external factors. The regional analyses indicatevarious clusterings of intellectual capital and performance measures, though only inthe case of the Russian sample do we find a link between an external factor (EF1:socio-political) and the comparative measure of performance, EP2. Individual factorloadings largely support the arguments we developed in the previous section regardingmarginal differences in intellectual capital practices across the four regions.

5. Summary, conclusions and outlookAs was the case with the research reported in our earlier paper (Tovstiga andTulugurova, 2007) this paper represents work in progress. As such, the outcomes andconclusions reported in this paper should be viewed as preliminary findings. On thebasis of the analysis of the research date, however, we may summarise and concludethe following:

. The study compares the perceived importance of intellectual capital and externalenvironmental factors for enterprise competitiveness of companies in the fourregions – Russia, Germany, Denmark and the USA. The research provides

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evidence that intellectual capital is perceived to be the most important factordriving competitive performance in all regions.

. Comparisons of the four regions show very high levels of consistency concerningthe importance of intellectual capital for their enterprise performance. This is tobe expected, given that companies in all regions are predominantly intechnology-intensive sectors – despite differences in the role that technologyplays in that particular region and sector: (specialty equipment manufacturing,life sciences, ICT), maturity (largely start-up to maturing), and environmentalconditions (transitional economy versus developed, market economies).

. Nonetheless, the various analyses, in particular the regression analysis and theprincipal component analysis, highlight differences in the impact of intellectualcapital practices between regions and sectors. We attribute these to uniquefeatures of the four regions, such as legacy elements pertaining to the earliereconomic periods (as in the case of the Russian firms), the current globalcompetitive environment (as in the case of the German Mittelstand firms), or evennational cultural attributes and attitudes, such as in the case of the Danish andSwedish firms.

References

Birchall, D.W. and Tovstiga, G. (2005), Strategic Capabilities for Competitive Advantage –Leading through Technological Innovation, Palgrave Macmillan, Basingstoke.

Region/country Component Individual factors and loadingsb,c

Varianceexplained(per cent)d KMOe,f

All regions (pooled) 1 HC1: 0.676; HC2: 0.866; HC3: 0.744; SC1: 0.774;SC2: 0.862; SC3: 0.864; EP1: 0.762

42.3 0.880

2 EF1: 0.684; EF2: 0.803; EF3: 0.806 17.7Germany 1 HC1: 0.774; HC2: 0.839; HC3: 0.800; SC1: 0.798:

SC2: 0.804; SC3: 0.863; EP1: 0.775; EP2: 0.51844.5 0.779

2 EF2: 0.865; EF3: 0.843 16.33 EF1: 0.910 11.4

Russia 1 HC2: 0.865; HC3: 0.827; SC1: 0.757; SC2: 0.888;SC3: 0.800; EP1: 0.604

37.9 0.736

2 HC1: 0.561; EF2: 0.823; EF3: 0.610 16.63 EF1: 0.811; EP2: 0.530 13.0

Denmark 1 HC1: 0.849; HC2: 0.776; HC3: 0.860; SC1: 0.651;SC2: 0.884; SC3: 0.861

39.1 0.664

2 EF1: 0.703; EF2: 0.829; EF3: 0.890 21.03 EP1: 0.829; EP2: 0.941 20.3

USA 1 HC1: 0.677; HC2: 0.650; HC3: 0.804; SC1: 0.796;SC2: 0.726; SC3: 0.544

29.6 0.555

2 SC3: 0.654; EP1: 0.727; EP2: 0.889 22.33 EF1: 0.838; EF2: 0.668; EF3: 0.779 19.9

Notes: aBased on means. bExtraction method: principal component analysis. cRotation method:Varimax with Kaiser normalisation. dRotation sums of squared loadings. eKaiser-Meyer-Olkinmeasure of sampling adequacy. fSignificance based on Bartlett’s test of sphericity: 0.000 in all cases

Table VI.Factor analysis: pooledand individual regionsa

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Bontis, N. (1999), “Managing organizational knowledge by diagnosing intellectual capital:framing and advancing the state of the field”, International Journal of TechnologyManagement, Vol. 18 Nos 5-8, pp. 433-62.

Hofstede, G. (1991), Cultures and Organisations, McGraw-Hill, Maidenhead.

Nonaka, I. and Takeuchi, H. (1995), The Knowledge-Creating Company: How Japanese CompaniesCreate the Dynamics of Innovation, Oxford University Press, Oxford.

Stewart, T.A. (1997), Intellectual Capital, Nicholas Brealey Publishing, London.

Theil, S. (2008), “The factory of factories”, Newsweek, January 21, pp. 48-51.

Tovstiga, G. and Tulugurova, E. (2007), “Intellectual capital practices, external factors andenterprise performance in Russian small innovative enterprises”, Journal of IntellectualCapital, Vol. 8 No. 4, pp. 695-707.

Tovstiga, G., Den Hamer, P., Popova, V.A., Efimov, I.P., Moskalev, S.V. and Bortnik, I.M. (2004),“Preparing Russian small innovative enterprises for international competitiveness:a scoping study”, Journal of International Entrepreneurship, Vol. 2, pp. 89-108.

Further reading

Birchall, D.W. and Tovstiga, G. (2001), “Assessing the firm’s strategic knowledge portfolio:a framework and methodology”, International Journal of Technology Management, Vol. 24No. 4, pp. 419-34.

Tovstiga, G. (1999), “Profiling the knowledge worker in the knowledge-intensive organization:emerging roles”, International Journal of Technology Management, Vol. 18 Nos 5-8,pp. 731-44.

Corresponding authorGeorge Tovstiga can be contacted at: george.tovstiga@henleymc.ac.uk

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