Some explanation about computation of core independent variables should be presented. Proxy-variables of decisions concerning the change of investments in intangibles at the time of the recession serve as core independent variables. They represent dummies equaling 1 if growth rate of investments for the corresponding period is positive and 0 otherwise. So that, if real value of investments increases, we assume that a company holds to accumulate intangible capital during a crisis (keeps to a strategy of maintaining or rising the investments).

All growth rates employed in the analysis represent geometric average annual growth rates of the corresponding investments for the denoted period. These notes relate to the core independent variables (changes in investments in intangibles during the crisis 2008-2009), as well as to control variables of historical companies' dynamics of knowledge investments (2004-2007). The method of calculations of geometric average growth rates is presented in the table below.

Table 16 Formulas for computation of growth rates and corresponding dummies employed in the analysis

Table 17 Calculation of control variables employed within the analysis

Regarding the question of performance measurement in the study the author denotes two indicators:

a) Economic Value Added (EVA®) sized by book value of company's assets,

b) Return on Assets (ROA).

These tools selection as outcomes of a company activity in the model implies two kinds of company's profit: economic and accounting. Although an accounting profit reflects money earned by a company, it leaves out of account opportunity costs of company's capital. A concept of economic profit and EVA® as its particular example intend to diminish this flaw of accounting measures.

Other researchers also apply suggested concepts of performance measurement. Tseng, 2008 uses EVA as a financial metric in order to compare effects of traditional production factors and innovation factors on company performance. Tseng, 2008 denotes that EVA reflects modern economic reality more precisely than conventional measures. ROA as a performance measure is used in Sydler et al, 2013 as a proxy for firm profitability when analyzing the model with different kinds of intellectual capital as independent variables.

Performance indicators have different ways of calculation. Particular methods that are used in this study should be observed. Note that all methods of calculation should have complied with requirement of public availability of data.

It should be mentioned that represents metric of profitability of a firm, while is considered to be an indicator of alternative costs of company invested capital. Usage of year (t-1) for invested capital implies that a company in current year operates with capital available at the beginning of year t, or similarly at the end of year (t-1).

When calculating WACC (4) the author made the serious simplification that market values of company's debt and equity equal their book values. This step allowed him to use public available data for the analysis (company's financial reports).

Details about subcomponents of cost of equity and cost of debt formulas (5) and (6) are presented in Appendix G

,

where: - return of company's assets in year t.

At the end of this section the author examines the question of endogeneity.

The broad definition of endogeneity implies correlation between the explanatory variables and the disturbance (the error term) in a regression (Wooldridge, 2009; Roberts and Whited, 2013; Wang, 2010). All researchers pay special attention to solution to this problem, as endogeneity makes regression parameter estimates biased and inconsistent (Wooldridge, 2009; Roberts and Whited, 2013). There are certainly some possible sources of endogeneity: omitted variables, simultaneity, and measurement error (Roberts and Whited, 2013).

Omitted variables represent a source of endogeneity when they are correlated with the included explanatory variables. The problem arises when it is hard to measure an explanatory variable or there are some unobservable factors. This way an error term contains these omitted variables and included independent variables start to correlate with the error term (Roberts and Whited, 2013, Wang, 2010].

Simultaneity occurs when one or more of the independent variables is jointly determined with the dependent variable, for example through an equilibrium mechanism (Wooldridge, 2009).

Measurement errors appear when proxies are used within a model. This does not happen because of mistakes of data collectors, but conceptual differences between proxies and their unobservable parts (Roberts and Whited, 2013). This makes measurement errors a part of a regression error. Roberts and Whited, 2013 state that biases in this case are rather complicated and that they can be even severe.

Any endogeneity problem solution method contributes to unbiased and consistent estimates and, moreover, reliable inferences. However, we need to bear in mind that it is impossible to empirically test the correlation between a variable and an error term, because an error is an unobservable part of a model. This means that we cannot confirm the absence of endogeneity ultimately. What is more, we should assume the presence of some bias in all cases, because an estimate is a fixed number, obtained from a particular sample and estimates can be unbiased only when the analysis is applied across all possible random samples (Wooldridge, 2009), which is impossible concerning restricted data available for a researcher.

Consequences of endogeneity problem existence in the present study is limited. Research design of the thesis implies that investment decisions of previous years have an effect on current indicators. This way possibility of reverse causality in the study is eliminated. What is more, the analysis does not enable omitted variables, as a set of control variables are applied. Stability of coefficients in all estimated models serves as an indirect evidence of the absence of endogeneity problem.

4. Results

Influence of investment decisions towards intangible capital in 2008-2009 on company performance in recovery period of 2010-2013 was observed. Empirical results of the model estimation is presented below. Note that all necessary procedures of regression analysis were conducted. The problem of multicollinearity is avoided.

Table 18 Estimation results of hypotheses testing

Four versions of the model (A-D) were tested. EVA and ROA were used as dependent variables in order to examine two main metrics representing economic and accounting approaches to firm performance. The author applies dummy variables embodying investment decisions at the time of a crisis. In addition to dummy variables, average growth rates of investments at a recession time as core independent variables were used.

Core independent variables of investments in intangibles in crisis as dummy variables and growth rates were applied in the analysis in aggregate form (for the whole period of recession 2008-2009, Table 18) and in annual form (for every year of the crisis, Appendix F).

The following results were obtained. Hypothesis 1 can be rejected partly. Investment decisions towards R&D in 2007-2008 was recorded to have positive influence on EVA (Appendix F, models E-L). Similar to this, investment decision to accumulate innovation capital was advantageous for an average company in 2009-2010 (model F). Note that a period of 2009-2010 is also examined, because investment budgets for the next year are usually approved in the previous year.

Concerning ROA as a performance metric, positive effect of innovation capital accumulation in 2007-2008, 2008-2009 and 2009-2010 on profitability was found in models L, L and K, and J correspondingly.

However, average annual growth rates of investments in R&D for the period of the crisis had insignificant impact on company performance 2010-2013 (both on EVA and ROA, see table 18).

Hypothesis 2 can be accepted. An average increase in employee costs in 2008-2009 (dummy variable) proved to cause positive influence on EVA and ROA in 2010-2013. Although a case of a rise of investments in employees in crisis is significant, the growth rate of such increase does not matter (models B and D relative to A and C).

Concerning a particular year analysis of investments in human capital shows that 2008-2009 company decision to accumulate such investments had a positive influence on accounting metric ROA in 2010-2013 (models I and L), while had no impact on EVA. Human capital accumulation in 2009-2010 contributed to both EVA and ROA in 2010-2013 (models F, H, J and L).

Comparing empirical results of the analysis with results of previous studies the author found some similarity. Henry, 2013; Holtskoga and Ringenb, 2013 and Sheehan, 2012 insisted on positive influence of intangible accumulation at the time of crisis, which found confirmation in the thesis.

In the literature there are more evidence of positive effects of human capital accumulation than of R&D in tough economic conditions. As for the empirical results of the thesis, they confirm relative advantage of employee costs when we take into account average investment policy for the whole crisis period. However, when we go deeper and observe investment decisions in each year an increase in R&D expenditures at the time of crisis may be also advantageous in terms of future profitability.

A set of control variables, which proved to have a significant influence on company performance, should be mentioned. Among others the variable of history of investments in human capital (average growth rate for the period 2004-2007) has a negative significant coefficient. This means that companies increasing employee costs at the time of prosperity 2004-2007 tended to demonstrate worse results in 2010-2013 than other companies did. Such kind of relationships may relate to overinvestment in human capital during the period 2004-2007. Furthermore, intensive investments in employees during prosperity time may become counterproductive in a long run, as opportunity costs of such investments are high in economic upswing.

Factors of risk and related with it concept of yield measured by sales growth for the last 3 years and financial leverage proved to have negative influence on economic profit EVA and positive impact on profitability ROA. The reason for the difference in signs may lie in nature of economic profit. EVA considers alternative costs of invested capital that correlate positively with risks of a company. Alternative costs are subtracted when EVA is calculated, consequently increasing risks may worsen EVA value. Meanwhile risks may support short-term profitability of a company, so that stimulate ROA increase.

Firm characteristics age and size demonstrated positive influence on company performance within the analysis. The relationships are positive and significant that may indirectly confirm existence of an increasing return to scale or that experienced and well-provided with resources including financial resources firms may operate more efficiently than other companies do.

Conclusion

The goal of this study was to reveal the most beneficial strategy towards knowledge investments at the time of a recession. Company performance appeared as key judging factor in the analysis of the strategies.

One of the contributions of the thesis to the field is that main risks and opportunities of investments in intangibles in a crisis are summarized. Complexity of decisions related to knowledge investments in tough economic conditions is stated. The chance to gain long-term competitive advantage and low opportunity costs of employed staff stimulate company to accumulate investments in core intangibles during a crisis. However, simultaneously such risks as information asymmetry, high uncertainty and cash intensity inherent in knowledge investments put obstacles in the way of intangibles budgets increasing at the time of economic downturn.

Hypotheses testing based on the sample of about 400 mainly large companies from United Kingdom, Germany and France was carried out. It was confirmed that those firms, which are not afraid to face risks of intangibles accumulation during economic turmoil, are able to improve performance in the following economic upswing utilizing knowledge investments opportunities. Both R&D expenditures and expenses on employees have revealed positive influence on company results.

The most popular strategy for sample companies regarding human capital was accumulation of investments throughout all observed time 2004-2013 (30% of companies followed this pattern). Concerning innovation capital the companies tended to contract R&D expenditures in crisis, though enhancing R&D budgets in the rest of time (27% of sample companies). However, regression analysis has not revealed that R&D cut-backs in crisis had negative consequences for company future results.

The study has certainly some shortcomings that may be eliminated in future. The author could not consider some factors, such as existence of financial constraints for a company. However, controlling for this variable may change empirical evidence of the analysis. Probably, division of the sample by financially constraint companies and others may bring different effects of innovation capital accumulation in crisis on future performance. Accounting for this flaw of the study as well as other possible issues is a task for future research.

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