The influence of intellectual capital factors on innovation

Investigation of intellectual capital in small and medium-sized enterprises in the conditions of the emerging market. The role of innovation potential in the activities of little and average-sized concerns. Transition to a higher level of introduction.

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By passing international quality certificating process, some Russian companies show their commitment to quality. The purpose is to enhance customer relationships. More than 15% of small and medium enterprises have international quality certificates.

More than 50% of small and medium Russian enterprises continue to introduce significantly improved or new products into the market while almost 40% of them adopt new technologies.

Additionally, on average only 23% of SMEs prefer to establish long-run partnership with colleagues in other region of Russia and less than 10% - partners abroad. It is worth to notice that collaboration with Russian partners aims to research and development process to higher extent in comparison with production process. To expand on this point, only 2% of Russian SMEs focuses on R&D and production in working processes which were realized in co-operation with foreign partners. intellectual capital market innovation

Since the vast majority of studies prove the fact of presence of correlation between IC elements and the process on innovative development, it needs to be stressed that insufficient rates of intellectual capital factors is related to low percentage of companies that create products and technologies which are new for the global market. According to the measure of frequency, only 1.5% of SMEs indicated that their main product is modern not merely for domestic but also for the world market. Besides, almost 20% assessed their goods as new for Russian market. It is very important to mention that the vast majority of small and medium enterprises in Russia do not consider their products as innovative at all (Table 1).

Table 1 Measure of frequency*

Valid

Frequency

Percent

No innovation

719

52.8

1

184

13.5

2

179

13.1

3

259

19.0

4

21

1.5

Number of observations

1362

100.0

*Constructed based on the source: RUFIGE database (2013)

2.4 Data analysis

This part of the study gives special emphasis on the analysis of the data. In our research paper we apply an innovative approach to measure the influence of intellectual capital factors on the probability of transition to the higher level of innovative product. Since the key aim of each empirical research is to construct a reliable model and to achieve unbiased estimators, we examine several characteristics: content validity, construct validity, criterion validity and reliability.

An analysis of the data is conducted via SPSS software. The initial study of each research consists of comprehensive analysis of the dataset in order to reveal whether it is representative or not; are there any outliers and mistakes.

In order to be sure that our results are of practical usage, we have to test the representativeness of the sample and to make clear that it reflects the general population for Russian companies of manufacturing industry. Following this purpose, national statistics agency Rosstat is used. There were overall 202633 of small and medium enterprises in 2012. In 2013 the pattern did not change greatly. That means that the sample covers 0.7% of general population. Confidence interval (95%) and margin of error (3%) which were set manually as the most frequently used, require 1062 as the minimum number of observations. Thus, we can be sure that the sample is representative and future findings may be applied to the general population of Russian SMEs of manufacturing industry [64].

Identification of outliers and its exclusion is an important step which enables to make the sample more homogeneous and helps to escape from estimation bias (Wooldridge, 2012). The existence of outliers is studied by means of boxplot analysis for continues variables, frequency tables and descriptive statistics measures. As a consequence, the variable related to the training courses is restricted by 50% as the highest value, and to the salary of unskilled workers - by 4 (Appendix 4). Overall, we have 1362 observations. Furthermore, according to the Kolmogorov-Smirnov test, the probabilities for each of continue variables are less than significance level (0.01). Thus, we may conclude that since H0 hypothesis about normal distribution is failed to accept, all variables are non-normally distributed. We cannot use linear regression for further research.

After the representativeness of the sample has been proved and outliers have been eliminated, the dataset can be estimated in accordance with general econometrical characteristics: content validity, construct validity, criterion validity, and reliability.

Content validity. Content validity describes how well selected elements fit the conceptual domain of interest. There are several ways to indicate whether the data has content validity or not. Literature review and key experts are among most used (Mastaglia et al., 2003). Since all items were selected in accordance with previous international literature, we may assume that the model has content validity. We appreciate to have sufficiently high explanatory power.

Reliability. Reliability reflects whether the results obtained are repeatable or not. The model is anticipated to have the same data which produce valid and reliable scores (Hammersley, 1987).

The preliminary analysis for model construction includes estimation of the reliability of the instruments and measurement of internal consistency. Following this purpose, Cronbach's alpha is implemented. The assessment of this coefficient is based on calculations of average correlations between the variables related to each element of the explanatory factors, from a single administration of the presented questionnaire (Cronbach, 1951). According to Nunnally et al. (1994), the internal consistency is observed when Cronbach's alpha coefficient is greater than 0.6. Following this rule, the analysis reveals that maximization of the Cronbach's alpha would require an exclusion of some variables from each category of intellectual capital (Table 2). Later on all variables selected by Cronbach's alpha coefficient will be assessed by factor analysis which is based on loading values.

Table 2 Cronbach's alpha coefficients

Type of IC

Number of indicators

Cronbach's alpha

HC

6

0.637

SC

6

0.543

RC

7

0.659

Table 2 presents the results according to which structural capital is the only type of intellectual capital which does not reach required levels of reliability. Nevertheless, the minimum criterion (>0.5) for measuring an internal consistency under early evaluation has been met (Wagner, 1995). Besides, some studies argue that there are no reasons to worry since low Cronbach's alpha can be explained by some characteristics of the sample (Crocker et al., 2008). For example, high proportion of missing values.

Construct validity. Factor analysis through principal component analysis is employed for each of three constructs for the purpose of evaluating the construct validity. The main target of this econometrical method is to reduce the number of variables which have been chosen relying on the previous literature and to define the key components for each type of intellectual capital. We perform factor analysis through principal component analysis with oblique (0-oblimin) or varimax rotation. In order to identify which type of rotation should be applied we have to conclude if the factors are believed to be correlated or not. In other words, in contrast to varimax rotation, oblimin rotation allows the components to have non-zero correlation. In our case we use varimax rotation for identifying the components of structural and human capital, meanwhile oblimin rotation is employed with the aim to obtain factors of relational capital (Appendix 5). Factor loadings reveal how each hidden factor is connected with observable variables in the analysis. According to the results which were obtained, since factor loading values are greater than 0.4, we may confirm that all independent items are valid. It is worth to notice that loading values of variables related to relational capital have maximum positive signs.

As mentioned above, factor analysis enables the researcher to test the validity of the technique. An integral part of factor analysis is the Barlett's test and the coefficient of Kaiser-Meyer and Okin (KMO). These are two auxiliary instruments which give the degree of validity of the employed technique and accept the sample adequacy. Table 3 indicates the KMO and Barlett's test results. Both Barlett's test and the Kaiser-Meyer and Okin test reveal that there is a reasonable correlation between the items. Following the purpose to obtain valid results, the value of Kaiser-Meyer test should be not less than 0.6. Moreover, the significance of p-value for each component is 0.000. By comparison with the 1% significance level, we may conclude that null hypothesis about identity of the correlation matrix is rejected. The null hypothesis of Barlett's test suggests that the correlation matrix is an identity matrix in which all diagonal values equal one, while all off the diagonal elements are zero. According to p-value<0.01, null hypothesis is rejected. Thus, the statement about an existence of correlation between some variables is approved.

Criterion validity. This type of validity is also known as external validity, because it assesses the predictive ability of the model. Discrete choice model, in particular ordinal logistic regression, is used to assess the criterion validity. The main objective of this regression is to model ordered responses as functions of explanatory variables.

The function for individual i choosing an alternative j:

,

where: M is the number of categories; X is a vector of explanatory variables; the coefficients á (scalar), â are parameters to be estimated; e is the error term captures all unobservable factors that are able to affect the decision.

Table 3 Construct analysis

Constructs

Cumulative explanatory var (%)

KMO

Barlett's test

HC

Salary costs (investments into employees)

60.216

0.717

Chi2=1858.188

Sig.=0.000

Quality of the labor force

SC

Investments into innovation developments

51.215

0.627

Chi2=372.631

Sig.=0.000

Measures undertaken for quality improvements

RC

Strategic partnership with Russian companies

56.634

0.699

Chi2=1815.130

Sig.=0.000

Strategic partnership with foreign companies

We can present X as a matrix with dimension 1362 (number of observations) by 6 (number of explanatory variables); â as a matrix with dimension 6 (number of explanatory variables) by 4 (number of categories of product novelty).

We can write the logit model as follows:

,

,

,

,

,

where: P is the probability of certain level of product novelty if an individual chooses a particular alternative; âi is the column of â matrix; g is a function.

The function is defined on infinite intervals and takes values in the range [0,1]. We have to define the best estimates of the parameters bj. It is impossible to apply the method of least squares as is done in the case of ordinary linear regression. So we use the maximum likelihood method. MLM gives unbiased, consistent and effective estimates

The log-likelihood function for our model is the following:

where: is an indicator function for individual i choosing j alternative.

Since p-value of measure of error (-2 Log Likelihood) equals 0.000, we may conclude at 1% significance level we reject the null hypothesis. Thus, we assume that this model has an ability to predict the outcome. Moreover, according to the goodness-of-fit criteria, an observed data is consistent with the model fitted to it.

Results

Table 4 contains the estimated coefficients for the model. It should be noted that there are four variables which are statistically significant. The coefficients for all variables have an expected sign consistent with the previous literature. All items influence positively the probability of transition to the higher level of innovation product.

Table 4 Empirical results of regression analysis

Variable

Coefficient

SE

Rate of the salary

-0.105

0.083

Quality of the labor force

0.146*

0.079

Investments into innovation developments

1.682***

0.106

Measures undertaken for quality improvements

0.056

0.086

Strategic partnership with Russian companies

0.243***

0.077

Strategic partnership with foreign companies

0.163**

0.075

Manufacture of food

0.205

0.242

Other city

0.148

0.163

Size

-0.227

0.172

Number of observations

640

Log likelihood

1354.669

Pseudo R2 (McFadden)

0.239

Intercept

1779.048

***p<0.01, **p<0.05, *p<0.1

According to the estimated results, if Russian firm pays much attention to the quality of its employees, does some investments into R&D processes and has strong partnerships with domestic or foreign companies, there will be more chances that its product turns out to be innovative for the whole country or even for the world.

In spite of small scope of SMEs, the result of incorporation of new technology into the business model is indicated in the overlapping of the market needs. As mentioned above, the success of small and medium enterprises depends on realization of market demand. If owners or managers of the local company pursue the aim to expand its market share, to reach competitive advantage and to make its product more innovative, they have to focus on methods which are not acceptable for their rivals. The best way in order to make this come true is to increase R&D expenditures and to develop the product in alliance with partners which are interested in promotion of new product.

According to the findings which were obtained, we conclude that several research hypotheses were validated. Hypotheses H1b, H2a, H3a and H3b are fully supported (Table 5).

Human capital. Monetary rewards give quick response on the innovation. Nevertheless, in accordance with previous international literature, monetary awarding does not considered to be strong motivator for the long period. Incremental and radical types of innovation are more connected with recognition, respect, and other non-monetary incentives.

There is no doubt that quality of the labor force has positive influence on the level of product novelty. Innovation can be stimulated by means of training courses, attractiveness of workers with higher education or those who have already been hired by foreign companies.

Usually managers and owners organize some electives for their employees in order to develop their creativity, and problem-solving skills, improve their flexibility, and ability to adapt to changes. These development programs enable to encourage innovation and creativity. Consequently, to increase the probability that goods and services produced by the company will be new for the broader area. More than that, as for Russia, employees with higher education and foreign experience are associated with highly innovative company. They are regarded as professionals that are eager to share their fresh ideas with peers and to create innovative products.

Table 5 Hypothesis test

Hypotheses

Situation

H1a: An amount of money earned by the employee does not influence the probability of transition to the higher level of product novelty.

Accepted*

H1b: The more qualified employees work in the company, the higher probability of transition to the new level of product novelty.

Accepted

H2a: Investments into innovation development influence positively the probability of transition to the new level of product novelty.

Accepted

H2b: Focus on instruments of quality improvements enables the company to increase the probability of transition to the higher level of innovative product.

Rejected

H3a: The presence of foreign strategic partners enhances the probability to transit to the new level of product novelty.

Accepted

H3b: The presence of domestic strategic partners enhances the probability to transit to the new level of product novelty.

Accepted

*- since variables are statistically insignificant

Structural capital. Two factors have been analyzed within the framework of structural capital: implementation of systems which enable to trace the quality of new products, and procedures related to R&D expenses. Despite the fact that both features are important at the first glance, we have received controversial results.

Instruments of quality improvements do not have significant influence on the probability of transition to the higher level of product novelty. There are several reasons for that fact. Firstly, CRM or ERP systems, international certification process are very expensive for SMEs. These things are more common for large companies which allow spending huge amounts of money on CRM or ERP systems implantation. Secondly, some SMEs in Russia do not regard it as effective tools for enhance of innovation activity, but for strength of customer satisfaction and loyalty. Thirdly, some of such means do not offer satisfying cost-benefit ratio.

Small and medium enterprises are much more likely to spend their resources on research and development, and to finance introduction of significantly improved goods or adoption of new technologies. Direct investments into innovation development are considered to be the fastest way to increase the level of product novelty.

Relational capital. In accordance with the vast majority of research, we have proved that relational capital plays significant role for small and medium enterprises which operate under the conditions of emerging market. Either manufacturing or R&D activities which are realized jointly with strategic partners stimulate innovation activity. The network of Russian managers comprises people of different mindset and knowledge that help them to acquire and generate unique, diverse knowledge and opportunities. As a consequence, it helps companies to go beyond technological boundaries and fosters the probability to increase the level of product novelty.

Conclusion

In order to stimulate innovation activity some steps and forces should be applied. By and large, the following statement has been proved both theoretically and empirically: intellectual capital can be considered by the owners and managers of enterprises as a tool that can raise the level of development of innovative business in Russia. It is an essential source of SMEs' success which allows to expand the resource base, to manage the resources of the company more effectively, to improve the competence and motivation of staff, to ensure access to information about potential future flows of the company, as well as to enhance the innovative equipment of the company and the level of innovation activity greatly. Depending on its financial performance, strategic goals, and market needs each company has to decide what particular attribute of intellectual capital should be introduced. Managers and owners of SMEs in emerging countries have to remember that they are always constrained by numerous factors which may cause negative consequences and risks. Thus, accomplishment of an analysis of all future aspects of intellectual capital and innovations has become extremely crucial.

Our study promotes to close the gap between the influence of the intellectual capital and innovation. In summary, our study contributes in two subject areas: the literature of intellectual capital, as well as innovative literature. The author offers to implement new significant indicator for the calculation of probability to transit to the higher level of innovative product. Instead of speculating on common elements of innovation it is possible to go further and to predict what will be an outcome after implementation of several intellectual capital factors into the model.

By utilizing of econometrical methods, the results which have been reached by the author of the present paper have yield to the conclusion that not all intangible elements have positive effect on innovational performance of small and medium enterprises. Author pursues the view that human capital is the most essential type of intellectual capital as it provides the company with high educated employees who are ready to generate ideas for product promotion, the results of our investigation show the same position. Although financial incentives remain innovative activity on the same level, high quality of labor force is able to stimulate it. The effect of human capital on product innovation performance has to be pondered very accurately. Truth to be told, investments in research and development, high-tech equipments, programs of innovative development are the things that initially evolves in the minds of a great fraction of managers and owners of SMEs in Russia while speaking about innovative perspective of the company. Only minority of them regards employees as the source of innovative ideas.

More than that, we can generally conclude that as a result of scarcity of financial resources, SMEs are not ready to pay much attention on expensive techniques (CRM, SAP, certificates from international organization) which can stand as a decent proof of high quality of the main product.

The limitations of the present research are needed to be mentioned. Firstly, following the purpose to improve the results, it would be more preferred to rely on longitudinal study. It is necessary to consider the dynamics of utilization of the assets. Secondly, expert survey is the basic method of information collecting. This information is subjective and may give biased results of the model. Thirdly, all selected indicators are presented as proxy variables that can not reflect all the phenomena as a whole. It may lead to biased results and put additional constraints on research opportunities. Finally, as a consequence of large number of missing data, the investigated sample is quite limited.

Further investigation of the theme will be established on the basis of EFIGE data, which investigates the competitiveness of European countries. The main target is to concentrate on the distinctive features in the activity of innovative small and medium enterprises. Hopefully, it will help to give recommendations to managers and owners of Russian companies about efficient control methods over IC components and innovational capabilities.

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Appendices

Appendix 1

Intellectual capital components

Element of HC

Item description

Measurement

References

Rate of the salary

Rate of an average salary of white collars and an average salary in the company

Quantitative (%)

Song et al.,2007

Rate of an average salary of skilled blue collars and an average salary in the company

Quantitative (%)

Rate of an average salary of unskilled blue collars and an average salary in the company

Quantitative (%)

Quality of the labor force

Percentage of participants of training programs

Quantitative (%)

Laursen et al, 2003

Managers' experience of working abroad for at least one year

Nominal: 1(yes); 0(no)

Percentage of employees with higher/academic degree and unfinished higher education

Quantitative (%)

Element of SC

Item description

Measurement

References

Investments into innovation developments

Introduction of a good which is either new or significantly improved

Nominal: 1(yes); 0(no)

Caner et al., 2013

Adoption of a production technology which is either new or significantly improved

Nominal: 1(yes); 0(no)

Presence of R&D investments

Nominal: 1(yes); 0(no)

Measures undertaken for quality improvements

Internal information system of planning and resource management

Nominal: 1(yes); 0(no)

Santos-Rodrigues et al., 2013

Mertinz et al, 2009

System of sales/purchases management (CRM system)

Nominal: 1(yes); 0(no)

The firm has international quality certificates issued by accredited international organization

Nominal: 1(yes); 0(no)

Element of RC

Item description

Measurement

References

Strategic partnership with Russian companies

Strategic partnership with Russian companies in other regions

Nominal: 1(yes); 0(no)

Renzl, 2008

Costa et al, 2014

Working processes which were realized in co-operation with Russian partners/R&D

Nominal: 1(yes); 0(no)

Strategic partnership with Russian companies (in its region)

Nominal: 1(yes); 0(no)

Working processes which were realized in co-operation with Russian partners/ production

Nominal: 1(yes); 0(no)

Strategic partnership with foreign companies

Working processes which were realized in co-operation with foreign partners/R&D

Nominal: 1(yes); 0(no)

Working processes which were realized in co-operation with foreign partners/ production

Nominal: 1(yes); 0(no)

Strategic partnership with foreign companies

Nominal: 1(yes); 0(no)

Element of product innovation

Item description

Measurement

References

Level of novelty of company's product

The company's product was new for the particular market

Ordered :

1 -New for the company only

2-New for the Russian market

3- New for the regional market

4-New for the world market

OECD, 2015

Laursen et al, 2003

Appendix 2

Descriptive statistics

Variable

Mean

Maximum

Minimum

Standard deviation

Number of observations

SMEs

SMEs

SMEs

SMEs

SMEs

HC: Rate of an average salary of white collars and an average salary in the company

0.820

2.670

0.000

0.575

1362

Rate of an average salary of skilled blue collars and an average salary in the company

0.751

2.310

0.000

0.510

1361

Rate of an average salary of unskilled blue collars and an average salary in the company

0.423

1.670

0.000

0.350

1362

Managers' experience of working abroad for at least one year

0.080

1

0

0.265

1338

Percentage of employees with higher/academic degree and unfinished higher education

0.328

1.000

0.000

0.198

1235

Percentage of employees who were the participants of training programs (retraining programs, refresher courses, internship, etc.) with a separation from manufacturing process

0.022

0.50

0.000

0.055

1362

SC: Introduction of a good which is either new or significantly improved

0.520

1

0

0.500

1106

Adoption of a production technology which is either new or significantly improved

0.360

1

0

0.480

1093

Presence of R&D investments

0.190

1

0

0.389

1342

Internal information system of planning and resource management

0.170

1

0

0.378

1248

System of sales/purchases management (CRM system)

0.160

1

0

0.364

1249

International quality certificates issued by accredited international organization

0.180

1

0

0.380

1027

RC: Strategic partnership with Russian companies in other regions

0.230

1

0

0.423

1362

Working processes which were realized in co-operation with Russian partners/R&D

0.190

1

0

0.389

1362

Strategic partnership with Russian companies (in its region)

0.360

1

0

0.481

1362

Working processes which were realized in co-operation with Russian partners/ production

0.140

1

0

0.350

1362

Working processes which were realized in co-operation with foreign partners/R&D

0.020

1

0

0.144

1362

Working processes which were realized in co-operation with foreign partners/ production

0.020

1

0

0.144

1362

Strategic partnership with foreign companies

0.080

1

0

0.273

1362

Appendix 3

Identification of outliers

Appendix 4

Principal component analysis

Human capital

Component*

1

2

Rate of an average salary of white collars and an average salary in the company

0.890

0.060

Rate of an average salary of skilled blue collars and an average salary in the company

0.908

0.003

Rate of an average salary of unskilled blue collars and an average salary in the company

0.860

-0.047

Presence of foreign employees or specialists

0.033

0.509

Managers' experience of working abroad for at least one year

0.086

0.728

Percentage of employees with higher/academic degree and unfinished higher education

-0.119

0.663

*-varimax rotation with Kaiser normalization converged in three interactions

Structural capital

Component*

1

2

Introduction of a good which is either new or significantly improved

0.760

0.175

Adoption of a production technology which is either new or significantly improved

0.774

0.127

Presence of R&D investments

0.634

0.106

Internal information system of planning and resource management

0.090

0.727

System of sales/purchases management (CRM system)

0.051

0.755

The firm has international quality certificates issued by accredited international organization

0.283

0.595

*-oblimin rotation with Kaiser normalization converged in three interactions

Acknowledgment

I would like to express my special thanks to Carlos M. Fernandez-Jardon Fernandez who is professor at the International Laboratory of Intangible-Driven Economy (IDLab) in Perm. His professional skills, experience and in-depth knowledge is a priceless gift for me. Professor was one of those who inspired me to start this study. It seems to me that I would have never got such results without his continuous support.

Abstract

Over the past decade researchers regard both intellectual capital and innovation capability as the principal components which create the competitive advantage of the company. Nevertheless, the relationship between these factors for small and medium enterprises within emerging market has not been subjected to conscious scrutiny. Therefore, Russian managers and owners cannot weigh the benefits and costs of their innovation capability and make decisions related to expanding its activity to the broader markets. Thus, the general purpose of this research is to gain an in depth understanding of interaction effect between intangibles or intellectual capital components and product innovation in Russian SMEs of the manufacturing industry. The author proposes the new explanation of innovation capability. We pursue the aim not just to analyze whether intellectual capital factors affect innovation, but to shed the light on the main factors which have the strongest influence and to assess what can be the predicted probability of transmitting to the higher level of innovation. The author uses logit model regression for cross-sectional data. The findings confirm that intellectual capital may either boost the probability of switching to higher level of innovation or remain it at the same level without any significant changes.

Keywords: intellectual capital; product innovation; emerging market; innovative SMEs; competitive advantage.

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