Obsah+Contents. Ekonomie Economics

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„E+M Ekonomie a Management“ je vûdeck˘ ãasopis publikující pÛvodní recenzované vûdecké práce a vûdecké studie, jejichÏ základem je teoretická a empirická anal˘za. âasopis je zamûfien do oblasti EKONOMIE, PODNIKOVÉ EKONOMIKY, FINANCÍ, MANAGEMENTU resp. INFORMAâNÍHO MANAGEMENTU a MARKETINGU. âasopis je uvádûn˘ v Social Sciences Citation Index, Social Scisearch, Journal Citation Reports/Social Sciences Edition (http://www.thomsonreuters.com), v elektronické verzi indexu EconLit (www.econlit.org), v International Bibliography of the Social Sciences (www.ibss.ac.uk), v databázích Inspec (www.iee.org), SCOPUS (www.info.scopus.com), ABI/INFORM (www.proquest.com), EBSCO Publishing (www.ebscohost.com) a v 11th Edition of Cabell’s Directory of Publishing Opportunities in Economics and Finance/Management (www.cabells.com).

„E&M Economics and Management“ is a scientific journal, that publishes original scientific articles and scientific studies based on theoretical and empirical analyses. The journal is comprised of several sections: ECONOMICS, BUSINESS ADMINISTRATION, FINANCE, MANAGEMENT, INFORMATION MANAGEMENT, and MARKETING&TRADE. The journal is covered in the Social Sciences Citation Index, Social Scisearch and Journal Citation Reports/Social Sciences Edition (http://www.thomsonreuters.com), It is also monitored by the electronic EconLit index (www.econlit.org), International Bibliography of the Social Sciences (www.ibss.ac.uk) and by Inspec (www.iee.org), SCOPUS (www.info.scopus.com), ABI/INFORM (www.proquest.com) and EBSCO Publishing databases (www.ebscohost.com). It is listed in the 11th Edition of Cabell’s Directory of Publishing Opportunities in Economics and Finance/Management (www.cabells.com).

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Obsah+Contents Ekonomie Economics

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4 Testing the Predicative Ability of the Tax Progressiveness Indices Testování vypovídací schopnosti ukazatelÛ daÀové progresivity Václav Friedrich, Katefiina Maková, Jan ·irok˘

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17 Zhodnocení intenzity absorpce podpory podnikání v regionech se soustfiedûnou podporou státu Evaluation of the Absorption Intensity of the Entrepreneurial Support in the Regions Funded Intensely by the Government Katefiina Felixová

Ekonomika a management Business Administration and Management

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28 A Multi-Objective Decision Support System for Project Selection with an Application for the Tunisian Textile Industry Vícekriteriální podpÛrn˘ rozhodovací systém pro v˘bûr projektÛ s aplikací na tunisk˘ textilní prÛmysl Willem Karel M. Brauers, Edmundas Kazimieras Zavadskas

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44 ·truktúra nákladov kvality a citlivosÈ podnikov na v˘kyvy ekonomiky Quality Costs Structure and Company Sensitivity to Fluctuation of Economy Martin Mizla, Patrycja Pud∏o

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57 Hexagonal Stellar Model of CRM – Key Elements Influencing the CRM Building ·esÈhrann˘ hviezdicov˘ model CRM – kºúãové prvky ovplyvÀujúce budovanie CRM Milan Kubina, Viliam Lendel

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73 Maticové modely na meranie v˘konnosti produkãn˘ch systémov Matrix Models for Production Systems Efficiency’s Measurement Michal Grell, Eduard Hyránek

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89 Determinanty kapitálové struktury ãesk˘ch podnikÛ Determinants of Capital Structure within Czech Companies Pavlína Prá‰ilová

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Marketing a obchod Marketing & Trade

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105 Antecedents and Outcomes of Perceived Service Value: Evidence from Slovenia PfiedchÛdci a v˘stupy vnímané hodnoty sluÏeb: studie ze Slovinska Aleksandra Pisnik Korda, Boris Snoj, Vesna Îabkar

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116 Facebook Advertising and its Efficiency on the Slovak Market Reklama na Facebooku a jej efektívnosÈ na slovenskom trhu Martin Vejaãka

Informaãní management Information Management

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128 Identification of Data Content Based on Measurement of Quality of Performance Identifikace datového obsahu na základû mûfiení kvality v˘konu Stanislava ·imonová

RÛzné

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139 Recenze knih Book Review

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139 Management komerãn˘ch bánk, bankov˘ch obchodov a operácií (Jaroslav Belás a kolektív) Jaroslav Slepeck˘

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140 Vybrané teorie ekonomického rÛstu (Iva Nedomlelová) Milan ·ikula

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141 Internacionalizácia podnikateºskej ãinnosti mal˘ch a stredn˘ch podnikov vo vybranom samosprávnom kraji (Ladislav Mura) Alena Pauliãková

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142 Pokyny pro pfiispûvatele Notices and Instructions for the Authors of the Articles

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Ekonomie

TESTING THE PREDICATIVE ABILITY OF THE TAX PROGRESSIVENESS INDICES Václav Friedrich, Katefiina Maková, Jan ·irok˘

Introduction and Aim of the Paper The even tax rate of personal income tax has been a part of the tax system in the Czech Republic since 1/1/2008. The Czech Republic has joined the group of countries that use the even tax rate. In 31/12/2008 this group included 7 countries in the framework of the European Union (Bulgaria, Czech Republic, Estonia, Latvia, Lithuania, Romania and the Slovak Republic), conversely there are 17 tax brackets in Luxembourg. The term "even tax" is misleading according to the authors. Upon deductibles, allowances or the existence of tax credits, "even tax" will always be progressive tax and theoretically the value of the effective tax will achieve the "even tax" value in infinitude. This rate is – viewing the existence of deductibles, allowances or tax credits – a progressive tax as well, though [2]. The tax progressiveness can be measured according to several indices, whereas the indices of progressiveness of the average rate, progressiveness of the tax obligation and progressiveness of earning after taxation are used the most frequently [9], in Czech literature [5] or [11, p.128]. The aim of the paper is to test the predicative ability of these indices or how sensitively they will react to the changes of the effective tax rate. This analysis will be done with the help of the application of tax progressiveness indices on the tax system of the Czech Republic. A dynamic model of personal income tax (PIT) was presented in [13], but it does not deal with tax progressiveness. In the analysis some statistical tools are used, especially correlation. The authors have chosen as an example of using the method described by analysis of the

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personal income tax paid by an employee in Czech Republic in the period of 1993–2008. A relatively long analysed period (the lower bound is qualified by the implementation of the present tax system) gives the possibility to generalize some results for possible upcoming research in this field. It is possible to analyse other types of employee e.g. employee with children or disabled in future continuation of the research. In detail – see [12, p. 654].

1. The Effective Tax Rate and the Tax Progressiveness Definition The effective taxation (effective tax rate; ETR) is characterized by the average tax rate that is defined as the percentage ratio of tax obligation to gross pay. The ETR can be defined in different ways depending on the definition of the tax obligation (which deliveries are implicated in the tax obligation). The ETRT index was monitored in the Czech Republic with regard to the purpose of the paper. The ETRT was defined as the personal income tax (T) to the gross income (Y) from which the tax is calculated: T ETRT = ––– × 100 [%] Y

(1)

Moreover the ETRT+SI index was monitored, which adds the social insurance payments paid by an employee (SI) to the tax obligation and expresses the employee’s total tax burden by tax deliveries more objectively: T + SI ETRT + SI = –––––– × 100 [%] Y

(2)

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Ekonomie The progressiveness of personal income tax is calculated by means of the gross income values and the tax obligation corresponding to the gross income as well. Even if the progressiveness of the personal income tax determination comes out of the same values as the effective tax rate (eventually tax burden), there is no distinct link between the tax progressiveness and the effective tax rate. If the effective tax rate at two gross incomes is higher in one country than in another country, this does not have to mean that the tax progressiveness has to be higher in this country too [10, p. 153] or [3, p. 386]. While the effective tax rate (eventually tax burden) is a static value, the tax progressiveness is examined at a specific income interval (or in time [6] did concrete calculations of the tax progressiveness in the Czech Republic in 1993–2006), and that’s why this is the flow value [7, p. 201]. According to the tax progression, the tax can be proportional, progressive and regressive. The tax is progressive if the average tax rate grows together with growth of the gross income (the tax grows more quickly than the income). Measuring the tax progressiveness and its changes is important for the comprehensive determination of the impacts of the tax legislation amendments or for the determination of which income interval has the highest progressiveness (in which group of taxpayers the tax is growing the most). While the degree of tax burden only tells “...what part of their income the taxpayers pay in the form of tax, the degree of progressiveness characterizes the degree of difference of the tax burden of individual taxpayers according to their income” [5, p. 455]. There are usually three ways of measuring the tax progressiveness in literature (e.g. [9, p. 333] or [5, p. 456]. The term “local progressiveness” is used sometimes: progressiveness of the average rate (PAR), progressiveness of the tax obligation (PTO) and progressiveness of earning after taxation (PEAT). Progressiveness of the average rate measures the ratio of the average rate change and the income change:

(3)

Indices j and j-1 relate to marginal points of the j-th income interval, in which the progressiveness is measured. The PAR index illustrates the inclination of the curve that models the dependence of the effective tax rate ETR on the income Y. If the tax is proportional (ETR = constant), its value is zero. Mathematically, the index shows the derivation of the examined values (∂ETR / ∂Y). The progressiveness of the tax obligation represents the elasticity of the tax obligation with regards to the income before taxation:

(4)

The calculated coeffective measures the ratio of the relative change of the tax obligation T towards the relative change of the income Y – it would be expressed by the direction of the dependence curve of the tax T to the income Y in the logarithmic graph. If the tax is proportional, the value of this index is one. The index shows the tax elasticity ET/Y mathematically. The PEAT index of the progressiveness of earnings after taxation is the elasticity of the earnings after taxation with regards to the income before taxation:

PEAT j =

% ∆ (Y - T ) % ∆Y

(Y j - T j ) - (Y j - 1 - T j - 1 ) =

(Y j - 1 - T j - 1 ) Yj - Yj - 1 Yj - 1

(5)

If the tax is proportional, the value of this index is one as well. However, this index is opposite towards the index (4) – see Table 1.

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Ekonomie Tab. 1:

Interpretation of Particular Tax Progressiveness Indices Value Progressiveness Progressiveness of the average rate of the tax obligation

Tax

Progressiveness of earning after taxation

proportional tax

0

1

1

progressive tax

>0

>1

<1

regressive tax

<0

<1

>1 Source: [9] or [8]

It is evident that any of the indices can be used for the calculation of the tax progressiveness; nevertheless the degree of progression will differ at all indices depending on the construction of the index. A review of values that the individual indices can reach is given in Table 1. As it cannot be assessed theoretically, which from the featured indices is more proper for investigation of the tax progressiveness, these indices were calculated in the Czech Republic in the period of 1993–2008. Act No. 586/1992 Coll., on Income Tax, was amended 98 times during the period and these changes obviously have a reflection in the PIT change and in the change of the progressiveness.

2. Methodology Adopted If such methodology is chosen [8], the standard procedure is that indices 0 and 1 are matched with marginal values of the income interval, and cannot be changed for the time of the Tab. 2:

examination. Such an approach, however, represents a fixation of the values whose real valuation is decreasing with time. If a fixed interval were taken into account, the result would be a comparison of the tax progressiveness along the interval, and a determination of how the changes in the construction of the tax (deductibles, tax brackets and tax rates within them, tax credits) affected the degree of the progressiveness within the interval defined by means of fixed nominal margins. The average employee gross wage was increased more than 4 times in the Czech Republic in the examined period and that’s why the authors have chosen, for the calculations “movable” end points of the examined intervals with the help of average wage multiples. This solution partly eliminates the impact of the change of the price level that has a reflection in an increase of the average wage too. Table 2 shows the values of the average wage in the Czech Republic, their totals and the year-onyear growth.

Development of the Average Wage in the Czech Republic in 1993–2008

year

1993

1994

1995

1996

average wage (in CZK)

5,817

6,894

8,172

9,676

year 1993 = 100

100.00 118.51 140.48 166.34 183.79 210.01 217.74

231.91

previous year = 100 100.00 118.51 118.54 118.40 110.49 109.37 108.32

106.51

year

2001

2002

2003

2004

1997

1998

1999

10,691 11,693 12,666

2005

2006

2007

2000 13,490

2008

average wage (in CZK)

14,642 15,707 16,917 18,250 19,406 20,211 21,119

year 1993 = 100

251.71 270.02 290.82 313.74 333.61 347.45 363.01

404.71

previous year = 100 108.54 107.27 107.70 107.88 106.33 104.15 104.49

111.47

23,542

Source: www.czso.cz [Czech Statistic Office] + own calculations

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Ekonomie Now the margin values of the intervals are matched to the average wage adjusted by coeffectives equal to the particular multiple of the average wage at the PAR, PTO and PEAT calculations. Viewing the level of the intervals, the average wage represents an independent variable. The main advantage of this modified approach is the relatively constant number of the taxpayers within the individual intervals analysed, taking into account the fact that the income "scissors" have been opening wide (In the Czech Republic 68 % of the employees were earning below-average wages.), applying this method of determining the interval margin values, you may find out how the tax progressiveness is changing in the case of a taxpayer that stays within the same income interval for the whole period examined. For the purposes of the analysis, an employee was chosen as a representative of the majority of the "active" taxpayers who claims only the non-taxable part of a tax base (in 1993–2005) or the tax credit (in 2006–2008).

3. Results The calculations of the effective tax rate and the tax progressiveness of the personal income tax in the Czech Republic cover the period from 1993–2007, income categories 0.50; 0.67; 1.00; 1.33; 1.50; 1.67 and 2.00 multiple of the average wage; the lower average wage is not as predicative for the taxpayer’s income (social security benefit influence), higher incomes refer to the minimum of the employee. In the Czech Republic three methods have been used in taking into account the inflation since the tax system reform in 1993. This includes: increasing tax reliefs, the adjustment of the tax rates and the adjustment of the tax brackets. The exemption limit was considered as the basic allowance, whose worth was raised every year during the period from 1993–1999 (and in 2001 as well). In 2006 the allowances were substituted with tax credits. In the 1993–2000 period the number of tax brackets was lowered gradually from the previous six (1993–1995) to five (1996–1999) and four (until 2007), in the year 1993 (47 %), 1994 (44 %), 1995 (43 %), 1996 (40 %) and 2000 (32 %) the highest marginal tax rate was lowered as well. On the other hand the lowest marginal tax rate (15 %) was not changed until

2006, in the next two years it was 12 %. The tax bracket with the lowest tax rate was increased in 1996, 1998, 1999, 2001 and 2006. The biggest change in calculation of the tax happened in 2008 when the even tax rate was established in the amount of 15 %, however this is calculated from the “super gross wage”, i.e. social insurance payments are also included in the tax base (paid by an employer for an employee). Tax credits also increased considerably in 2008. The values of the effective tax rate, using only the tax (ETRT) and the deliveries of social insurance (ETRT+SI) and the tax progressiveness indices calculated from them are shown in Tables 3, 4 and 5. These tables are in the appendix.

4. Strength of the Individual Indicators of Tax Progressiveness In the analysis, using mathematical and statistical tools, especially correlation, three important indicators of tax progressiveness were compared: PAR – Progressiveness of the Average Rate; PTO – Progressiveness of the Tax Obligations; PEAT – Progressiveness of the Income After Tax. The indicators have been calculated both for the income tax itself, and for all charges (i.e. taxes + insurance). Some information about the similarity of these characteristics can be offered using the “contour maps” coloured tables with the values of all the indicators broken down by income intervals (rows) and year (column). The Excel Spreadsheet offers this option in its last version MS Excel 2007 (this tool can be found as Conditional Formatting – Color Ranges at Styles toolbar). The minimum values for each indicator are assigned dark green (“plain”), maximum values to dark red (“top of the mountain”). Other values are interpolated into a range from green through yellow-brown, brown to red. In black and white printings the advantages of color maps are not so clear, as it is shown in Table 6 (colour maps of ETR and PAR Tables to compare). (The full colour version of Excel 2007 spreadsheet with all the tables from this article can be found on web – see http://moodle.vsb.cz/ moodle/file.php/1/analysis2_final.xlsx.)

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Ekonomie Tab. 6:

Graphic Design of “Contour Map” Views of Table Values to be Compared (ETR and PAR – calculated without charges)

year / level

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5

0.177 0.184 0.189 0.188 0.189 0.188 0.187 0.192 0.191 0.196 0.204 0.204 0.207 0.171 0.173 0.152

0.67

0.199 0.204 0.208 0.205 0.206 0.205 0.205 0.208 0.208 0.211 0.214 0.217 0.220 0.195 0.199 0.196

1

0.221 0.223 0.226 0.222 0.223 0.222 0.222 0.225 0.226 0.231 0.236 0.240 0.244 0.227 0.230 0.240

1.33

0.232 0.239 0.246 0.239 0.242 0.241 0.240 0.244 0.244 0.248 0.252 0.255 0.260 0.255 0.258 0.261

1.5

0.240 0.247 0.253 0.246 0.248 0.248 0.247 0.250 0.250 0.254 0.259 0.265 0.270 0.265 0.268 0.269

1.67

0.247 0.253 0.258 0.251 0.253 0.253 0.252 0.255 0.256 0.261 0.267 0.273 0.277 0.277 0.282 0.275

2

0.257 0.262 0.273 0.266 0.267 0.266 0.265 0.270 0.270 0.272 0.280 0.287 0.294 0.298 0.302 0.284

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

0.132 0.117 0.109 0.102 0.101 0.103 0.103 0.096 0.097 0.090 0.084 0.078 0.073 0.141 0.151 0.262

0.67-1.0

0.065 0.059 0.057 0.051 0.050 0.051 0.052 0.053 0.054 0.059 0.065 0.070 0.074 0.097 0.095 0.131

1.0-1.33

0.034 0.049 0.060 0.050 0.058 0.058 0.056 0.056 0.056 0.052 0.049 0.045 0.049 0.085 0.085 0.066

1.33-1.5

0.049 0.044 0.041 0.041 0.039 0.039 0.039 0.038 0.038 0.034 0.042 0.058 0.056 0.059 0.059 0.044

1.5-1.67

0.041 0.035 0.031 0.032 0.031 0.031 0.032 0.030 0.030 0.045 0.051 0.047 0.044 0.071 0.082 0.035

1.67-2.0

0.030 0.028 0.044 0.046 0.040 0.040 0.037 0.043 0.044 0.042 0.038 0.042 0.051 0.064 0.061 0.026 Source: own calculations (in MS Excel)

At first glance no visual similarity is clear between the efficient tax rate (ETR) and the various indicators of progressiveness (PAR, PTO, and PEAT). On the contrary, the similarity between the PAR and PTO indicators is obvious, both for the distribution of the tax itself, and all charges. Therefore, it seems that these two factors have similar explanatory power (they can be interchangeable). The similarity between the first two indicators (PAR, PTO) and the third (PEAT) is significantly weaker, especially in the distribution of the tax itself. If we examine the distribution of both taxes and insurance, a certain similarity between the PTO and PEAT indicators is shown, however, it is weaker than between the PAR and PTO. More precise expression of the similarity between the indicators can be allowed using correlation analysis. The correlation can be understood as the degree of linear interdependence between two indicators. The higher the degree of correlation, the stronger the link between the values of both indicators can be understood. The high correlation cannot be explained as the causal relationship, it means that the similarity between the two indicators can

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be expressed as the approximate arithmetic relationship between their values. The default statistic for measuring the correlation is the correlation coefficient with values from the interval of -1 to +1 (for more details see [4]). Correlation coefficients were calculated between the individual tax burden and progressiveness indicators, as well as between the indicators themselves. The calculation was performed both in individual income groups as well as for the entire table. Correlation coefficients were calculated between the individual tax burden and progressiveness indicators, as well as between the indicators themselves. The calculation was performed both in individual income groups as well as for the entire table (total). When examining the correlation of individual income groups all three indicators in both types of tax burdens (i.e. only tax and taxes + insurance) are shown for similar behaviour. While the low and high incomes show a higher correlation, this means better explanatory power, on the other hand the lowest explanatory power was shown in income groups around the average wage (the group with the lowest correlation is 0.67 to 1.00 times the average wage) – see Table 7.

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Ekonomie Tab. 7:

Correlation between the Effective Tax Rate and Various Indicators of the Progressiveness Tax Burden – for the Individual Income Groups and the Total only tax

range

tax + insurance

ETR-PAR

ETR-PTO

ETR-PEAT

ETR-PAR

ETR-PTO

ETR-PEAT

0.5-0.67

-0.819

-0.747

0.817

-0.887

-0.873

0.888

0.67-1.0

0.115

-0.142

-0.142

0.066

-0.025

-0.093

1.0-1.33

0.373

0.052

-0.407

0.297

0.177

-0.333

1.33-1.5

0.486

-0.069

-0.538

0.597

0.438

-0.638

1.5-1.67

0.634

0.430

-0.654

0.691

0.632

-0.709

1.67-2.0

0.650

0.307

-0.683

0.622

0.488

-0.660

total

-0.030

-0.028

-0.237

-0.046

-0.040

-0.228

Source: own calculations (in MS Excel)

When we examined the similarity of the tables as a whole, the highest correlation between the percentage of tax burden and the progressiveness indicator was shown using PAR, the lowest (weak dependence, virtually independent) using PEAT. In all cases the negative correlation is calculated. The values of the correlation for both types of tax burdens are practically identical, we can therefore say that particular explanatory power indicators do not differ if we follow only its own tax is a tax or insurance. (The significance of correlations mentioned above is not calculated using the statistical t-tests because it is meaningful only for sample analysis.) Although all three indicators of the progressiveness tax burden are relative, i.e. they eliminate the development of average wages over time, an analysis correlation with eliminating the time influence was made (using the trace analysis and partial correlations). Although the values of the correlation coeffectives changed slightly, these changes are minimal and the findings mentioned in the previous paragraph shall remain valid. The effect of development time on the explanatory power of the indicators is minimal. We confirm the initial assumption that the relative tax burden progressiveness indicators are independent of time. Given the minimal relevance of this correction the concrete results are not indicated in this paper. In the next phase of the correlation analysis the progressiveness indicators were compared among themselves, again separately for each type of tax burden. The results monitored in both tax burdens (without and with insurance)

are practically identical. In various income groups the correlation coeffectives show a nearly perfect linear relationship (the value is close to +1 or -1), where the PAR – PEAT shows 100% correlation even in all groups, which points to a mathematical relationship in the calculations of both indicators. It also (as a result of the nature of these indicators) shows that PEAT has the opposite development tendency from the other two factors – correlations between PEAT and the remaining indicators are negative, while the correlation between the PAR and the PTO is positive. If we examine the correlation of the entire table, i.e. over all the income bands, the similarity between indicators will no longer be as strong. The greatest similarity is shown between the PAR and PTO, there the dependence may be assessed as very strong. It can be stated that both indicators have very similar explanatory power and are largely fungible. The similarity between the PAR and PEAT, or the PTO and PEAT, is significantly lower, here we have to evaluate the dependence as of the average strength. Only a PTO – PEAT pair in the case of taxes including insurance has again shown a strong similarity. It seems that with the growing share of the tax burden the behaviour of PEAT is more similar to PTO, even if their dynamics are opposite – see Table 8. All of these exact findings still confirm the assessment based on the analogy with coloured line maps in the tables as mentioned at the beginning of this text. Finally, the interdependence of the same indicators for both types of reference tax

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Ekonomie Tab. 8:

Correlations between Various Indicators of Progressiveness Tax Burdens – for the Individual Income Groups and the Total only tax

range

tax + insurance

ETR-PAR

ETR-PTO

ETR-PEAT

ETR-PAR

ETR-PTO

ETR-PEAT

0.5-0.67

0.986

-1.000

-0.985

0.999

-1.000

-0.999

0.67-1.0

0.967

-1.000

-0.959

0.996

-1.000

-0.993

1.0-1.33

0.944

-0.999

-0.931

0.992

-0.999

-0.987

1.33-1.5

0.835

-0.998

-0.800

0.982

-0.999

-0.971

1.5-1.67

0.969

-1.000

-0.962

0.997

-1.000

-0.994

1.67-2.0

0.920

-0.999

-0.900

0.986

-0.999

-0.976

total

0.902

-0.999

-0.900

0.883

-0.487

-0.801


burdens was examined. In this case an extremely high dependence was detected in both ETR and all three progressiveness indicators (i.e. PAR, PTO, and PEAT). All the correlation coefficients of the PAR and PEAT indicators were virtually equal to 1 that indicates almost a direct linear functional relationship between the indicators, while the amount of the tax burden is almost invariant to the behaviour of these indicators (the only exception is the category 1.67–2.00). The origin ETR and PTO index also indicate high relationship, but not directly functional, meaning that they depend somewhat on the amount of the tax burden – see Table 9. The almost 100% level of correlation between the two indicators of the PAR is so interesting that we have returned to the original tables 4A and 5A expressing the distribution of this indicator over time and between different

Tab. 9:

income groups. One surprise finding is that even the value of the PAR indicators alone is, with only one exception virtually identical, which means that this indicator does not affect whether it is calculated from the tax only or from taxes with insurance. Low sensitivity to the understanding of the tax burden is also shown in the indicator of progressiveness of income after tax – PEAT. There are, however differences between the values of the order of hundredths of points (units in per cent). The biggest differences (the order of tens or hundreds of percentage points) are reported in the progressiveness of the PTO tax obligation indicator. This indicator reacts most of all to a change of the perception of the tax burden (the "net" tax or any charges). The greatest changes in the PTA values are in the lowest income categories.

Correlation between Corresponding Indicators for Both Types of Tax Burdens (Tax Only, or Taxes + Insurance) – for the Individual Income Groups and the Total tax – tax + insurance

range

ETR

PAR

PTO

PEAT

0.5-0.67

0.955

1.000

0.991

1.000

0.67-1.0

0.917

1.000

0.984

1.000

1.0-1.33

0.951

1.000

0.975

1.000

1.33-1.5

0.963

1.000

0.919

1.000

1.5-1.67

0.969

1.000

0.985

1.000

1.67-2.0

0.975

0.966

0.933

0.969

total

0.993

0.999

0.909

0.995


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Discussion and Conclusion An employee was chosen for analysis in this paper, this employee claims only the basic allowances (from 1993–2005), or the tax credit (from 2006–2008) and he does not claim any other tax reliefs or credits. This means that this analysis refers e.g. to a single, childless taxpayer or the second from the spouse who does not claim any tax reliefs for children. An increasing effective tax rate trend was stable in the 1993–2005 period with the exception of the years 1998 and 1999 (all taxpayers) and 2001 (the two lowest income groups of taxpayers). This was caused by the increase of the average wage and by the fact that the tax system in the Czech Republic was only slightly flexible. In 1998 and 1999 the change of this trend was caused mainly by the enlargement of the tax brackets with the lowest tax rate (15 %). In 2006 tax credits were introduced instead of allowances and this led to the decrease of ETRT for all taxpayers with the exception of taxpayers earning 2 times the average wage. In 2008 the establishment of a flat tax rate caused a decrease of ETRT for most taxpayers with the exception of taxpayers earning the average wage or 1.33 and 1.5 multiples of the average wage. The analyses were done in the Czech Republic in 1993–2008. It would be interesting to verify whether similar conclusions have been reached in other countries, especially in countries with different philosophy of calculating tax from wages. This would show in what measure the conclusions reached are independent of the taxation method and in what measure they represent the specifics of the Czech tax model. It is necessary to remember that the methods used (correlation) belong among statistical methods, i.e. they examine the similarity of the behaviour of individual indices regardless of their real mathematical relationship. Analysis of the mathematical relationship would predicate the affinity of the mentioned indices interestedly, e.g. by a formula deduction that would make it possible to transform one index into another. Nevertheless such a relationship can be quite complicated and would not provide an easy survey, the function of up to 4 incoming parameters complicates transparent analysis. The use of statistical methods simplifies this

problem considerably even if the correlation analysis does not explain the real bindings among indices (it considers these relationships only as “the black box”).

References [1] AUERBACH, A., FELDSTEIN, M. Handbook of Public Economics. Vol. 2. Amsterdam: NorthHolland, 1987. 1106 p. ISBN 0-444-87908-0. [2] CAMINADA, K., GOUDSWAARD, K. Does a Flat Rate Individual Income Tax Reduce Tax Progressivity? A Simulation for the Netherlands. Public Finance and Management. 2001, Vol. 1, Iss. 4. ISSN 1523-9721. [3] CREEDY, J. Taxation, Poverty and Income Distribution. New York: Edward Elgar Publishing, Inc., 1994. 255 p. ISBN 978-1852789138. [4] FRIEDRICH, V., HRADECK¯, P. Statistika B: Vícenásobná lineární regrese a korelace. Moodle na EkF: Archiv v˘ukov˘ch kurzÛ [online]. Ostrava: Vysoká ‰kola báÀská – Technická univerzita Ostrava, 2008–2009 [cit. 2009-20-09]. Available from: <moodle.vsb.cz/archiv09>. [5] KINKOR, J. Mûfiení daÀové progresivity. Finance a úvûr. 1994, Vol. 44, Iss. 9, pp. 455-462. ISSN 0015-1920. [6] MAKOVÁ, K., ·IROK¯, J. Theoretical approaches to measuring of the tax progressiveness (with the practical application). In 2007 Labsi International Conference on Political Economy and Public Choice: Theory and Experiments. Siena: University of Siena, 2007. Available from: . [7] MIRRLEES, J. A. An exploration in the theory of the optimal income tax. Review of Economic Studies. 1971, Vol. 38, Iss. 2, pp. 175-208. Available also from: . ISSN 0034-6527. [8] MUSGRAVE, R., THIN, T. Income Tax Progression, 1929–48. Journal of Political Economy. 1948, Vol. 56, Iss. 6, pp. 498-514. ISSN 0022-3808. [9] MUSGRAVE, P., MUSGRAVE, R. Public Finance in Theory and Practice. 5th ed. New York: McGraw-Hill, 1989. 627 p. ISBN 978-0070441279. [10] SLEMROD, J. Tax progressivity and Income Inequality. Cambridge: Cambridge University Press, 1996. 375 p. ISBN 978-0521587761. [11] ·IROK¯, J. a kol. DaÀové teorie s praktickou aplikací. 2. vyd. Praha: C.H.Beck, 2008. ISBN 9780-80-7400-005-8.

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Ekonomie [12] ·IROK¯, J., MAKOVÁ, K. Independence Between the Efficient Tax Rate and Tax Progressiviness in the Czech Republic During 1993–2007. Ekonomick˘ ãasopis. 2009, Vol. 57, Iss. 7, pp. 653-666. ISSN 0013-3035.

[13] VLACH¯, J. Dynamick˘ model zdanûní pfiíjmÛ fyzick˘ch osob. E+M Ekonomie a Management. 2008, Vol. 11, Iss. 3, pp. 85-93. ISSN 1212-3609.

Ing. Václav Friedrich, Ph.D., ING-PAED-IGIP VSB – Technical University of Ostrava Faculty of Economics Department of Mathematical Methods in Economics [email protected] Ing. Katefiina Maková, Ph.D. VSB – Technical University of Ostrava Faculty of Economics Department of Public Economics [email protected] prof. Ing. Jan ·irok˘, CSc. VSB – Technical University of Ostrava Faculty of Economics Department of Public Economics [email protected]

Doruãeno redakci: 2. 10. 2009 Recenzováno: 10. 1. 2010, 4. 1. 2010 Schváleno k publikování: 9. 1. 2012

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Abstract TESTING THE PREDICATIVE ABILITY OF THE TAX PROGRESSIVENESS INDICES (USING THE EXAMPLE OF AN EMPLOYEE IN THE CZECH REPUBLIC IN 1993–2008) Václav Friedrich, Katefiina Maková, Jan ·irok˘ The personal income tax is not harmonised in the European Union that’s why there are different systems of the personal income tax which reflects in different nominal tax rates, different allowances, deductions and tax credits. The comparisons based on nominal tax rates predicate the real rate of taxation insufficiently because of these differences. More objective way how to measure the tax circumstances of the taxpayers in individual countries are relative indicators such as the tax incidence of the taxpayer with an average wage, the calculation of an effective tax rate or measuring the tax progressiveness. The index of the tax progressiveness which is based on the effective tax rate predicates the effective tax burden and the relationship between the change of the income and the tax burden. The personal income tax paid by the employee in the period of 1993–2008 in the Czech Republic was chosen for analysis. The paper deals with the application of tax progressiveness indices on the tax system of the Czech Republic. Calculations are performed of the effective tax rate, the progressiveness of the average rate, the progressiveness of the tax obligation and the progressiveness of earning after taxation for an employee who claims only the basic allowances (from 1993–2005), or the tax credit (from 2006–2008) and does not claim any other tax reliefs or credits. It is tested how sensitively the particular indices of the tax progressiveness react to the changes of the effective tax rate. Key Words: effective tax rate, even tax rate, personal income tax, tax obligation, tax progressiveness. JEL Classification: H 21, H 22, H 24.

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Appendix Tab. 3A:

Development of the Effective Tax Rate in the Czech Republic from 1993–2008 (tax only)

year / interval

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5

4.17 5.15 5.66 6.28 6.38 6.26 6.22

6.65 6.62 7.07 7.49 7.90 8.22 4.56 4.81 2.66

0.67

6.41 7.14 7.51 8.02 8.10 8.01 7.97

8.28 8.27 8.60 8.92 9.23 9.46 6.96 7.38 7.13

1

8.57 9.08 9.38 9.70 9.75 9.70 9.67 10.03 10.06 10.56 11.05 11.53 11.89 10.16 10.51 11.46

1.33

9.68 10.69 11.36 11.36 11.65 11.61 11.52 11.88 11.90 12.29 12.66 13.01 13.51 12.95 13.33 13.64

1.5

10.52 11.44 12.05 12.05 12.32 12.28 12.19 12.52 12.54 12.87 13.37 13.99 14.46 13.96 14.33 14.39

1.67

11.22 12.04 12.58 12.60 12.84 12.81 12.73 13.03 13.05 13.64 14.23 14.79 15.21 15.17 15.72 14.98

2


Tab. 3B:

Development of the Effective Tax rate in the Czech Republic from 1993–2008 (tax and social insurance)

year / interval

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5

17.67 18.40 18.91 18.78 18.88 18.76 18.72 19.15 19.12 19.57 19.99 20.40 20.72 17.06 17.31 15.16

0.67

19.91 20.39 20.76 20.52 20.60 20.51 20.47 20.78 20.77 21.10 21.42 21.73 21.96 19.46 19.88 19.63

1

22.07 22.33 22.63 22.20 22.25 22.20 22.17 22.53 22.56 23.06 23.55 24.03 24.39 22.66 23.01 23.96

1.33

23.18 23.94 24.61 23.86 24.15 24.11 24.02 24.38 24.40 24.79 25.16 25.51 26.01 25.45 25.83 26.14

1.5

24.02 24.69 25.30 24.55 24.82 24.78 24.69 25.02 25.04 25.37 25.87 26.49 26.96 26.46 26.83 26.89

1.67

24.72 25.29 25.83 25.10 25.34 25.31 25.23 25.53 25.55 26.14 26.73 27.29 27.71 27.67 28.22 27.48

2


Tab. 4A:

Progressiveness of Average Rate Development PAR (tax only) – Czech Republic 1993–2008

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

0.132 0.117 0.109 0.102 0.101 0.103 0.103 0.096 0.097 0.090 0.084 0.078 0.073 0.141 0.151 0.262

0.67-1.0

0.065 0.059 0.057 0.051 0.050 0.051 0.052 0.053 0.054 0.059 0.065 0.070 0.074 0.097 0.095 0.131

1.0-1.33

0.034 0.049 0.060 0.050 0.058 0.058 0.056 0.056 0.056 0.052 0.049 0.045 0.049 0.085 0.085 0.066

1.33-1.5

0.049 0.044 0.041 0.041 0.039 0.039 0.039 0.038 0.038 0.034 0.042 0.058 0.056 0.059 0.059 0.044

1.5-1.67

0.041 0.035 0.031 0.032 0.031 0.031 0.032 0.030 0.030 0.045 0.051 0.047 0.044 0.071 0.082 0.035

1.67-2.0


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Ekonomie Tab. 4B:

Progressiveness of Tax Obligations PTO (tax only) – Czech Republic 1993–2008

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

3.117 2.523 2.288 2.092 2.063 2.102 2.109 1.966 1.982 1.853 1.752 1.664 1.595 3.074 3.106 7.600

0.67-1.0

2.021 1.823 1.755 1.635 1.617 1.639 1.646 1.640 1.656 1.691 1.724 1.755 1.778 2.393 2.285 2.842

1.0-1.33

1.522 1.715 1.851 1.690 1.785 1.794 1.771 1.743 1.737 1.660 1.587 1.517 1.549 2.107 2.081 1.767

1.33-1.5

1.766 1.619 1.536 1.536 1.507 1.509 1.513 1.475 1.475 1.416 1.495 1.665 1.620 1.688 1.662 1.485

1.5-1.67

1.654 1.515 1.432 1.448 1.415 1.424 1.435 1.400 1.400 1.588 1.632 1.562 1.510 1.851 1.953 1.407

1.67-2.0


Tab. 4C:

Progressiveness of Income After Tax, PEAT (tax only) – Czech Republic 1993–2008

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

0.908 0.917 0.923 0.927 0.928 0.926 0.926 0.931 0.930 0.935 0.939 0.943 0.947 0.901 0.894 0.819

0.67-1.0

0.930 0.937 0.939 0.945 0.946 0.944 0.944 0.942 0.941 0.935 0.929 0.923 0.919 0.896 0.898 0.859

1.0-1.33

0.951 0.929 0.912 0.926 0.915 0.915 0.917 0.917 0.918 0.922 0.927 0.933 0.926 0.875 0.873 0.901

1.33-1.5

0.918 0.926 0.931 0.931 0.933 0.933 0.933 0.936 0.936 0.942 0.928 0.901 0.903 0.898 0.898 0.923

1.5-1.67

0.923 0.933 0.941 0.939 0.942 0.941 0.940 0.943 0.943 0.913 0.902 0.909 0.914 0.862 0.841 0.932

1.67-2.0


Tab. 5A:

Progressiveness of Average Rate Development PAR (taxes + insurance) – Czech Republic 1993–2008

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

0.132 0.117 0.109 0.102 0.101 0.103 0.103 0.096 0.097 0.090 0.084 0.078 0.073 0.141 0.151 0.262

0.67-1.0

0.065 0.059 0.057 0.051 0.050 0.051 0.052 0.053 0.054 0.059 0.065 0.070 0.074 0.097 0.095 0.131

1.0-1.33

0.034 0.049 0.060 0.050 0.058 0.058 0.056 0.056 0.056 0.052 0.049 0.045 0.049 0.085 0.085 0.066

1.33-1.5

0.049 0.044 0.041 0.041 0.039 0.039 0.039 0.038 0.038 0.034 0.042 0.058 0.056 0.059 0.059 0.044

1.5-1.67

0.041 0.035 0.031 0.032 0.031 0.031 0.032 0.030 0.030 0.045 0.051 0.047 0.044 0.071 0.082 0.035

1.67-2.0


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Ekonomie Tab. 5B:

Progressiveness of Tax Obligations PTO (taxes + insurance) – Czech Republic 1993–2008

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

1.500 1.426 1.386 1.365 1.359 1.368 1.368 1.335 1.340 1.308 1.282 1.257 1.236 1.554 1.585 2.160

0.67-1.0

1.329 1.288 1.273 1.248 1.243 1.250 1.252 1.255 1.261 1.281 1.301 1.321 1.335 1.498 1.477 1.669

1.0-1.33

1.203 1.291 1.353 1.301 1.344 1.347 1.336 1.331 1.329 1.302 1.276 1.248 1.268 1.496 1.494 1.367

1.33-1.5

1.320 1.276 1.247 1.255 1.245 1.245 1.246 1.232 1.231 1.206 1.249 1.339 1.322 1.350 1.342 1.253

1.5-1.67

1.286 1.239 1.206 1.220 1.206 1.210 1.215 1.200 1.200 1.298 1.327 1.297 1.273 1.449 1.509 1.218

1.67-2.0


Tab. 5C:

Progressiveness of Income After Tax PEAT (taxes + insurance) – Czech Republic 1993–2008

year / range

1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

0.5-0.67

0.893 0.904 0.910 0.916 0.916 0.915 0.915 0.921 0.920 0.925 0.930 0.934 0.938 0.886 0.878 0.793

0.67-1.0

0.918 0.926 0.928 0.936 0.937 0.936 0.935 0.933 0.932 0.925 0.918 0.911 0.906 0.880 0.882 0.837

1.0-1.33

0.943 0.916 0.897 0.914 0.902 0.901 0.904 0.904 0.904 0.909 0.915 0.921 0.914 0.855 0.852 0.884

1.33-1.5

0.904 0.913 0.919 0.920 0.922 0.922 0.922 0.925 0.925 0.932 0.916 0.884 0.887 0.880 0.881 0.910

1.5-1.67

0.909 0.922 0.930 0.928 0.932 0.931 0.930 0.933 0.933 0.899 0.886 0.893 0.899 0.838 0.813 0.920

1.67-2.0


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ZHODNOCENÍ INTENZITY ABSORPCE PODPORY PODNIKÁNÍ V REGIONECH SE SOUST¤EDùNOU PODPOROU STÁTU Katefiina Felixová

Úvod Z celé fiady dílãích tematick˘ch a regionálních operaãních programÛ, ze kter˘ch strukturální fondy Evropské unie sestávají, se podnikatelského prostfiedí nejtûsnûji dot˘ká operaãní program Podnikání a inovace. Operaãní program Podnikání a inovace nabízí finanãní pomoc ve formû dotací, úvûrÛ a záruk podnikatelsk˘m subjektÛm bez ohledu na to, zda poskytnutá podpora plyne prostfiednictvím podpofien˘ch podnikatelsk˘ch projektÛ do zaostalého ãi rozvinutého regionu. Na jedné stranû tak existuje hmatatelná snaha o regionální rozvoj podporou pfiedev‰ím malého a stfiedního podnikání, na druhé stranû je patrné, Ïe se jedná a priory o podporu podnikÛ, nikoliv regionÛ. Ov‰em úãinná podpora podnikatelského prostfiedí, v jejímÏ dÛsledku se zv˘‰í podnikatelská aktivita a konkurenceschopnost soukromého sektoru, povede témûfi zákonitû k rÛstu prosperity daného regionu. Následující v˘zkum byl zamûfien na zachycení a kvantitativní podloÏení této naznaãené souvislosti mezi prosperujícím podnikatelsk˘m prostfiedím a rozvojem regionu. Z tohoto dÛvodu se ãlánek ve své anal˘ze zamûfiuje na problematické oblasti, aby tak mohl vykrystalizovat pfiípadn˘ kontext mezi podporou podnikatelsk˘ch subjektÛ a problémov˘mi regiony. Cílem v˘zkumu bylo provést anal˘zu podpofien˘ch projektÛ formou dotací v programovacím období 2007–2013 v centrálnû vymezen˘ch problémov˘ch regionech, resp. ve strukturálnû postiÏen˘ch regionech, v hospodáfisky slab˘ch regionech a regionech s vysoce nadprÛmûrnou nezamûstnaností. V souvislosti s deklarovan˘m cílem byly stanoveny následující hypotézy, jejichÏ potvrzení ãi vyvrácení bylo pfiedmûtem provedeného v˘zkumu:

H1: Podnikatelské subjekty pÛsobící v problémov˘ch regionech jsou pfii rozhodování o pfiidûlení dotace upfiednostÀovány oproti podnikatelsk˘m regionÛm pÛsobících v ostatních „neproblémov˘ch“ regionech. H2: âerpané objemy dotací dílãích programÛ operaãního programu Podnikání a inovace jsou diferencovány v závislosti na tom, zda místo urãení dotace se nachází v problémové oblasti ãi nikoliv. H3: Podpofiené projekty jsou realizovány pfieváÏnû v regionech vyÏadujících soustfiedûnou podporu státu vymezen˘ch na centrální úrovni, ménû pak v problémov˘ch regionech vymezen˘ch ze strany samotn˘ch krajÛ.

1. Metodika Z hlediska metodiky byl v˘zkum zaloÏen na re‰er‰i ãeské i zahraniãní literatury, na anal˘ze statistick˘ch dat, dokumentÛ a databází publikovan˘ch státními a soukrom˘mi institucemi (zejména Ministerstvem pro místní rozvoj âeské republiky, Ministerstvem prÛmyslu a obchodu âeské republiky a agenturou CzechInvest), anal˘ze v˘sledkÛ proveden˘ch dílãích v˘zkumn˘ch projektÛ a ‰etfiení, a v neposlední fiadû také na anal˘ze dat a v˘stupÛ, které byl pfiedmûtem vlastního v˘zkumu. Vlastní v˘zkum byl zaloÏen na anal˘ze databází, resp. seznamÛ pfiíjemcÛ podpor publikovan˘ch Ministerstvem prÛmyslu a obchodu âeské republiky. Z více neÏ tfií tisíc podpofien˘ch projektÛ byly dal‰í anal˘ze podrobeny ty podnikatelské subjekty, které získaly podporu ve formû dotace. Zkouman˘ vzorek sestával z patnácti set subjektÛ. Jako hlavní úskalí v˘zkumu se ukázala skuteãnost, Ïe pfievzaté databáze uvádí u kaÏdého

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Ekonomie podpofieného projektu, kromû v˘‰e dotace, kraj realizace, bez bliÏ‰ího urãení obce. Vzhledem k tomu, Ïe problémové oblasti v âeské republice jsou vymezovány na úrovni okresÛ a územních obvodÛ obcí s roz‰ífienou pÛsobností, pfiípadnû územních obvodÛ obcí s povûfien˘m obecním úfiadem, byl údaj o kraji realizace zcela nedostaãující. Potfiebn˘ údaj o lokalitû urãení na úrovni obce byl zji‰tûn pro kaÏd˘ podnikatelsk˘ subjekt podpofien˘ ve formû dotace prostfiednictvím obchodního rejstfiíku. Po vytvofiení vlastní databáze roz‰ífiené o dodateãn˘, nezbytn˘ údaj t˘kající se lokality urãení, byly vyfiltrovány údaje relevantní pro následn˘ v˘zkum.

2. V˘sledky 1. Podpofiené projekty v rámci OP Podnikání a inovace v kontextu státem vymezen˘ch regionÛ se soustfiedûnou podporou státu Jedním z hlavních v˘stupÛ vlastního ‰etfiení jsou data uvedená v tabulce 1. Tato tabulka, resp. data v ní uvedená jsou v˘sledkem agregace relevantních údajÛ t˘kajících se aktuálnû vymezen˘ch regionÛ se soustfiedûnou podporou státu ze vzorku 1 455 projektÛ podpofien˘ch formou dotací. Projekty plynoucí do tûchto regionÛ byly vygenerovány podle místa urãení dotace, resp. obce z databáze roz‰ífiené v rámci vlastního v˘zkumu právû o tyto údaje. Souhrnnû a následnû zvlá‰È pro kaÏdou ze tfií kategorií regionÛ se soustfiedûnou podporou státu byly sledovány jednak celkové poãty projektÛ a celkové objemy dotací ve tfiech kategoTab. 1: Program

Rozvoj

riích problémov˘ch regionÛ (vertikální anal˘za), a jednak poãty projektÛ a objemy dotací v jednotliv˘ch programech (horizontální anal˘za). Ve‰keré níÏe uvedené grafy v rámci této podkapitoly vychází z tabulky 1. Pfii zacílení pohledu na úroveÀ problémov˘ch oblastí zahrnujících strukturálnû postiÏené regiony, hospodáfisky slabé regiony a regiony s vysoce nadprÛmûrnou nezamûstnaností, je z ãíseln˘ch údajÛ v tab. 1 patrno nûkolik skuteãností. V první fiadû odpovídá podíl finanãní ãástky plynoucí do problémov˘ch oblastí podílu podpofien˘ch projektÛ vzhledem k hodnotám vztaÏen˘m k celé republice. Tento pomûr je pfiibliÏnû 1 : 3 (Zmínûn˘ pomûr je zfiejm˘ pfii porovnání svûtle ‰ed˘ch a tmavû ‰ed˘ch polí ve v˘‰e uvedené tab. 1.), z celkové ãástky tak plyne celá tfietina pfiidûlen˘ch dotací do problémov˘ch oblastí, a souãasnû tfietina z celkového poãtu podpofien˘ch projektÛ pfiipadá na problémové oblasti. Dal‰ím neopomenuteln˘m úhlem pohledu jsou procentní podíly finanãních prostfiedkÛ v jednotliv˘ch programech na úrovni problémov˘ch oblastí vztaÏen˘ch k celkov˘m ãástkám ãerpan˘m v tûchto programech. Pro moÏnost srovnání je tfieba mít stále na pamûti, Ïe podíl schválen˘ch projektÛ v problémov˘ch oblastech pfiedstavuje pfiibliÏnû pouze tfietinu z celkového poãtu pfiidûlen˘ch projektÛ v aktuálním programovacím období. Procentní podíly finanãních prostfiedkÛ plynoucích do problémov˘ch oblastí prostfiednictvím jednotliv˘ch programÛ jsou, jak vizuálnû vypl˘vá z obr. 1, následující:

Pfiidûlené dotace celkem v rámci vybran˘ch programÛ OP Podnikání a inovace, a v kontextu problémov˘ch oblastí v období 2007–2013 Poãet projektÛ

Celková ãástka

Strukturálnû postiÏené regiony

Hospodáfiky slabé regiony

Poãet

âástka v Kã

Poãet

68

522 667 000

97

âástka v Kã 739 101 000

Regiony vysoce s nadprÛm. nezam.

Problémové oblasti celkem

Poãet

âástka v Kã

Poãet

âástka v Kã

28

211 774 000

193

1 473 542 000

247

1 956 042 000

ICT a str.sl.

25

289 329 000

5

46 300 000

1

8 069 000

0

0

6

54 369 000

ICT v pod.

236

546 173 000

34

59 941 000

31

54 331 000

12

23 834 000

77

138 106 000

Eko-energie

103

997 300 000

12

52 194 000

13

302 248 000

3

7 644 000

28

362 086 000

Inovace

100

1 664 106 000

8

221 574 000

8

90 577 000

2

12 908 000

18

325 059 000

Potenciál

83

1 123 921 000

3

32 382 000

3

25 988 000

3

38 019 000

9

96 389 000

Marketing

661

397 773 000

77

49 463 000

69

37 991 000

24

16 525 000

170

103 979 000

Celkem

1455

6 974 644 000

207

984 521 000

222

1 258 305 000

72

310 704 000

501

2 553 530 000

Zdroj: vlastní zpracování na základû dat pfievzat˘ch ze statistik agentury CzechInvest

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Ekonomie Obr. 1:

Porovnání celkového objemu pfiidûlen˘ch dotací a dotací plynoucích do problémov˘ch oblastí (v Kã)


penûÏní ãástka v rámci programu Rozvoj v problémov˘ch oblastech pfiedstavuje 75 % jiÏ pfiidûlené celkové alokace pro tento program, program ICT a strategické sluÏby je v problémov˘ch oblastech na úrovni 19 %, program ICT a podnikání pfiedstavuje 25 %, program Eko-energie je na úrovni 36 %, program Inovace se podílí 20 %, program Potenciál pfiedstavuje 9 %, program Marketing je ãerpán ve v˘‰i 26 %.

S ohledem na tyto skuteãnosti je moÏné konstatovat, Ïe zcela prioritní oblastí v problémov˘ch oblastech je prostfiednictvím programu „Rozvoj“ podpora vzniku a rozvoje nov˘ch podnikatelsk˘ch subjektÛ roz‰ifiujících „základnu“ podnikatelského sektoru, kter˘ je moÏné povaÏovat za „Ïivnou pÛdu“ hospodáfiské prosperity daného regionu. Tfii ãtvrtiny celkov˘ch finanãních zdrojÛ z tohoto programu pfiipadají právû na problémové oblasti. Z tohoto úhlu pohledu tak tento program plní svÛj úãel a lze v jeho pÛsobení spatfiovat konvergentní tendence.

Dal‰ím programem, jehoÏ ãerpaná ãástka v problémov˘ch oblastech pfiedstavuje v˘znamn˘ (témûfi 40 %) podíl na celkové ãerpané ãástce v tomto programu je program Ekoenergie. Podnikatelské subjekty pÛsobící v problémov˘ch regionech mají nejspí‰e tendence v rámci zvy‰ování své konkurenceschopnosti vÛãi podnikÛm v ostatních rozvinutûj‰ích regionech sniÏovat nezanedbatelné náklady na energie zavádûním nov˘ch úspornûj‰ích technologií v zájmu sniÏování energetické nároãnosti v˘roby, pfiípadnû vyuÏíváním energie získáné z obnoviteln˘ch zdrojÛ. Jako dal‰í prozaiãtûj‰í dÛvod je moÏné uvést fakt, Ïe v zaostal˘ch oblastech jsou k dispozici rozsáhlé plochy zanedban˘ch pozemkÛ, které lze vyuÏít právû napfi. pro pûstování biomasy. Ostatní programy, resp. jejich procentní podíl pfiipadající na problémové oblasti je ve srovnání s ãerpan˘mi ãástkami pfiipadajícími na celou republiku pod úrovní jedné tfietiny. Jednoznaãnû nejmen‰í podíl vykazuje program Potenciál, jehoÏ ãerpan˘ objem finanãních prostfiedkÛ v problémov˘ch oblastech nedosahuje ani desetiny vÛãi celkové ãástce programu

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Ekonomie Potenciál. S jistou nadsázkou je moÏné kontatovat, Ïe program Potenciál pfiedstavuje v rámci podnikatelsk˘ch aktivit ponûkud „nadstandard“, neboÈ pfiedpokládá, Ïe podnikatelsk˘ subjekt je jiÏ v takové pozici, Ïe mÛÏe uvaÏovat o financování vlastního v˘zkumu a v˘voje, zakládání rozvojov˘ch center, ad. Z uvedeného 9% podílu lze usuzovat, Ïe podniky provozující svou ãinnost v ménû rozvinut˘ch regionech se pot˘kají ãastûji s ponûkud elementárnûj‰ími problémy ve formû napfi. zachování podnikatelské

Obr. 2:

ãinnosti, modernizace zastaral˘ch technologií ãi sniÏování energetické nároãnosti v˘roby. Dal‰ím hlediskem mÛÏe b˘t zamûfiení pozornosti podnikatelsk˘ch subjektÛ spí‰e na operativu, která z krátkodobého hlediska fie‰í problém „pfieÏití“ firmy, neÏ na strategické vize dlouhodobého charakteru. Ov‰em mnohdy právû z dÛvodu absence strategie, v˘zkumu a inovaãních aktivit dochází k zániku podnikatelsk˘ch subjektÛ. VyuÏívání programu Potenciál v tak malém rozsahu je proto ponûkud alarmující.

Podíly finanãních ãástek (pfiidûlen˘ch dotací) podle jednotliv˘ch programÛ OP Podnikání a inovace v problémov˘ch oblastech (v %)


V˘znam programu Rozvoj je zcela zfiejm˘ také z jeho procentního podílu pfiipadajícího na celkovou ãerpanou alokaci smûfiující do problémov˘ch oblastí. Tento podíl pfiedstavuje témûfi 60 %. Ostatní programy, jak dokumentuje obr. 2, se dûlí o zb˘vajících cca 40 %. Velikost jednotliv˘ch podílÛ víceménû koresponduje s nûkter˘mi jiÏ naznaãen˘mi odÛvodnûními v rámci celorepublikového zhodnocení. VÛbec nejniÏ‰ích hodnot dosahuje program ICT a strategické sluÏby (2 %) a Potenciál (necelá 4 %). DÛvod je moÏné spatfiovat v jiÏ zmínûném „nadstandardu“, kter˘ tyto programy mohou v problémov˘ch

20

oblastech pfiedstavovat. Relativnû nízké hodnoty programu Marketing (4 %) jsou s nejvût‰í pravdûpodobností dÛsledkem niÏ‰í finanãní nároãnosti aktivit spojen˘ch s marketingem a prezentací firmy. Naopak spí‰e vy‰‰ích procentních podílÛ dosahují programy Eko-energie a Inovace, které mohou pro podnikatelsk˘ subjekt v problémov˘ch regionech pfiedstavovat nástroj sniÏování v˘robních nákladÛ prostfiednictvím modernizace v˘roby z hlediska vlastních v˘robních technologií i z hlediska jejich energetické nároãnosti.

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Ekonomie 2. Podpofiené projekty v rámci OP Podnikání a inovace v kontextu problémov˘ch regionÛ vymezen˘ch jednotliv˘mi kraji âR Dal‰í ãást v˘zkumu se vûnovala ponûkud dílãímu úhlu pohledu. Tím prvním bylo zohlednûní problémov˘ch oblastí vymezovan˘ch na centrální úrovni – tedy regionÛ se soustfiedûnou podporou státu. Druh˘ dílãí úhel pohledu pfiedstavoval zahrnutí také problémov˘ch regionÛ vymezen˘ch jednotliv˘mi kraji nad rámec státem vymezen˘ch problémov˘ch regionÛ. Slovní spojení „nad rámec“ je zdÛraznûno, neboÈ v této ãásti anal˘zy nebyly brány v úvahy ty regiony, resp. obce, jejichÏ vymezení na centrální a krajské úrovni se shoduje. V˘stupy této ãásti anal˘zy nebyly tak zatíÏeny duplicitou, ale roz‰ífiily zorn˘ úhel pohledu na problematiku dotací plynoucích do problémov˘ch oblastí. Z hlediska metodiky bylo postupováno analogicky jako v pfiedchozí kapitole. V první fiadû byly vybrány v jednotliv˘ch krajích ty obce, které jsou kraji klasifikovány jako problémové a souãasnû nejsou vymezeny jako regiony se soustfiedûnou podporou státu. V dal‰ím kroku byly z tohoto vzorku obcí vyfiazeny ty, do kter˘ch v aktuálním programovacím období neplynula podpora ve formû dotací. Ve‰keré informace jsou vygenerovány ze stejné databáze, kde klíãem pfii vyhledávání ãíseln˘ch údajÛ jsou názvy Tab. 2:

obcí, které byly jednotliv˘mi kraji vymezeny jako problémové, a které jiÏ nejsou souãasnû vymezeny jako regiony se soustfiedûnou podporou státu. Do anal˘zy bylo zahrnuto celkem osm krajÛ – Libereck˘, Královéhradeck˘, Olomouck˘, Pardubick˘, Zlínsk˘, Jihoãesk˘, Stfiedoãesk˘ a Jihomoravsk˘. Ústeck˘, Karlovarsk˘ a Moravskoslezsk˘ kraj souãástí anal˘zy nebyly, neboÈ jimi vymezované problémové regiony se shodují s regiony vymezen˘mi na centrální úrovni, tzn., Ïe zde absentují regiony vymezené nad rámec státem vymezen˘ch. PlzeÀsk˘ kraj a kraj Vysoãina také nejsou zahrnuty do anal˘zy, neboÈ tyto kraje na svém území nevymezují problémové oblasti. Následující v˘zkum byl z ãásti zaloÏen na anal˘ze programÛ rozvoje územních obvodÛ jednotliv˘ch krajÛ âeské republiky se zamûfiením na anal˘zu metodiky vymezování problémov˘ch regionÛ. V˘sledky této anal˘zy byly nezbytné pro potvrzení ãi vyvrácení tfietí hypotézy, která se jiÏ nezamûfiuje pouze na centrálnû vymezené problémové oblasti, ale také na problémové oblasti vymezené jednotliv˘mi kraji. V˘stupem naznaãené anal˘zy byla následující tabulka 2, která pfiehlednû uvádí pro kaÏd˘ kraj problémové regiony tak, jak jsou vnímány samotn˘m krajem, a problémové regiony tak, jak je pro dan˘ kraj vymezuje stát:

Komparace problémov˘ch oblastí na úrovni státu a na úrovni krajÛ (1. ãást)

Kraj

Centrálnû vymezené Problémové oblasti vymezené kraji problémové oblasti

Ústeck˘

Most, Chomutov, Litvínov, Most, Podbofiany Teplice, Louny, Dûãín, Ústí nad Labem, Litomûfiice

Karlovarsk˘

Sokolov, Ostrov

Kraslice, Ostrov, Sokolov

Libereck˘

Vojensk˘ újezd Ralsko, Fr˘dlant

Fr˘dlant, Hejnice, Nové Mûsto pod Smrkem, Raspenava, Vi‰Àová, Doksy, Dubá, Kravafie, Zahrádky, Îandov, Roztoky u Jilemnice, Studenec, MimoÀ, Ralsko, StráÏ pod Dalekem, Zákupy, âesk˘ Dub, Jeni‰ovice, KfiíÏany, Oseãná, Sychrov, Jesenn˘, Lomnice nad Popelkou, Poniklá, Rovensko pod Troskami, Semily, Velké Hamry, Vysoké nad Jizerou, Zásada, Cvikov, Jablonné v Podje‰tûdí, Kamenick˘ ·enov, Krompach, Rynoltice, Bzenecko, Horní Branná, Hrádek nad Nisou, Chrastava, Jablonec nad Jizerou, Lib‰tát, Mní‰ek, Nov˘ Bor, Pfií‰ovice

PlzeÀsk˘

nemá vymezeny

nemá vymezeny

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Ekonomie Tab. 2:

Komparace problémov˘ch oblastí na úrovni státu a na úrovni krajÛ (2. ãást)

Kraj

Centrálnû vymezené Problémové oblasti vymezené kraji problémové oblasti

Královéhradeck˘

nemá vymezeny

Olomouck˘

Jeseník, Pfierov, Olomouc, Prostûjov, Pfierov, Hranice, Konice, ·umperk, ·ternberk, Lipník nad Beãvou, Litovel, Mohelnice, Uniãov, Zábfieh, ·umperk, Uniãov ·ternberk, Jeseník

Sobotka, Nová Paka, Láznû Bûlohrad, Hostinné, DvÛr Králové n.L., Teplice n.M., Hradec Králové, Vamberk, Kopidlno, Úpice, Broumov, Hofiice, Îacléfi, Svoboda n.Ú.

Pardubick˘

Svitavy, Králíky

Moravská Tfiebová, Králíky, Svitavy, Poliãka, Litomy‰l, Hlinsko

Zlínsk˘

KromûfiíÏ, RoÏnov pod Radho‰tûm, Vala‰ské Klobouky

Bojkovice, Brumov-Bylnice, Horní Lideã, Koryãany, Morkovice-SlíÏany, Vala‰ské Klobouky, Hulín, Chropynû, Karolinka, KromûfiíÏ, Uhersk˘ Brod, Vsetín

Jihoãesk˘

nemá vymezeny

Mirovice, Mirotice, Milevsko, Protivín, Volary, Vojensk˘ újezd Boletce, Horní Planá, âesk˘ Krumlov, Vy‰‰í Brod, Kaplice, Nové Hrady, Suchdol nad LuÏnicí, TfieboÀ, Nová Bystfiice, Daãice, Slavonice

Stfiedoãesk˘

Vojensk˘ újezd Mladá

Nymburk, Kolín, Bene‰ov, Beroun, Pfiíbram, Kutná Hora, Rakovník

Moravskoslezsk˘

Karviná, Ostrava-mûsto, Fr˘dek-Místek, Nov˘ Jiãín, Bruntál, Opava

OsoblaÏsko, Moravskoberounsko, Albrechticko, Karvinsko, Orlovsko, Havífiovsko, Vítkovsko, R˘mafiovsko, Ostravsko, Tfiinecko

Jihomoravsk˘

Hodonín, Znojmo, Buãovice, Mikulov

Vranov nad Dyjí, Moravsk˘ Krumlov, Hru‰ovany, Znojmo, Miroslav, Pohofielice, Mikulov, Hustopeãe, Klobouky u Brna, Velká nad Veliãkou, Kyjov, Îdánice, Buãovice, Ivanovice na Hané, Ti‰nov, Letovice, Velké Opatovice, Boskovice

Vysoãina

Tfiebíã, Bystfiice nad Pern‰tejnem

nemá vymezeny

Zdroj: vlastní zpracování na základû „programy rozvoje územních obvodÛ jednotliv˘ch krajÛ âeské republiky“

Tuãnû vymezené lokality pfiedstavují shodu mezi státem vymezen˘mi problémov˘mi oblastmi a krajem vymezen˘mi problémov˘mi oblastmi s absencí bliÏ‰í konkretizace „problémovosti“ daného území. K této poznámce je je‰tû nutné dodat, Ïe kraje hodnotí problémovost svého území v rámci men‰ích územních jednotek, neÏ jak ãiní stát pfii vymezování regionÛ se soustfiedûnou podporou státu. Z tohoto dÛvodu se neshoduje poãet oblastí, a pouze zdánlivû dochází k rozporu také obsahovû – napfi. v Libereckém kraji vymezuje stát ORP Fr˘dlant. Sám Libereck˘ kraj ov‰em vymezuje generelové jednotky Hejnice, Nové Mûsto pod Smrkem, Raspenava a Vi‰Àová, které administrativnû spadají pod ORP Fr˘dlant.

22

Ze souhrnn˘ch hodnot, které jsou patrné ze svûtle ‰edého fiádku tab. 3 a s ohledem na celkovou ãástku pfiidûlen˘ch dotací v rámci podpofien˘ch projektÛ k 23. 2. 2009 (viz ‰ed˘ fiádek tab. 1) je zfiejmé následující: Do problémov˘ch regionÛ vymezen˘ch kraji nad rámec státem vymezen˘ch plynulo ve sledovaném období pfiibliÏnû 680 mil. Kã, coÏ odpovídá 9,6 % z celkového objemu pfiidûlen˘ch dotací (cca 6,97 mld. Kã), zatímco do regionÛ se soustfiedûnou podporou státu bylo v rámci podpofien˘ch projektÛ pfiidûleno pfiibliÏnû 2,5 mld. Kã, coÏ odpovídá 36,6 % z celkového objemu pfiidûlen˘ch dotací. Stejného závûru je dosaÏeno srovnáním poãtu podpofien˘ch projektÛ v centrálnû vymezen˘ch regionech a v regionech

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Ekonomie Tab. 3:

Celkové dotace plynoucí v rámci podpofien˘ch projektÛ do regionÛ se soustfiedûnou podporou státu ve srovnání s objemem dotací smûfiujících do problémov˘ch regionÛ vymezen˘ch kraji nad rámec státem vymezen˘ch (v Kã)

Kraj

Poãet projektÛ

Dotace (centr. úroveÀ)

Poãet projektÛ

Dotace (krajská úroveÀ)

Ústeck˘

78

538 868 000

-

-

Karlovarsk˘

16

157 994 000

-

-

Libereck˘

0

0

6

9 946 000

PlzeÀsk˘

-

-

-

-

Královéhradeck˘

-

-

29

91 544 000

Olomouck˘

59

236 613 000

31

93 033 000

Pardubick˘

23

227 662 000

3

1 274 000

Zlínsk˘

170 612 000

22

65 577 000

24

Jihoãesk˘

-

-

13

50 821 000

Stfiedoãesk˘

0

0

17

173 196 000

Moravskoslezsk˘

206

905 961 000

-

-

Jihomoravsk˘

55

234 590 000

7

90 125 000

Vysoãina

42

186 030 000

-

-

501

2 553 295 000

130

680 551 000

Celkem

Zdroj: vlastní zpracování na základû dat pfievzat˘ch ze statistik agentury CzechInvest Pozn. pro‰krtnutá políãka u nûkter˘ch krajÛ pfiedstavují skuteãnost, Ïe kraj nemá na centrální ãi krajské úrovni vymezeny problémové regiony.

vymezen˘ch kraji „nad rámec“, resp. jejich podílem na celkovém poãtu jiÏ podpofien˘ch projektÛ v rámci dotací. V pfiípadû regionÛ se soustfiedûnou podporou státu se jedná o 501 projektÛ, tedy 34,4 % z celkového poãtu 1 455 podpofien˘ch projektÛ, zatímco v regionech vymezen˘ch kraji „nad rámec“ dosahuje poãet podpofien˘ch projektÛ hodnoty 130, ãemuÏ odpovídá 8,9 % z celkového poãtu podpofien˘ch projektÛ.

3. Diskuse V˘zkum, kter˘ byl pfiedmûtem tohoto pfiíspûvku, tematicky koresponduje s nûkter˘mi dal‰ími pfiíspûvky publikovan˘mi v dfiívûj‰í dobû. Autofii se zamûfiují na problematiku, která je doplnûním ãi roz‰ífiením poznatkÛ uveden˘ch v tomto ãlánku. Za témûfi analogii a paralelu je moÏné povaÏovat pfiíspûvek autorÛ Stejskala, Charburského [12], ktefií se zamûfiují na zkoumání absorpãní kapacity ãesk˘ch regionÛ a jejich zku‰enosti s ãerpáním finanãních zdrojÛ je strukturálních fondÛ EU. Autofii se v rámci svého v˘zkumu vûnují pfiedchozímu programovacímu období 2000–2006.

Z jiného úhlu pohledu na problematiku dotaãních programÛ strukturálních fondÛ EU hledí Pavelková, Jirãíková [10]. Oblast konkurenceschopnosti podnikÛ autorky zkoumají z hlediska klastrÛ, jejich cílÛ, aktivit a zpÛsobÛ fiízení. Dal‰í související v˘zkum provedli napfi. autofii ·ipikal, Pisár, Uramová [14], dále Hlaváãek [3], Uramová, KoÏiak [15], Heimpold [2].

Závûr Problematika regionální rozvoje zejména v problémov˘ch regionech âeské republiky v kontextu ãerpaní finanãních zdrojÛ ze strukturálních fondÛ na podporu podnikatelského prostfiedí, resp. malého a stfiedního podnikání byla v provedeném v˘zkumu analyzována z pohledu intenzity absorpce tûchto podpor. Z moÏn˘ch forem podpory v podobû dotací, úvûrÛ a záruk byl brány v potaz pouze dotace pro svÛj „jedineãn˘“ charakter nevratné formy pomoci (na rozdíl od úvûrÛ a záruk). Dal‰ím dÛvodem byla skuteãnost, Ïe dotace pfiedstavují ve finanãním vyjádfiení 90 % celkové podpory.

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Ekonomie Hlavním cílem v˘zkumu bylo podchytit vazbu regionální rozvoje v problémov˘ch oblastech âeské republiky na podnikatelské prostfiedí, a následnû potvrdit ãi vyvrátit stanovené hypotézy. Anal˘za provedená v souvislosti s první hypotézou, t˘kající se preference podnikatelsk˘ch subjektÛ pÛsobících v problémov˘ch regionech pfii rozhodování o podpofiení projektu formou pfiidûlení dotace byla zaloÏena na v˘stupech uveden˘ch v tabulce 1. Tabulka byla vytvofiena na základû vyfiltrování potfiebn˘ch dat z databáze roz‰ífiené v rámci vlastního v˘zkumu o údaj okresu, resp. obce s roz‰ífienou pÛsobností, ve které pÛsobí dan˘ podnikatelsk˘ subjekt ãerpající dotaci. Data byla uspofiádána podle objemu ãerpan˘ch dotací zvlá‰È pro kaÏd˘ dílãí program operaãního programu Podnikání a inovace, a to jednak na úrovni celé âeské republiky, a jednak na úrovni regionÛ se soustfiedûnou podporou státu, vãetnû tfií podkategorií centrálnû vymezen˘ch problémov˘ch oblastí (strukturálnû postiÏen˘ch regionÛ, hospodáfisky slab˘ch regionÛ a regionÛ s vysoce nadprÛmûrnou nezamûstnaností). Vertikální anal˘za tabulky 1, resp. celkov˘ch objemÛ dotací a celkového poãtu podpofien˘ch projektÛ poskytnut˘ch na jedné stranû v rámci celé âeské republiky, a na druhé stranû plynoucích pouze do problémov˘ch oblastí, umoÏnila uãinit rozhodnutí o nepotvrzení první ze stanoven˘ch hypotéz. Na základû anal˘zy byl totiÏ zji‰tûn jen zhruba tfietinov˘ podíl poãtu podpofien˘ch projektÛ v problémov˘ch regionech vztaÏen˘ k celkovému poãtu podpofien˘ch projektÛ v celé âeské republice (1 455 celkem, 501 v problémov˘ch oblastech), ãemuÏ odpovídá i finanãní vyjádfiení. Do problémov˘ch oblastí plyne rovnûÏ tfietina celkového objemu poskytnut˘ch dotací (pfiibliÏnû 7 mld. celkem, 2,5 mld. v problémov˘ch oblastech). Vezmeme-li v úvahu relativní poãet obyvatel, resp. plochu regionÛ se soustfiedûnou podporou státu, které ãítají 32 % populace, resp. 30 % území âR, pak to znamená, Ïe do regionÛ vyÏadujících soustfiedûnou podporu státu plyne odpovídající podíl finanãních zdrojÛ. Tato hypotéza byla na 5% hladinû v˘znamnosti ovûfiena technikou chí kvadrát dobré shody (p-value = = 0,0845). V tomto smûru bylo moÏné uãinit související dílãí závûr o neutrálním vlivu operaãního programu Podnikání a inovace na zmírÀování regionálních disparit.

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Objasnûní druhé hypotézy t˘kající se diferencovanosti objemÛ ãerpan˘ch dotací z dílãích programÛ operaãního programu Podnikání a inovace v závislosti na tom, zda se jedná o podnikatelsk˘ subjekt z problémového regionu ãi nikoliv, vycházelo rovnûÏ z anal˘zy dat, které dokumentuje tabulka 1. Horizontální pohled na tuto tabulku, zejména na celkové poãty projektÛ a objemy finanãních zdrojÛ v rámci celé âeské republiky v kontextu stejn˘ch sledovan˘ch údajÛ celkem za problémové oblasti, potvrdil platnost druhé hypotézy, a to nejmarkantnûji v pfiípadû programu Rozvoj. Z celkové pfiidûlené alokace pro celou âeskou republiku na program Rozvoj pfiipadají celé tfii ãtvrtiny na problémové oblasti. Platnost druhé hypotézy byla na 5% hladinû v˘znamnosti ovûfiena testem chí kvadrát nezávislosti (p-value = 7 x 10-55). Postavení a v˘znam programu Rozvoj se jednoznaãnû potvrdil také pfii anal˘ze podílÛ finanãních ãástek jednotliv˘ch programÛ operaãního programu Podnikání a inovace ãerpan˘ch v rámci problémov˘ch oblastí. V regionech se soustfiedûnou podporou státu pfiipadá z celkové ãástky pfiidûlen˘ch dotací cel˘ch ‰edesát procent právû na program Rozvoj, kter˘ je a priori zamûfien na podporu vzniku a rozvoje nov˘ch podnikatelsk˘ch subjektÛ roz‰ifiujících soukrom˘ sektor, jeÏ lze právem povaÏovat za základní stavební kámen a pilífi prosperujícího regionu. Poslední ãást v˘zkumu si kladla za cíl zúÏit úhel pohledu a zamûfiit se na regiony se soustfiedûnou podporou státu v komparaci s problémov˘mi regiony vymezen˘mi jednotliv˘mi kraji âeské republiky. Vzhledem ke skuteãnosti, Ïe vymezování problémov˘ch oblastí kraji je zcela v jejich gesci a není tedy Ïádn˘m zpÛsobem centrálnû upraveno ãi korigováno, a mnohdy se ani pfiíli‰ neshoduje s centrálnû vymezen˘mi regiony se soustfiedûnou podporou státu, bylo pfiedmûtem zkoumání poslední stanovené hypotézy, zda dochází k upfiednostÀování podnikatelsk˘ch subjektÛ pfii schvalování Ïádostí o dotace v centrálnû vymezen˘ch problémov˘ch regionech oproti podnikatelsk˘m subjektÛm pÛsobícím v problémov˘ch oblastech vymezen˘ch ze strany samotn˘ch krajÛ. Na základû anal˘zy dat uveden˘ch v tabulce 3, která pfiedstavuje dal‰í modifikaci údajÛ z databáze (sídlo firmy v rámci jednotliv˘ch

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Ekonomie krajÛ), bylo moÏné porovnáním procentních podílÛ objemu dotací a poãtu projektÛ plynoucích do centrálnû vymezen˘ch problémov˘ch oblastí a do problémov˘ch oblastí vymezen˘ch nad rámec centrálních, potvrdit ãi vyvrátit poslední stanovenou hypotézu. Zatímco do problémov˘ch regionÛ vymezen˘ch kraji nad rámec centrálnû vymezen˘ch plynulo ve sledovaném období necel˘ch deset procent ve‰ker˘ch ãerpan˘ch dotací (680 mil. z cca 7 mld. Kã), v regionech se soustfiedûnou podporou státu dosahoval tento podíl v témÏe ãasovém úseku bezmála ãtyfiiceti procent (2,5 mld. z cca 7 mld. Kã). S tûmito procentními podíly objemÛ dotací se víceménû shodují podíly poãtu projektÛ, kter˘ch bylo v problémov˘ch regionech vymezen˘ch kraji „nad rámec“ pfiibliÏnû devût procent (130 z celkového poãtu 1 455 podpofien˘ch projektÛ), oproti témûfi pûtatfiiceti procentÛm podpofien˘ch projektÛ v centrálnû vymezen˘ch problémov˘ch oblastech (501 z celkového poãtu 1 455 podpofien˘ch projektÛ). Tfietí hypotéza o vût‰ím objemu ãerpan˘ch dotací podnikatelsk˘mi subjekty pÛsobícími v regionech se soustfiedûnou podporou státu oproti podnikatelsk˘m subjektÛm v problémov˘ch regionech vymezen˘ch kraji „nad rámec“ se tak potvrdila. Uvedené v˘sledky v˘zkumu sk˘tají prostor pro úvahy o dal‰ím smûfiování v˘zkumn˘ch prací. Pfiedmûtem v˘zkumu by mohla b˘t napfi. anal˘za provedená na úrovni jednotliv˘ch podnikÛ, jejichÏ projekty byly podpofieny, aÈ jiÏ ve formû dotací, úvûrÛ nebo záruk. Podrobnûji by bylo moÏné zkoumat, jaké podniky o získání podpory usilují, v jak˘ch odvûtvích tyto podniky pÛsobí a s jak˘mi bariérami se pfii Ïádosti o podporu setkávají.

Literatura [1] CzechInvest. Programy podpory. Podnikání a inovace [online]. Praha: Agentura pro podporu podnikání a investic, 2009 [cit. 2009-07-12]. Dostupné z: . Seznam PfiíjemcÛ ke dni 11.9.2008 – úvûry (oprava 26.3.2009). Dokument ve formátu PDF. Seznam PfiíjemcÛ ke dni 11.9.2008 – záruky. Dokument ve formátu PDF. Seznam PfiíjemcÛ ke dni 23.2.2009 – dotace. Dokument ve formátu PDF.

[2] HEIMPOLD, G. Growth versus equalisation? An examination of strategies for regional policy in the Czech Republic, Hungary and Poland after EU accesion. Jahrbuch für Regioanlwissenschaft. 2008, roã. 28, ã. 1, s. 1-29. ISSN 0173-7600. [3] HLAVÁâEK, P. The foreign direct investments in the Usti region: theory, actors and space differentiation. E+M Ekonomie a Management. 2009, roã. 12, ã. 4, s. 27-39. ISSN 1212-3609. [4] HRACH, K. Sbírka úloh ze statistiky. 1. vyd. Fakulta sociálnû ekonomická, Univerzita Jana Evangelisty Purkynû v Ústí nad Labem, 2006. 65 s. ISBN 80-7044-845-8. [5] CHAJDIAK, J. ·tatistika v Exceli 2007. Bratislava: STATIS, 2009. 304 s. ISBN 978-8085659-49-8. [6] Národní rozvojov˘ plán âeské republiky 2007–2013. Praha: Fondy Evropské Unie, 2006. Dostupn˘ také z WWW: . [7] Národní strategick˘ referenãní rámec âeské republiky 2007–2013. Praha: Ministerstvo pro místní rozvoj, 2007. Dostupn˘ také z WWW: . [8] NOEN, a.s. O spoleãnosti [online]. Praha: Open-cast mining technologies, 2009 [cit. 200908-03]. Dostupné z: . [9] Operaãní program Podnikání a inovace. Ministerstvo prÛmyslu a obchodu âR, Praha, 2007. [10] PAVELKOVÁ, D., JIRâÍKOVÁ, E. Klastry jako nástroj zv˘‰ení konkurenceschopnosti firem. E+M Ekonomie a Management. 2008, roã. 11, ã. 3, s. 62-72. ISSN 1212-3609. [11] SKOKAN, K. Konkurenceschopnost, inovace a klastry v regionálním rozvoji. 1. vyd. Ostrava: Repronis, 2004.159 s. ISBN 80-7329059-6. [12] STEJSKAL, J., CHARBURSK¯, M. Zku‰enosti ãesk˘ch regionÛ z ãerpání prostfiedkÛ ze strukturálních fondÛ EU. E+M Ekonomie a Management. 2005, roã. 8, ã. 4, s. 1. ISSN 1212-3609. [13] Strategie regionálního rozvoje âeské republiky. Ministerstvo pro místní rozvoj. Praha, 2006.

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Ekonomie [14] ·IPIKAL, M., PISÁR, P., URAMOVÁ, M. Support of innovation at regional level. E+M Ekonomie a Management. 2010, roã. 13, ã. 4, s. 74-85. ISSN 1212-3609. [15] URAMOVÁ, M., KOÎIAK, R. Regional disparities in Slovakia from the aspect of average nominal wage. E+M Ekonomie a management. 2008, roã. 11, ã. 2, s. 6-18. ISSN 1212-3609.

[16] ÎIÎKA, M., RYDVALOVÁ, P. Konkurenceschopnost a jedineãnost obce. Studie III – Identifikace dynamického rozvoje obce. Liberec, 2008. 217 s. V˘zkumn˘ projekt WD-30-07-1. Technická univerzita v Liberci, Ekonomická fakulta. ISBN 978-80-7372-423-8.

Ing. Katefiina Felixová, Ph.D. Univerzita Jana Evangelisty Purkynû Fakulta sociálnû ekonomická Katedra financí a úãetnictví [email protected]


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Ekonomie

Abstract EVALUATION OF THE ABSORPTION INTENSITY OF THE ENTREPRENEURIAL SUPPORT IN THE REGIONS FUNDED INTENSELY BY THE GOVERNMENT Katefiina Felixová The paper focuses on the use of financial sources from structural funds of European Union and with the focus on the operation programme of Entrepreneurship and Innovation in the context of the regional development support in the troubled regions. The intensity of the absorption of the entrepreneurial support is analysed according to the criteria set by the government and according to the troubled areas given by the individual regions of the Czech Republic. Whereas the regions supported by the government are given centrally within the Strategy of the regional development of the Czech Republic. The regional level of the troubled areas is set ad hoc. Thus one of the crucial issues of this paper is to find potential preferences of the local authorities when deciding about the applications for the entrepreneur subsidies, loans and guaranties. The detailed analysis focuses on subsidies as the only non-profit form of the support. Further question is whether the regions with the intense government support get enough subsidies from entrepreneurial bodies in comparison with other (non-troubled) areas in the Czech Republic. The key documents for the research were the lists of recipients of subsidies in the first half of programme term 2007–2013 published by the CzechInvest agency. In total there are more than three thousand approved projects. The analysis covered only those businesses which received the subsidies. This research sample comprised of almost fifteen hundred subjects. Given the fact that the Czech Ministry of Trade and Industry publishes the location exclusively on the regional level it was necessary to add the information about the municipality or the place of the business which received the subsidy, to the list of recipients of the subsidies. It was necessary to define the concerned areas on the district level or territorial areas of municipalities with extended competencies or territorial units of municipalities chartered by town halls. The data about municipalities was found in the trade register for each subsidised project separately. The relevant data concerning the research were found in the researcher’s database. Key Words: Regional development, regional policy, regional disparities, European Union, structural funds, operational programme, operational programme Enterprise and Innovation , business support, small and middle business, intensive government support of regions, structurally affected regions, economically weak regions, regions with the very high unemployment, region, district, municipality, Czech Republic, subsidies, guarantees, loans, competition. JEL Classification: R110, R280, R380, R580.

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Ekonomika a management

A MULTI-OBJECTIVE DECISION SUPPORT SYSTEM FOR PROJECT SELECTION WITH AN APPLICATION FOR THE TUNISIAN TEXTILE INDUSTRY Willem Karel M. Brauers, Edmundas Kazimieras Zavadskas

1. Tunisian Development Planning as Illustrated by its Textile Industry In this paper project selection is proposed as an answer to development planning. Project Selection assumes "that the project to be analysed will constitute a new economic activity…..in practice, however, many projects will only modify an existing economic activity" [42]. In addition different competing projects are considered and a final choice is made by Multiple Objective Optimization. Project Selection is subject of an evolution concerning the objectives to strive after. If before the stress was put on market analysis, Net Present Value, Internal Rate of Return and other micro-economic targets, macro-economic objectives receive more and more attention such as employment, value added and the influence on the balance of payments. Attention for social well being goes even a step further with for instance environment and pollution. Employment is a human right, sometimes even written down in national constitutions. In order to be more specific the Tunisian Textile Industry will be used as an illustration. Tunisia's industrial sector comprises 5,624 enterprises having 10 or more employees, including 2,095 enterprises in the textile and clothing industry (All figures for 2008 come from the Industry Promotion Agency and from Textile and Clothing Industries, Think Tunisia.). Of these 2,095 textile firms 1,752 work entirely for exportation; the remaining ones work for home consumption and exportation. In the subsector "manufacture of fabric and knitted wear"

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even 1,406 on 1,566 enterprises work totally for exportation. In the whole textile sector some 1,000 Tunisian companies work in partnership with EU companies: of which 365 are French, 206 Italian, 121 Belgian and 106 German. On a total of 478,608 employees in total industry 200,230 or 42 % work in the textiles and clothing industries. For Value Added it goes in the other direction. The Value Added in the textiles and clothing industries amounted to 1,610 billion dinars in 2008 against 9 billion for the whole industry. Consequently against a labor productivity of 18,805 dinars per employee in the whole Tunisian industry stands only a labor productivity of 8,041 dinars per employee in the textile industry. Anyway a correlation is observed between the low pay and the off shoring from the European countries for the Tunisian textile industry. Bergin et al. [2] remark if off shoring, meaning outsourcing but abroad, takes place between the United States and Mexico it is also the case between the European countries and the emerging countries and in global trade with China. Under these circumstances the Tunisian Government could promote textile firms with higher Value Added and lower employment, automatically meaning higher productivity. If in addition the government income could increase would be welcome too. Therefore a simulation will take into account multiple objectives even expressed in different units and facing different projects. Indeed, the Tunisian Ministry of Industry and Technology is always very active with enterprise creation under the form of an investor's guide, of launching project ideas, of legal assistance and of other forms of coaching (Industry Promotion Agency).

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2. A Simulation Exercise for Enlarged Project Selection Suppose the Government of Tunisia would have the choice to support one of three projects. The following objectives are proposed: 1) maximization of Net Present Value (NPV) at the end of the project period and expressed in money terms (in 1 million dinars): Net Present Value = discounted Revenues exclusive local and direct and indirect government taxes, inclusive rent on industrial land and depreciation, but minus investments; 2) maximization of the Internal Rate of Return (IRR) expressed as a % interest rate, considering NPV equal to zero at the end of the project period; 3) minimization of the Payback Period of NPV, expressed in years and months; Tab. 1:

4) maximization of Government Income: local and direct and indirect government taxes in 100,000 dinars; 5) maximizing direct and indirect local and national employment in person-years; indirect employment found by local and national input-output tables; 6) maximizing the increase in Gross Domestic Product in 1 million dinars; 7) minimization of the risk on 5) and 6) in %; 8) maximization of increase in 100,000 dinars in the Balance of Payments; 9) maximization of hard currency to be provided by foreign sources for investment, expressed in money terms (in million dinars). Table 1 presents the three projects.

Reaction of Three Projects on Nine Objectives for the Tunisian Textile Industry

1

2

NPV

IRR

3

4

5

MAX.

MAX.

MIN.

MAX

MAX.

A

1

14

9

200

600

B

1.6

16

7

150

800

C

2

17

5

80

1200

10

Pay-back Govern. Employm. Income

6

7

V. A.

Risk

8

9

MAX.

MIN.

MAX.

20

20

3.5

2.5

13.5

25

4

1.5

30

3.8

1.25

Bal.Paym. Investm. MAX.

Source: own

The whole exercise is linked to a simulation. Contrary to a lot of other definitions, simulation is defined here in a rather broad sense. Gordon et al. [19] give the most complete description of simulation as mechanical, metaphorical, game or mathematical analogs. They conclude: "are used where experimentation with an actual system is too costly, is morally impossible, or involves the study of problems which are so complex that analytical solution appears impractical". Project Management needs much more paper work than is shown here (see [42]). Brauers [13] made a pre-study for dyeing works in Tunisia as an example of application.

3. Why Using Multiple Objectives Optimization in Project Selection? Cost-Benefit Analysis is the traditional used method. Cost-Benefit takes a monetary unit as

the common unit of measurement for benefits and costs. In this way, cost-benefit presents a materialistic approach, whereby for instance unemployment and health care are degraded to monetary items. Multi-Objective Optimization will take care of the disadvantages of CostBenefit: the objectives can keep their own units. In order to define an objective better we have to focus on the notion of attribute. Keeney and Raiffa [23] present the example of the objective "reduce sulfur dioxide emissions" to be measured by the attribute "tons of sulfur dioxide emitted per year". An attribute should always be measurable. Simultaneously we aim to satisfy multiple objectives, whereas several alternative solutions or projects are possible, characterized by several attributes. An alternative should be quantitatively well defined. An attribute is a common characteristic of each alternative such as its economic, social, cultural or ecological significance, whereas an objective

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Ekonomika a management consists in the optimization (maximization or minimization) of an attribute. Economic Welfare (the term was invented by professor Pigou [31] comprises micro- and macroeconomics. Microeconomics would include attributes such as: yearly capacity to be reached, Net Present Value (NPV), Internal Rate of Return (IRR) and Payback Period. Macro-economics would include increase in GDP, surplus in the current account of the balance of payments, direct and indirect employment increase and ENPV. Indirect employment is measured by Input-Output techniques. ENPV means Economic Net Present Value, i.e. discounted revenues before national taxes, minus discounted investments, exclusive of subsidies. ENPV is different from GDP, but represents in macro-economics the counterpart of NPV, also with deduction of investments. Satisfaction of all stakeholders is still another series of objectives. Stakeholders mean everybody interested in a certain issue. Due to consumer sovereignty and the economic law of decreasing marginal utility, consumer surplus, level of salaries, leisure time and again employment at the local and national level have to be taken into consideration. Some attributes like NPV, ENPV, GDP, balance of payments surplus and consumer surplus are expressed in money terms, like dollars or Euros. However, a Euro in consumer surplus cannot be compensated for instance with a GDPEuro. In addition, IRR is expressed in a percentage, the payback period in months or years, employment in number of persons per year, production, for instance, in TEU, etc. Consequently, a serious problem of normalization is present. Normalization means reduction to a normal or standard state. However, the term got many interpretations but the stress is mainly put on the unification of diverting systems of measurement. As decision making is interested in measurement, normalization in technology is a main starting point, whereas scales of measurement and measurement of quality may be troublesome (for more on normalization, see: Brauers [9]).

4. Conditions of Robustness in Multi-Objective Methods For the researcher in multi-objective decision support systems the choice between many

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methods is not very easy. Indeed numerous theories were developed since the forerunners: Condorcet [15] (the Condorcet Paradox, against binary comparisons), Gossen [20] (law of decreasing marginal utility) Minkowski [27, 28] (Reference Point) and Pareto [30] (Pareto Optimum and Indifference Curves analysis) and pioneers like Kendall [24] (ordinal scales), Roy et al. [34] (ELECTRE), Miller and Starr [26] (Multiplicative Form), Hwang and Yoon [21] (TOPSIS), Saaty [35] (AHP) and Opricovic and Tzeng [29] (VIKOR). We intended to assist the researcher with some guidelines for an effective choice. Indeed, elsewhere we tried to define robustness in connection with multiple objectives [5] and seven conditions of robustness were set [6]. MOORA seemed to satisfy these seven conditions of robustness. The tests were made as non-subjective as possible, but as the authors of this article were involved in setting up the test, it seemed better to avoid any impression of favoritism. Therefore Chakraborty [16], as an outsider, could judge better about MOORA. Chakraborty [16] took up the seven conditions of robustness and checked six famous methods of Multi-Objective Decision Making for decision making in manufacturing. Table 2 shows the results.

5. The Data Assembled in a Matrix A matrix under the form of a table assembles the data with vertically numerous objectives, criteria (a weaker form of objectives) or indicators and horizontally alternative solutions like projects. The data originate from statistics, desk research, Project Engineering [41] or from simulated figures. In this way, alternatives, solutions or projects enter the response matrix as rows. When it concerns projects information has to be as complete as possible. Otherwise imagination has to be intensive eventually with the assistance of the Ameliorated Nominal Group Technique (see Appendix A). Some of the candidate alternatives are excluded if they do not respond to conditions concerning lower bounds or upper limits. All constraints concerning lower bounds or upper limits have to be hard constraints, which form a sine qua non for the acceptance of the candidate alternatives [38]. Distinction has to be made between qualitative and quantitative hard constraints. On the one hand, investments

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Ekonomika a management Tab. 2:

Comparative Performance of Some MODM Methods Computational time

Simplicity

Mathematical calculations

Stability

Information type

MOORA

Very less

AHP

Very high

Very simple

Minimum

Good

Quantitative

Very critical

Maximum

Poor

Mixed

Moderate

Moderately critical

TOPSIS

Moderate

Medium

Quantitative

VIKOR

Less

Simple

Moderate

Medium

Quantitative

ELECTRE

High

Moderately critical

Moderate

Medium

Mixed

PROMETHEE

High

Moderately critical

Moderate

Medium

Mixed

MODM

Source: own

Tab. 3:

Matrix of Responses obj.1

obj. 2

…

obj. i

…

obj. n

Alternative 1

X11

X21

Alternative 2

X12

X22

...

Xi1

...

Xn1

...

Xi2

...

....................

X...

X...

Xn2

...

X...

...

X...

Alternative j

X1j

X2j

...

Xij

...

Xnj

....................

X...

X...

...

X...

...

X...

Alternative m

X1m

X2m

...

Xim

...

Xnm Source: own

needed in a well-defined region and not in other regions, complete financial guaranties to be given for daughters of multinationals in case of failure, represent examples of qualitative hard constraints. On the other side, certain capacities in production not to be exceeded unless new investments are made. The World Bank granting a loan to a developing country unless for instance at least an Internal Rate of Return of 12 % is guaranteed, geometrical constraints under the form of a limiting line, surface or manifold, represent quantitative hard constraint examples.

6. How to Determine the Objectives? The question remains how to find and how to decide on the choice of the objectives. One decision maker like a captain of industry will focus on his own objectives. Different decision makers do not change the picture. In some industrial countries the large companies are

obliged to have in the board of directors some directors from outside the company. Even this group of decision makers will stick to their own limited objectives. For the choice of the objectives, certainly a necessity when the General Well Being is concerned, all stakeholders, which mean all persons interested in a certain issue, have to be involved. The choice of stakeholders in a Nominal Group Technique exercise for the Facilities Management Sector of Lithuania on October 15, 2002 forms an example of application. Fifteen delegates from the facilities sector itself, from the ministerial departments concerned and from the academic world came together during an afternoon to pronounce themselves about the objectives of the sector until 2012 [11]. The absence of a consumer representation formed a weak point, but at that time no representative consumer organization existed in Lithuania. Concerning the employees trade unions were not interested as the facilities sector in Lithuania is composed of only small firms. The

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Ekonomika a management more neutral academic representatives were assumed to represent the consumer interest. The original NGT as introduced by Van De Ven and Delbecq [37] delivered a total of 225 points. The total 225 is a control figure for the group result. Indeed, each participant could distribute maximum: 5+4+3+2+1 = 15 points. With 15 participants, the total has to be not more than 225. It could be less, as each participant is not obliged to allot 15 points. The total of the given points, here namely 225, means that each participant used his rights completely. Applying the Ameliorated Nominal Group Technique with the introduction of probabilities of realization, introducing a sense of reality and presenting a guaranty against wishful thinking, produces quite some changes in the ranking. This reality check diminished the total to 145 points. (Appendix A brings information on the Ameliorated Nominal Group Technique). For the choice of the objectives sometimes a general consensus is reached in the specialized literature or in the legislation. Once agreement reached about alternatives and objectives, a decision has to be taken how to read the Response Matrix (see table 3 above), either horizontally or vertically.

6.1 Horizontal Reading of the Response Matrix SAW and usual Reference Point Methods read the response matrix in a horizontal way. The Additive Weighting Procedure (MacCrimmon [25] which was called SAW, Simple Additive Weighting Method, by Hwang and Yoon [21]) starts from:

Traditional Reference Point Theory is nonlinear, whereas non-additive scores replace the weights. The non-additive scores take care of normalization and of importance. But being non-additive the comments on the weights adding to one and consequently creating a super-objective is absent here. With weights and scores importance of objectives is mixed with normalization. Indeed weights and scores are mixtures of normalization of different units and of importance coefficients.

6.2 Vertical Reading of the Response Matrix Vertical reading of the Response Matrix means that normalization is not needed as each column is expressed in the same unit. In addition if each column is translated into ratios dimensionless measures are created and the columns become comparable to each other. Indeed they are no more expressed in a unit. Different kind of ratios are possible but Brauers and Zavadskas [9] proved that the best one is based on the square root in the denominator. The Ratio System which forms the basis of the MOORA method follows the vertical reading of the matrix. Diagram I (Fig. 1) shows the exact relation between the two methods of MOORA and in addition to MULTIMOORA (MOORA plus the Full Multiplicative Form, method to be explained later).

7. Multi-Objective Optimization by Ratio Analysis (MOORA)

Max.Uj = w1x1j + w2x2j + ... + w1x1j + ... + wnxnj Uj = overall utility of alternative j with j = 1,2, …..,m, m the number of alternatives wi = weight of attribute i indicates as well as normalization as the level of importance of an objective i = 1,2,…..,n; n the number of attributes and objectives xij = response of alternative j on attribute i. As the weights add to one a new superobjective is created and consequently it gets difficult to speak of multiple objectives.

32

7.1 The Two Parts of MOORA The method starts with a matrix of responses of different alternatives on different objectives: (xij) with: xij as the response of alternative j on objective i i=1,2,…,n as the objectives j=1,2,…,m as the alternatives MOORA goes for a ratio system in which each response of an alternative on an objective

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Ekonomika a management Fig. 1:

Diagram of MULTIMOORA

Source: own

is compared to a denominator, which is representative for all alternatives concerning that objective. For this denominator the square root of the sum of squares of each alternative per objective is chosen. Brauers and Zavadskas [9] proved that this is the most robust choice:

(1)

with: xij = response of alternative j on objective i j = 1,2,...,m; m the number of alternatives i = 1,2,…n; n the number of objectives xij* = a dimensionless number representing the normalized response of alternative j on objective i. Dimensionless Numbers, having no specific unit of measurement, are obtained for instance by deduction, multiplication or division. The dimensionless responses of the alternatives on the objectives belong to the interval [0; 1]. However, sometimes the interval could be [-1; 1]. Indeed, for instance in the case of productivity growth some sectors, regions or countries may show a decrease instead of an increase in productivity i.e. a negative dimensionless number. For optimization these responses are added in case of maximization and subtracted in case of minimization:

(2)

with: i = 1,2,…,g as the objectives to be maximized; i = g+1, g+2,…, n as the objectives to be minimized; yj*= the normalized assessment of alternative j with respect to all objectives. A ranking of the yj * in a descending order will show the final preferences in MOORA. For the second part of MOORA the Reference Point Theory is chosen with the MinMax Metric of Tchebycheff as given by the following formula (Karlin and Studden [22])

  

  

(3)

with |ri – xij*| the absolute value if xij* is larger than ri for instance by minimization. This reference point theory starts from the ratios as defined in the MOORA method, namely formula (1). Preference is given to a reference point possessing as co-ordinates the dominating co-ordinates per attribute of the

33

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Ekonomika a management candidate alternatives and which is designated as the Maximal Objective Reference Point. This approach is called realistic and non-subjective as the co-ordinates, which are selected for the reference point, are realized in one of the candidate alternatives. The alternatives A (10; 100), B (100; 20) and C (50; 50) will result in the Maximal Objective Reference Point Rm (100; 100). Finally the Minima obtained by the Reference Point Method are ranked in an ascending order.

7.2 The Importance Given to an Objective by the Attribution Method in MOORA It may look that one objective cannot be much more important than another one as all their ratios are smaller than one (see formula 1) Nevertheless it may turn out to be necessary to stress that some objectives are more important than others. In order to give more importance to an objective its ratios could be multiplied with a Significance Coefficient. In the Ratio System to give more importance to an objective its response on an alternative under the form of a dimensionless number could be multiplied with a Significance Coefficient:

(2 bis)

with: i = 1,2,…,g as the objectives to be maximized. i = g+1, g+2,…, n as the objectives to be minimized. si = the significance coefficient of objective i. ÿj* = the total assessment with significance coefficients of alternative j with respect to all objectives. For the Reference Point Approach the place of the significance coefficient would be: | sjr – sixij* | The method with Significance Coefficients has to be based on a Delphi exercise with all the stakeholders in order to determine the importance of the objectives (for the Delphi Method see Appendix B).

34

One could think of aggregating all possible dimensionless methods in a single multiobjective system, for instance called MULTIMOORA. In this way MULTIMOORA would become the fulfillment of the seven robustness conditions on the basis of more than two methods.

8. MULTIMOORA MULTIMOORA is composed of MOORA and of the Full Multiplicative Form of Multiple Objectives. MULTIMOORA becomes a very robust system of multiple objectives optimization under condition of support from the Ameliorated Nominal Group Technique and Delphi (see Appendices A and B).

8.1 MOORA MOORA (Multi-Objective Optimization by Ratio Analysis) was explained under point 7.1 above.

8.2 The Full Multiplicative Form of Multiple Objectives Economics is familiar with the multiplicative models like in production functions (e.g. CobbDouglas and Input-Output formulas, Brauers [12]) and demand functions (Teekens and Koerts [36]), but the multiplicative form for multiobjectives was introduced by Miller and Starr in 1969 [26] and further developed by Brauers in 2004 [10]. The following n-power form for multiobjectives is called from now on a fullmultiplicative form in order to distinguish it from the mixed forms: (4)

with: j = 1, 2,...,m; m the number of alternatives; i = 1, 2,…, n; n being the number of objectives; xij = response of alternative j on objective i (xij = 0 means that an objective is not present in an alternative. A foregoing filtering stage can prescribe that an alternative with a missing objective to be maximized is withdrawn from the beginning. Otherwise for the calculation of a maximum the zero factor is just left out. A zero in a minimization problem is much more

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Ekonomika a management complicated. A real zero factor, like in the case of the absence of pollution, has to maintain its influence. Therefore the zero factor will receive an extremely low symbolic value like 0.01. If the zero factor means missing information then the situation is different and will ask for a serious correction. A correction factor has to be introduced being a bit larger than the corresponding factors of the other alternatives, for instance next ten, next hundred etc. With factors 8 and 11 next ten will be 20. With factors 80 and 110 next hundred will be 200 etc. Pollution can even be negative for a country which can offer drawing rights on pollution. However the situation can then be reversed. If pollution has to be minimized the possession of drawing rights can be maximized.) Uj = overall utility of alternative j. The overall utilities (Uj), obtained by multiplication of different units of measurement, become dimensionless. How is it possible to combine a minimization problem with the maximization of the other objectives? Therefore, the objectives to be minimized are denominators in the formula: (5)

with:

j = 1,2,...,m; m the number of alternatives; i = the number of objectives to be maximized

with:

n-i = the number of objectives to be minimized; with: Uj' : the utility of alternative j with objectives to be maximized and objectives to be minimized. The Full Multiplicative Form is read horizontally in the Response Matrix of Table 3 (see above). Nevertheless with the full-multiplicative form, the overall utilities, obtained by multiplication

of different units of measurement, become dimensionless measures. This situation would not bias the outcomes amidst the several alternatives as the last ones are represented by dimensionally homogeneous equations, being: "formally independent of the choice of units" [18]. Additionally, any attribute of size 10exp can be replaced by size 1 without changing the relation between the alternatives and consequently with no influence on their rankings (proved by Miller and Star [26]; also Brauers [10]). Does it mean that importance to an objective can not be given in the Full Multiplicative Form? Stressing the importance of an objective can be done by allocating an exponent (a Significance Coefficient) on condition that this is done with unanimity or at least with a strong convergence in opinion of all the stakeholders concerned (see Appendix B). A ranking of the Uj' in a descending order will show the final preferences in the Full Multiplicative Form.

8.3 MULTIMOORA as a Synthesis of the Results of the Ratio System, the Reference Method and the Full Multiplcative Form The three methods of MULTIMOORA are assumed to have the same importance. Stakeholders or their representatives like experts may give a different importance to objectives but this is not the case with the three methods of MULTIMOORA. These three methods represent all existing methods with dimensionless measures in multi-objective optimization and one is not better than the other. Consequently, all the three have the same significance of importance. Using for MULTIMOORA the total of the ranks of the ratio system, of the reference point and of the multiplicative form would mean working ordinal and not cardinal. Indeed, preference for cardinal numbers is rather based first on the saying of arrow [1]: “Obviously, a cardinal utility implies an ordinal preference but not Vice Versa” and second on the fact that the four essential operations of arithmetic: adding, subtracting, multiplication and division are only reserved for cardinal numbers (see Brauers and Zavadskas [4]; Brauers and Ginevicius [6]; Brauers [8]). In the most of the not too complicated cases a synthesis of the ranking of the three

35

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Ekonomika a management MULTIMOORA methods can be made. For very large matrices Brauers et al. developed a Theory of Dominance [3], [4]. Finally the results of MULTIMOORA, found as a synthesis, are ranked in a descending order.

which means that a multi-objective ranking has to bring the solution. Project A is the best for higher Value Added and lower employment, automatically meaning higher productivity. In addition the government income increase is welcome too. Project C is condemned as labor productivity is very low due to high employment and low Value Added. Project B shows an in between solution. Table 4 gives the reaction of the projects on the objectives after the MULTIMOORA approach, a synthesis of the three methods.

8.4 MULTIMOORA as applied for the Tunisian Textile Projects Appendices C and D give the detailed tables for MOORA and the Multiplicative Form concerning the projects for the Tunisian Textile Industry. Neither Project A, B or C is overall dominating, Tab. 4:

The Reaction of the Projects on the Objectives after the MULTIMOORA Approach

Projects

MOORA Ratio System

MOORA Reference Point

Multiplicative Form

MULTIMOORA

A

1

2

1

1

B

2

1

2

2

C

3

3

3

3 Source: own

As in MULTIMOORA an equal importance is given to each of the three methods, then A is general dominating B on two of the three methods. B takes an in between solution. Project C comes in the last position in spite of its favorable employment total.

Conclusion For a researcher in multi-objective decision support systems the choice between many methods for multi-objective optimization is not very easy. We intended to assist the researcher with some guidelines for an effective choice. In order to distinguish the different multi-objective methods from each other we use a qualitative definition of robustness, with an outsider judging favorably on MULTIMOORA, the method which was applied for a simulation on the Tunisian Textile Industry. Multi-Objective Optimization by Ratio Analysis (MOORA), composed of two methods: ratio analysis and reference point theory starting from the previous found ratios, solves the difficult problem of normalization whereas the importance of the objectives is treated separately. If MOORA is joined with the Full Multiplicative Form for Multiple Objectives

36

a total of three methods is formed under the name of MULTIMOORA, a mighty instrument for Multi-Optimization in a Well Being Society. In addition if MULTIMOORA is joined with the Ameliorated Nominal Group Technique and with Delphi the most robust approach exists for multi-objective optimization up to now. If the Simulation Exercise on the Tunisian Textile Industry has no practical consequences, in any case it provides a learning experience with MULTIMOORA in its triple composition.

References [1] ARROW, K. J. General Economic Equilibrium: Purpose, Analytic Techniques, Collective Choice. American Economic Review. 1974, Vol. 64, Iss. 3, pp. 253-72. ISSN 0002-8282. [2] BERGIN, P. R., FEENSTRA, R. C., HANSON, G. H. Offshoring and volatility: evidence from Mexico’s Maquiladora industry. American Economic Review. 2009, Vol. 99, Iss. 4, pp. 1664-1671. ISSN 0002-8282. [3] BRAUERS, W. K. M., BALEZENTIS, A., BALEZENTIS, T. MULTIMOORA fort the EU Member States updated with Fussy Number Theory. Technological and Economic Development of

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Ekonomika a management Economy. 2011, Vol. 17, Iss. 2, pp. 259-290. ISSN 2029-4913. [4] BRAUERS, W. K. M., ZAVADSKAS, E. K. MULTIMOORA optimization used to decide on a bank loan to buy property. Technological and Economic Development of Economy. 2011, Vol. 17, Iss.1, pp.174-188. ISSN 2029-4913. [5] BRAUERS, W. K. M., ZAVADSKAS, E. K. [Chap.] 2., Is Robustness Really Robust? Robustness from the Point of View of Statistics and Econometrics with an Application for Multi-Objective Optimization. In ZOPOUNIDIS, C. et al. Multiple Criteria Decision Aiding. Nova Science Publishers Inc., 2010, pp. 17-42. ISBN 978-1-61668-231-6. [6] BRAUERS, W. K. M., GINEVICIUS, R. Robustness in Regional Development Studies, the Case of Lithuania. Journal of Business Economics and Management. 2009, Vol. 10, Iss. 2, pp. 121-140. ISSN 1611-1699. [7] BRAUERS, W. K. M. Group Decision Making with Multi-Objective Optimization. Foundations of Computing and Decision Sciences. 2008, Vol. 33, Iss. 2, pp.167-179. ISSN 0867-6356. [8] BRAUERS, W. K. M. What is meant by normalization in decision making? International Journal of Management and Decision Making. 2007, Vol. 8, Iss. 5/6, pp. 445-460. ISSN 1462-4621. [9] BRAUERS, W. K. M., ZAVADSKAS, E. K. The MOORA method and its application to privatization in a transition economy. Control and Cybernetics. 2006. Vol.35, Iss. 2, pp. 443-468. ISSN 0324-8569. [10] BRAUERS, W. K. M. Optimization Methods for a Stakeholder Society, a Revolution in Economic Thinking by Multi-Objective Optimization. Boston: Kluwer Academic Publishers, 2004. 342 p. ISBN 1-4020-7681-9. [11] BRAUERS, W. K. M., LEPKOVA, N. The application of the nominal group technique to the business outlook of the facilities sector of Lithuania over the period 2003-2012. International Journal of Strategic Property Management. 2003, Vol. 7, Iss.1, pp. 1-9. ISSN 1648-715X. [12] BRAUERS, W. K. M. Prévisions Economiques ⁄ l’aide de la Méthode Entrées-Sorties. Paris: Economica, 1995. 111 p. ISBN 2-7178-2939-3. [13] BRAUERS, W. K. M. Multiple Criteria Decision Making in Industrial Project Management. Engineering Costs and Production Economics. 1990, Vol. 20, pp. 231-240. ISSN 0925-5273. [14] BRAUERS, W. K. M. Nominal methods in group multiple decision making. Research Paper No 3, Institute for Development Countries, University of Antwerpen, 1987, RUCA, Antwerpen.

[15] CONDORCET, J.-A.-N. Essai sur l’application de l’analyse ⁄ la probabilité des décisions rendues ⁄ la pluralité des voix, Paris, 1785, l’Imprimerie royale. [16] CHAKRABORTY, S. Applications of the MOORA method for decision making in manufacturing environment. The International Journal of Advanced Manufacturing Technology. 2011, Vol. 54, Iss. 9-12, pp. 1155-1166. ISSN 0268-3768. [17] DALKEY, N., HELMER, O. An Experimental Application of the Delphi Method to the use of Experts. Management Science. 1963, Vol. 9, Iss. 3, pp. 458-487. ISSN 0025-1909. [18] DE JONG, F. J. Dimensional Analysis for Economists. Amsterdam: North-Holland, 1967. 223 p. [19] GORDON, T. J., ENZER, S., ROCHBERG, R. An Experiment in Simulation Gaming for Social Policy Studies. Technological Forecasting. 1970, Vol. 1, pp. 241. [20] GOSSEN, H. H. Entwicklung der gesetze des Menschlichen Verkehrs und der daraus Flieszenden Regeln für Menschliches Handeln. 3 Auflage, 1853, Prager, Berlin, 1927. [21] HWANG, C.-L., and YOON, K. Multiple Attribute Decision Making, Methods and Applications: Lecture Notes in Economics and Mathematical Systems. Berlin: Springer, 1981. 259 p. ISBN 0387105581. [22] KARLIN, S., STUDDEN, W. J. Tchebycheff Systems: with Applications in Analysis and Statistics. 1st ed. New York: Interscience Publishers, 1966. 586 p. [23] KEENEY, R. L., RAIFFA, H. Decisions with Multiple Objectives. Preferences and Value Tradeoffs. Cambridge University Press, 1993. 592 p. ISBN 0521438837. [24] KENDALL, M. G. Rank Correlation Methods. London: Griffin, 1948. [25] MACCRIMMON, K. R. Decision Making among Multiple Attribute Alternatives. A Survey and Consolidated Approach, RM-4823- ARPA, the Rand Corporation, Santa Monica (CAL), 1968. [26] MILLER, D. W., STARR, M. K. Executive Decisions and Operations Research. 2nd ed. Englewood Cliffs (NJ): Prentice-Hall Inc., 1969, pp. 237-239. ISBN 013294538X. [27] MINKOWSKY, H. Geometrie Der Zahlen, Leipzig: Teubner, 1896. [28] MINKOWSKY, H. Gesammelte Abhandlungen, Teubner, Leipzig, 1911. [29] OPRICOVIC, S., TZENG, G-H. Compromise solution by MCDM methods: a comparative analysis of VIKOR and TOPSIS. European Journal of

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Ekonomika a management Operational Research. 2004, Vol. 156, Iss. 2, pp. 445-455. ISSN 0377-2217. [30] PARETO, V. Manuale di Economia Politica. Translation revised by Pareto Himself: Manuel d’économie politique, 1906, 2nd ed. Paris,1927. [31] PIGOU, A. C. The Economics of Welfare. 4th ed. London: Macmillan, 1946. [32] QUADE, E. S. Cost-Effectiveness: Some Trends in Analysis. Rand Corporation, P-3529-1, Santa Monica (CAL), 1970. [33] QUADE, E. S., BOUCHER W. I. Systems Analysis and Policy Planning: Applications in Defense. New York: Elsevier, 1968. [34] ROY, B., BENAYOUN, R., SUSSMAN, B. ELECTRE. Société d’Economie et de Mathématique appliquées, Paris, 1966. [35] SAATY, T. L. The Analytic Hierarchy Process. New York: Mcgraw-Hill, 1988. [36] TEEKENS, R., KOERTS, J. Some Statistical Implications of the Log Transformation of Multiplicative Models. Econometrica. 1972, Vol. 40, Iss. 5, pp. 793-819. ISSN 0012-9682. [37] VAN DE VEN, A. H., DELBECQ, A. L. Nominal Versus Interacting Group Processes for Committee Decision Making Effectiveness. The Academy of Management Journal. 1971, Vol. 14, Iss. 2, pp. 203-212. ISSN 0001-4273 [38] WIERZBICKI, A. P., MAKOWSKI, M. MultiObjective Optimization in Negotiation Support, WP-92-007, IASA, Luxemburg, 1992.

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[39] Industry Promotion Agency. Ministry of Industry and Technology. Republic of Tunisia [online]. [cit. 2011-08-08]. Available from: <www.tunisieindustrie.nat.tn>. [40] Textile & Clothing Industries. Think Tunisia. [online]. [cit. 2011-01-30]. Available from: <www.thinktunisia.tn>. [41] UNIDO. United Nations Industrial Development Organization Manual for the preparation of Industrial Feasibility Studies, United Nations, New York, 1978. [42] United Nations, United Nations Industrial Development Organization. Guidelines for Project Evaluation, New York, 1972.

Willem Karel M. Brauers*(Prof.) Vilnius Gediminas Technical University [email protected] Edmundas Kazimieras Zavadskas (Prof.) Internet and Intellectual Technologies Institute Vilnius Gediminas Technical University [email protected]


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Abstract A MULTI-OBJECTIVE DECISION SUPPORT SYSTEM FOR PROJECT SELECTION WITH AN APPLICATION FOR THE TUNISIAN TEXTILE INDUSTRY Willem Karel M. Brauers, Edmundas Kazimieras Zavadskas A developing country like Tunisia needs development planning but it will have problems with a top down strategy. As an answer to this problem the paper proposes a Multi-Objective Decision Support System for Project Selection. Project Selection is subject to an evolution concerning the objectives to strive after. If before the stress was put on market analysis, Net Present Value, Internal Rate of Return and other micro-economic targets, macro-economic objectives receive more and more attention such as employment, value added and the influence on the balance of payments. Employment is a human right, sometimes even written down in national constitutions. Traditional Cost-Benefit does not respond to these purposes. Indeed in Cost-Benefit all benefits (objectives) have to be translated into money terms, leading sometimes to immoral consequences. On the contrary Multi-Objective Optimization takes care of different objectives, whereas the objectives keep their own units. However different methods exist for the application of MultiObjective Optimization. These methods were tested after their performance. MOORA (MultiObjective Optimization by Ratio analysis) and MULTIMOORA (MOORA plus a Full Multiplicative Form), showed positive results; the more if they were assisted by Ameliorated Nominal Group and Delphi Techniques. A simulation exercise for Tunisia illustrates the use of these methods. The needs of the Tunisian textile industry are analyzed and as an answer three projects facing multiple objectives are simulated. Key Words: Project Selection, Cost-Benefit, Robustness, Multi-Objective Optimization, Ameliorated Nominal Group and Delphi Techniques, Full Multiplicative Form, MOORA, MULTIMOORA. JEL Classification: C44, O14, O16, O22.

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Appendix A The Ameliorated Nominal Group Technique as a source for objectives A.1 The Original Nominal Group Technique of Van de Ven and Delbecq (1971) A group of especially knowledgeable individuals representing all the stakeholders, is formed, which comes together in a closed meeting. A steering panel or a panel leader leads the group. The nominal group technique consists of a sequence of steps, each of which has been designed to achieve a specific purpose. 1) The steering group or the panel leader carefully phrases as a question the problem to be researched. Much of the success of the technique hinges around a well-phrased question. Otherwise the exercise can easily yield a collection of truisms and obvious statements. A successful question is quite specific and refers to real problems. The question has to have a singular meaning and a quantitative form as much as possible. 2) The steering group or the panel leader explains the technique to the audience. This group of participants is asked to generate and write down ideas about the problem under examination. These ideas too have to have a singular meaning and a quantitative form as much as possible. Participants do not discuss their ideas with each other at this stage. This stage lasts between five and twenty minutes. 3) Each person in round-robin fashion produces one idea from his own list and eventually gives further details. Other rounds are organized until all ideas are recorded. 4) The steering group or the panel leader will discuss with the participants the overlapping of the ideas and the final wording of the ideas. 5) The nominal voting consists of the selection of priorities, rating by each participant separately, while the outcome is the totality of the individual votes. A usual procedure consists of the choice by each participant of the n best ideas from his point of view, with the best idea receiving n points and the lowest one point. All the points of the group are added up. A ranking is the democratic result for the whole group. A.2 The Ameliorated Nominal Group Technique of Brauers (1987) 6) Out of experience, one may say that there is still much wishful thinking, even between the different stakeholders. Therefore the stakeholders were also questioned about the probability of realization of each objective. In this way they became more critical even about their own ideas. The probability of the group is found as the median of the individual probabilities. 7) Finally, the group rating (R) is multiplied with the group probability (P) in order to obtain the effectiveness rate of the event (E): RxP=E Once again, the effectiveness rates of the group are ordered by ranking. Experience proves that the introduction of probabilities decreases significantly the total number of points.

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Appendix B The Delphi Technique to Determine the Importance of an Objective Delphi, so named after the Greek oracle, was first thought of as a tool for better forecasting. In this sense, it seems that the first experiments took place around 1948 [33]. Today Delphi is no longer limited to forecasting alone. Dalkey and Helmer at RAND Corporation first used Delphi in its present form around 1953 [17]. The Delphi Method is a method for obtaining and processing judgmental data. It consists of a sequenced program of interrogation (in session or by mail) interspersed with feedback of persons interested in the issue, while everything is conducted through a steering group. The essential features of Delphi are the following: 1) the rather vague notion "persons interested in the issue" is interpreted by Quade as follows: "In practice, the group would consist of experts or especially knowledgeable individuals, possibly including responsible decision makers" [32]; 2) the steering group treats anonymously the sources of each input; 3) inputs must as much as possible possess a single meaning and a quantitative form. The inputs with these characteristics are elicited with feedback in a series of rounds; 4) opinions about the inputs are evaluated with statistical indexes such as median and quartiles; 5) there is also a feedback of the statistical indexes with a request for re-estimation after consideration of reasons for extreme positions. The practice of Delphi reveals that after several rounds convergence is shown between the various opinions (one of the main advantages of the Delphi method); 6) there are two developments of Delphi: one is based on a meeting, the other on the sending of questionnaires. The organization of a meeting produces quicker results; the meeting, however, has to be organized in such a way that communication between the panel members is impossible. In order to increase even further the speed of the outcome of a meeting, an on-line computer could be installed. Everybody involved in the Delphi teamwork would have a desk terminal linked to a computer and would be able to look at a television screen giving the results calculated by the computer. Convergence in opinion among the stakeholders to give more importance to an objective results from a Delphi exercise. Therefore, this exercise could provide the given objective with a Significance Coefficient. For instance, giving a significance coefficient to pollution abatement, the stakeholders are asked to give the following importance to pollution abatement: 0, 1, 2 or 3 Suppose that after several rounds convergence is reached on 3 (for an example concerning voting by a jury, see [7]).

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Appendix C Simulation of Project Planning by MOORA Simulation for the Ratio System (5a until 5c) and for Reference Point (5d-5e) of MOORA

Tab. 5:

5a – Matrix of Responses of Alternatives on Objectives: (xij) 1

2

3

4

5

6

7

NPV

IRR

Pay-back

Govern. Income

Employm.

V. A.

Risk

8

9

Bal.Paym. Investm.

MAX.

MAX

MIN.

MAX

MAX.

MAX.

MIN.

MAX.

MAX.

A

1

14

9

200

600

20

20

3.5

2.5

B

1.6

16

7

150

800

13.5

25

4

1.5

C

2

17

5

80

1200

10

30

3.8

1.25

5b – Sum of Squares and their Square Roots A

1

196

81

40,000

360,000

400

400

12.25

6.25

B

2.56

256

49

22,500

640,000

182.25

625

16

2.25

C

4

289

25

6,400

1,440,000

100

900

14.44

1.56

∑

7.56

741

155

68,900

2,440,000

682.25

1925

42.69

10.06

27.221

12.45

262.4881

1562.05

26.12

43.875

6.533758

3.1721444

root 2.749545

5c – Objectives Divided by their Square Roots and MOORA sum 0.384

0.536

0.788

rank

A

0.363696

0.5143

0.7229

0.761939

0.766

0.4558

2.93480

1

B

0.581914

0.5878

0.5623

0.571454 0.51215

0.5168

0.5698

0.612205 0.4728662

2.723

2

C

0.727393

0.6245

0.4016

0.304776 0.76822

0.3828

0.6838

0.581595 0.3940552

2.698

3

5d – Reference Point Theory with Ratios: co-ordinates of the reference point equal to the maximal objective values ri

0.727393 0.6245

0.4016 0.761939 0.76822

0.766

0.4558 0.612205 0.788110

5e – Reference Point Theory: Deviations from the reference point

42

A

0.364

0.1102

0.3213

B

0.145479

0.0367

0.1606

C

0

0

0

0

0.38411

rank min.

0.38411

2

0

0

0.0765

0.190485 0.25607

0.2489

0.1140

0

0.3152 0.3152442

1

0.457164

0.3828

0.2279

0.0306

0.3941 0.4571636

3

0

0

max.

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Appendix D Simulation of Project Planning by the full Multiplicative Form Tab. 6:

The Full Multiplicative Form 1

2

3

4

5

6

MIN.

7

MAX.

MAX.

Projects

NPV

IRR

3=1x2

Payback

5=3:4

Gov.Y

MAX. 7 = 5x6

A

1

14

14

9

1.55555556

200

311.111111

8

9

MAX. Employm. 9 = 7 x 8 600

186666.667

B

1.6

16

25.6

7

3.65714286

150

548.571429

800

438857.143

C

2

17

34

5

6.8

80

544

1200

652800

10

11

12

13

14

15

16

17

18

MAX.

MIN.

MAX.

MAX.

VA

11= 9 x10

Risk

13= 11:12

B. of P.

15= 13x14

Investm.

17= 15x16

Result

Projects

20

3733333.33

20

186666.667

3.5

653333.333

2.5

1633333

1

A

13.5

5924571.43

25

236982.857

4

947931.429

1.5

1421897

2

B

10

6528000

30

217600

3.8

826880

1.25

1033600

3

C

43

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·TRUKTÚRA NÁKLADOV KVALITY A CITLIVOSË PODNIKOV NA V¯KYVY EKONOMIKY Martin Mizla, Patrycja Pud∏o

Úvod Kvalita je jedn˘m z hlavn˘ch faktorov, ktor˘ vpl˘va na rozhodovanie zákazníka pri nákupe a voºbe produktov. Podnik, ktor˘ chce dlhodobo podnikaÈ, potrebuje zabezpeãiÈ vysokú kvalite svojich produktov a neustále zvy‰ovaÈ alebo aspoÀ udrÏiavaÈ svoju pozitívnu reputáciu. Kvalita by tak mala byÈ zárukou prosperity podniku zvy‰ovaním objemu predaja a ãiastoãne aj trÏieb. Zvy‰ovanie a zabezpeãovanie kvality je v‰ak s generovaním nákladov na zabezpeãenie a udrÏanie kvality. V praxi sa podniky prioritne orientujú buì na zniÏovanie celkov˘ch nákladov alebo na zvy‰ovanie ãi zabezpeãenie v˘‰ky trÏieb. Prístup zniÏovania nákladov (costs emphasis) je napæÀan˘ zniÏovaním nákladov na kvalitu predov‰etk˘m vy‰‰ou prevenciou a niÏ‰ím poãtom intern˘ch a extern˘ch ch˘b. Prístup zvy‰ovania trÏieb (revenue emphasis) vyuÏíva práve reputáciu na zvy‰ovanie objemu predaja a získavanie väã‰ieho trhového podielu. Vy‰‰ia kvalita, ako uÏ bolo spomenuté, má zabezpeãiÈ dlhodob˘ rozvoj podniku. Súãasná hospodárska kríza, ktorá sa zaãala v USA, v‰ak v krátkodobom horizonte ukázala, Ïe ohrozenú existenciu majú prekvapujúco aj podniky, ktoré kvalitu nielen formálne deklarujú, ale sa kvalitou zaoberajú poctivo a dlhodobo. To znamená, Ïe operujú v prostredí s vysok˘m operaãn˘m rizikom. âlánok podáva vysvetlenie príãin uvedeného javu a zároveÀ predstavuje prepojenie prístupov (náklady – trÏby) do uceleného prístupu dual emphasis. Prepojenie oboch princípov na jednej strane umoÏÀuje podnikom predikovaÈ moÏné externé dopady v˘kyvov ekonomiky a takto urãovaÈ citlivosÈ podnikov na ne. Na druhej strane umoÏÀuje riadiÈ alokáciu nákladov na kvalitu podºa stavu ekonomiky. To si vyÏaduje zmenu existujúceho triedenia nákladov na

44

kvalitu a ich preklasifikovanie. Nové triedenie umoÏÀuje ukázaÈ ako zmeny v ekonomike (chápané ako zmeny v dopyte) a ‰truktúra nákladov na kvalitu vpl˘vajú na operaãn˘ zisk (EBIT).

1. Podstata a triedenie nákladov kvality KaÏd˘ podnik sa usiluje o rozpoznanie nákladov spojen˘ch s v˘robn˘m procesom, marketingom, riadením ºudsk˘ch zdrojov, projektovaním a zlep‰ením produktu. Do päÈdesiatych rokov minulého storoãia klasifikácia nákladov nezah⁄Àala náklady na kvalitu, v˘nimkou boli iba náklady na kontrolu a testovanie [15]. V podmienkach stále rastúcej konkurencie na trhu hºadajú domáce a zahraniãné podniky rie‰enia, ktoré im pomôÏu udrÏaÈ na trhu. Známy je aforizmus Ïe „kvalita nieão stojí, lenÏe nedostatok kvality stojí oveºa viac“ [20]. Náklady na kvalitu podºa Jurana sú „zlatou baÀou“. ZdôrazÀuje, Ïe cieºom kaÏdej firmy by mala byÈ minimalizácia nákladov spojen˘ch s nízkou kvalitou, hºadanie zdrojov, teda miest tvorby nekvality a ich odstraÀovanie. V literatúre t˘kajúcej sa kvality stretávame veºa definície a modelov nákladov na kvalitu. V súãasnosti neexistuje jeden univerzálny vzorec nákladov na kvalitu, ktor˘ by zah⁄Àal univerzálne poloÏky t˘chto nákladov. Vypl˘va to zo ‰pecifikácie podniku a jeho procesov [17], lebo tieto náklady vznikajú na rôznych miestach, ktoré je pre podnik ãasto ÈaÏké definovaÈ [25]. Slovník EOQ (European Organization for Quality) za náklady na kvalitu povaÏuje v˘davky vynaloÏené v˘robcom, pouÏívateºom a spoloãnosÈou na kvalitu produktu resp. sluÏby [27]. Ilustratívne zosumarizované najroz‰írenej‰ie triedenia nákladov na kvalitu podºa rôznych autorov a organizácií je uvedené v tab. 1.

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Chápanie nákladov na kvalitu z pohºadu rôznych autorov J. Juran

•

•

•

J. Bank

Náklady na hodnotenie, ktoré zah⁄Àajú • v˘skumy, anal˘zy, kontrolu a v nej kontrolu dodávok, kontrolu aparatúry a meracích • prístrojov, pouÏité materiály, audit Náklady na externé chyby, ktoré obsahujú neopraviteºné chyby, opravy, stratu v dodávkach, anal˘zu ch˘b a nedostatkov • Náklady na externé chyby, ktoré obsahujú chyby vo v˘robe, technológie, záruky, anal˘zu ch˘b [13]. P. Crossby

•

•

A. Iwasiewicz

Náklady zhodnosti, ktoré zah⁄Àajú v˘davky • vynaloÏené na zabezpeãenie a adaptáciu v˘robku do potrieb. • Náklady nezhodnosti, teda v˘davky, ktoré sa t˘kajú ch˘b [4].

Náklady v norme BS 6143. 1. náklady na prevenciu, hodnotenie a chyby a) náklady na prevenciu, b) náklady na chyby, c) náklady ch˘b, 2. náklady procesu, a) náklady splnenia poÏiadaviek, b) náklady nesplnenia poÏiadaviek [31, s. 37]. Náklady na kvalitu podºa ASQC • náklady preventívnych ãinností • náklady hodnotenia kvality, • náklady niÏ‰ej kvality (interné), • náklady niÏ‰ej kvality (externé) [30]. V 90. rokoch XX. storoãia pridaná e‰te jedna poloÏka, tzv. náklady straten˘ch príleÏitostí [3].

Náklady zhodnosti, ktoré sa trieda na náklady prevencie a ceny Náklady nezhodnosti, ktoré sa trieda na náklady vnútorn˘ch a vonkaj‰ích ch˘b, a zároveÀ náklady spojené s prekroãením poÏiadaviek, Náklady straten˘ch moÏností [2].

Náklady na riadenie, teda náklady na prevenciu a náklady v˘skumu a hodnotenia Náklady na chyby a v nich straty spôsobené chybami extern˘mi a intern˘mi [11].

Náklady v TQM 1. náklady zhodnosti: a) náklady na prevencie • ‰kolenie zamestnancov, • spracovávanie a implementácia procedúr b) náklady na kontrolu a in‰pekciu 2. náklady nezhodnosti a) náklady opraviteºn˘ch v˘robkov • chyby interné pred dodaním v˘robku zákazníkovi, • chyby externé po dodaní v˘robku zákazníkovi b) náklady spojené so zv˘‰ením v˘roby • kvôli cenám, • kvôli likvidácii, c) náklady neopraviteºn˘ch v˘robkov, d) súdne konanie, od‰kodnenie, 3. náklady straten˘ch moÏností a) stratenie trhu, b) stratenie objednávky a trÏby, c) niÏ‰ie ceny, d) vy‰‰ie náklady spojené s predajom. Zdroj: vlastné spracovanie

ZároveÀ treba podotknúÈ, Ïe v literatúre sa stretávame s dvoma prístupmi k deleniu nákladov na kvalitu. Prv˘ prístup k nákladom na kvalitu zah⁄Àa náklady na prevenciu, hodnotenie a chyby. Druhou koncepciou je tzv. PQC – poor quality costs [9], ktorá väã‰inou triedi náklady na kvalitu na náklady na chyby a náklady na

udrÏanie kvality. V tejto koncepcii sa nezohºadÀujú náklady na prevenciu. Príãinou nezohºadnenia nákladov na prevenciu v koncepcii PQC je to, Ïe prevencia zabezpeãuje nízku kvalitu. Napr. Sandholm a Sörqvist [26] prijímali klasifikáciu vypl˘vajúcu z definície PQC a do nákladov nízkej kvality nezatrieìovali prevenciu. In˘

45

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Ekonomika a management pohºad mal Harrington [9], ktor˘ predstavil upraven˘ model PQC (predstaven˘ na obr. 1). Spomínan˘ autor klasifikoval náklady nízkej kvality na dve hlavné skupiny: priame a nepriame náklady. Priame náklady sú podºa neho náklady, ktoré sú viditeºné v úãtovn˘ch v˘kazoch.

Obr. 1:

Sem zatriedené náklady vznikajú preto, Ïe vÏdy existuje riziko ºudského zlyhania a to spôsobuje vznik ch˘b. ªudia vo svojej prirodzenosti robia chyby a kvôli tomu by mali byÈ neustále ‰kolení (náklady prevencie), aby vedeli správne vykonávaÈ svoju prácu.

Model PQC J. Harringtona

Zdroj: vlastné spracovanie na základe [9]

Harrington do PQC zatrieìuje náklady na prevenciu. Je to preto, lebo chce ukázaÈ v‰etky náklady, ktoré sú spojené s kvalitou. Svoj model PQC naz˘va modelom nákladov nízkej kvality (poor quality costs), a nie modelom nákladov na kvalitu. Podºa neho pojem nízka kvalita má vyvolaÈ negatívny dojem, ktor˘ je spojen˘ s nákladmi na kvalitu, presnej‰ie s nákladmi na zlú kvalitu. V ãase keì Harrington navrhol tento model, v podnikateºskom prostredí prevládal názor, Ïe vyrábanie v˘robkov lep‰ej kvality je nieão nad‰tandardné a stojí vÏdy viac [1]. Georgios, Enklawa, Washitani [8] tvrdia, Ïe najväã‰ím zdrojom skryt˘ch nákladov na kvalitu sú straty vo v˘robe a chyby v plánovaní, ktor˘ch následky prechádzajú väã‰inou procesov a cez nich pôsobia na zvy‰ovanie nákladov. Preto je veºmi dôleÏité, aby boli ão najr˘chlej‰ie odstránené. Spomenutí autori klasifikujú náklady na prevenciu, hodnotenie a chyby ako

46

náklady na kvalitu, a zároveÀ náklady na straty, ão znamená, Ïe aj v rámci prevencie a hodnotenia je moÏné hovoriÈ o stratách. Spomenutí autori zaviedli do klasifikácie nákladov na kvalitu dve nové poloÏky – straty z prevencie (prevention losses) a straty z hodnotenia (appraisal losses). Dahlgaard, Kristesen a Kanji [5] v súvislosti so skryt˘mi nákladmi zaviedli novú komplexnú klasifikáciu nákladov na kvalitu, ktorá berie do úvahy „skryté“ ãísla. Spomínaná klasifikácia je predstavená v tab 2. Podºa t˘chto autorov celkové náklady na kvalitu moÏno horizontálne klasifikovaÈ na externé a interné náklady na kvalitu a vertikálne na viditeºné a neviditeºné náklady na kvalitu. V dôsledku takéhoto triedenia existuje 6 hlavn˘ch skupín nákladov na kvalitu (1a, 1b, 2, 3a, 3b a 4) [5]. Spomenut˘ model sa od ostatn˘ch lí‰i t˘m, Ïe berie do úvahy skryté náklady, priãom pojem skryté je spojen˘ s faktom, Ïe podnik

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Nová klasifikácia nákladov na kvalitu v podniku

Viditeºné náklady:

Interné náklady

Externé náklady

1a. náklady ch˘b a opráv 1b. náklady na prevenciu

2. náklady reklamácie a garancie

Neviditeºné náklady: 3a. strata produktivity 4. strata vierohodnosti v dôsledku nízkej v dôsledku nízkej kvality kvality a zlého riadenia a zlého riadenia 3b. náklady prevencie a hodnotenia Spolu:

1+3

2+4

Spolu 1+2 3+4

1+2+3+4 Zdroj: [5]

môÏe dosiahnuÈ tú istú mieru kvality pri niÏ‰ích nákladoch (v porovnaní s t˘mi nákladmi, ktoré vynakladá momentálne). Túto skutoãnosÈ si ale podnik neuvedomuje, teda je to pre neho skryté. Príkladom môÏe byÈ napr. pouÏitie lacnej‰ej prevencie na odstránenie ch˘b. Tento model poukazuje na skutoãnosÈ, Ïe aj v prevencii hodnotení, ãi strát, existujú skryté náklady. Ako ukazuje teória a prax, náklady na kvalitu sa triedia na 3 hlavné skupiny. Prvá skupina predstavuje náklady, ktoré zabezpeãujú poÏadovanú úroveÀ kvality, literatúra ich oznaãuje ako náklady na prevenciu alebo náklady zhodnosti. Druhá skupina sú náklady, ktoré vznikajú v dôsledku monitorovania kvality, v literatúre sú oznaãované ako náklady na hodnotenie alebo náklady zhodnosti. Tretia skupina sú náklady spojené z vyskytovaním sa ch˘b a nedostatkov, v literatúre sa oznaãujú ako náklady na interné alebo externé chyby, náklady straten˘ch príleÏitostí, náklady straty reputácie, náklady nezhodnosti.

2. Modely sledovania nákladov kvality Na základe rôznych definícií a rôznych triedení nákladov kvality, teória a prax poskytuje rôzne modely sledovania nákladov na kvalitu v závislosti od toho, ktoré triedenie sa berie do úvahy. Jedn˘m z modelov nákladov na kvalitu, ktor˘ je najviac roz‰íren˘m modelom v praxi, je model PAF, ktor˘ je úspe‰ne aplikovan˘ nielen vo v˘robe, ale i v sluÏbách. Spomenut˘ model rozdeºuje a sleduje náklady na kvalitu v troch základn˘ch skupinách – prevencia (Prevention – P), hodnotenie (Appraisal – A) a nezhodnosti – chyby (Failure – F). Model PAF navrhol v roku

1956 Feigenbaum a prv˘krát bol predstaven˘ v roku 1957 Walterom Masserom [19]. The British Standard Institution (BSI) a Spojené ·táty ho zapracovali do svojich ‰tandardov v norme BS 6143 a v ãasti 2 cez ASQC (American Society for Quality Control) [10]. Navy‰e veºa autorov, napr. Harrington, Juran, Gryna a Gibson, pouÏívalo tento model ako základ svojich v˘skumov [9]. Je ale potrebné uviesÈ, Ïe skupiny nákladov na kvalitu v Crosbyho modeli [4] obsahujú rovnaké poloÏky ako PAF model, rozdiel je iba v terminológii. In˘m modelom, ktor˘ je spojen˘ s procesn˘m riadením je procesn˘ model prezentovan˘ Rossom v roku 1977, prv˘krát pouÏit˘ Marshom v roku 1989. Tento model sa viac sústreìuje na procesy ako na v˘robky alebo sluÏby [4]. Triedi náklady do dvoch skupín a predpokladá, Ïe aj jedna, aj druhá skupina môÏu byÈ zdrojom úspor [18]. Prvou skupinou sú náklady zhody: náklady splnenia v‰etk˘ch stanoven˘ch i vopred predpokladan˘ch potrieb zákazníka pri nedostatku ch˘b aktuálneho procesu. Druhá skupina zah⁄Àa náklady nezhody: náklady, ktoré vznikajú v dôsledku nesprávneho priebehu procesu [7]. Tento prístup predpokladá moÏnosti úspor v oboch urãen˘ch skupinách. DôleÏitou v˘hodou tohto modelu, oproti in˘m modelom, je skutoãnosÈ, Ïe v prípade ak dôjde ku zmene parametrov procesu, r˘chlo sa to odrazí aj v nákladov˘ch poloÏkách. Tento model opú‰Èa tradiãn˘ prístup k nákladom na kvalitu (náklady na kvalitu v˘robkov), do urãitej miery uÏ zohºadÀuje problematiku hodnototvorn˘ch reÈazcov a teda sa zameriava aj na tvorbu hodnoty v˘robku [30]. Procesn˘ model nákladov na kvalitu vedie podniky k neustálemu zlep‰ovaniu

47

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Ekonomika a management a inovovaniu podnikov˘ch procesov (uÏitoãné v tomto smere môÏe byÈ vyuÏitie tak˘ch metód ako je Kaizen, metóda 5S ãi Demingov cyklus plan – do – check – act) [16]. V literatúre sa moÏno stretnúÈ s modelom chybného v˘robku. Pojem chybn˘ v˘robok sa net˘ka len nákladov vznikajúcich vo vnútri podniku, ale aj nákladov, ktoré podniku vznikajú v priebehu pouÏívania v˘robku zákazníkom [10]. Navy‰e, tento model môÏe byÈ pouÏit˘ na sledovanie nákladov spojen˘ch s v˘vojom inovácie, a práve v prípade, keì sa inovácia na trhu neuplatní, teda nezabezpeãí kvalitu, bude len inováciou a náklady s Àou spojené budú len inovaãn˘mi nákladmi a z pohºadu nákladov na kvalitu to budú náklady na chyby. Chyby môÏu vznikaÈ na rôznych oddeleniach a rôznych úrovniach podniku. ëal‰ím modelom je CoPQ model (Costs of Poor Quality), ktor˘ berie do úvahy definície PQC. Základom tohto modelu je nesplnenie poÏiadaviek zákazníka a s t˘m spojené straty. Model ãlení náklady na kvalitu do ‰tyroch skupín. Prvá a druhá skupina obsahuje: 1 – náklady na interné straty z nekvalitnej v˘roby, 2 – externé straty z nekvalitnej v˘roby. Tretia skupina obsahuje náklady spojené s investíciami a vyuÏitím príleÏitostí, posledná skupina sú náklady súvisiace s po‰kodením Ïivotného prostredia. Ako ukazuje prax, napriek v˘hodám tohto modelu stále existujú problémy s vytriedením jednotliv˘ch poloÏiek, ktoré by sa dali zahrnúÈ do niektorej z uveden˘ch skupín tohto modelu. Veºmi ãasto neexistuje v podnikoch taká evidencia, ktorá by bola schopná poloÏky spadajúce do dvoch posledn˘ch skupín sledovaÈ, a to ani na úrovni analytickej evidencie [28]. ëal‰ím modelom, ktor˘ sa priamo net˘ka nákladov na kvalitu, ale jeho vyuÏitie sleduje v‰etky náklady, ãiÏe aj náklady na kvalitu, je model ABC – Activity-based costing. Tento model ako prví definovali Robert S. Kaplan a W. Bruns v roku 1987 [14]. Vìaka tomu modelu je moÏné odhadnúÈ, aké rezervy má podnik a zadefinovaÈ, aké sú ich náklady. To napomáha pri definovaní efektívnosti urãen˘ch ãinností. Podºa metódy ABC celkové náklady prípravy produkcie sú pripisované celej skupine v˘robkov v danom cykle [30]. Activity based costing je úspe‰ne pouÏívan˘ tak vo v˘robn˘ch podnikoch, ako aj v podnikoch sluÏieb, vyskytuje sa aj v sektore bankovníctva, ãi poisÈovníctva [21]. Treba zároveÀ podotknúÈ, Ïe ABC metóda

48

zameriava pozornosÈ na procesy, preto je ãoraz ãastej‰ie pouÏívaná v podnikoch, ktoré pre‰li na procesné riadenie [22], [23]. Ako bolo spomenuté, metóda ABC nie je modelom, ktor˘ hovorí len o nákladoch na kvalitu. Je to alternatívny prístup, ktor˘ môÏe byÈ vyuÏit˘ na identifikáciu kvantity (hodnoty) a miesta tvorby nákladov na kvalitu. Tsai [28] vo svojich ãlánkoch navrhuje prepojenie nákladov na kvalitu a ABC „metódy“. Základom tohto prepojenia je vytvorenie spoloãnej databázy, ktorá poskytuje údaje o rôznych nákladoch, priãom zároveÀ podáva nefinanãné informácie, ktoré môÏu byÈ zdrojom v oblasti zlep‰enia kvality. Dlhodob˘m cieºom pri pouÏití metódy ABC je eliminácia ãinností, ktoré nepriná‰ajú hodnotu, neustále zlep‰ovanie a Ïiadne chyby (zero defects). Z v˘skumov Schiffauerovej a Tomsona, ktoré sa t˘kali kvality vypl˘va, Ïe najãastej‰ie pouÏívan˘m modelom nákladov na kvalitu je model PAF. V jednom z ich ãlánkov [24] je uvedené, Ïe United Technologies Corporation, Essex Telecomunication Products Division sledujú náklady na kvalitu podºa PAF modelu, a poãas piatich rokov jeho fungovania ich produktivita vzrástla o 26 %. PAF model vyuÏívajú zároveÀ také firmy ako Hydro Coatings UK, York International UK, Philips Power Semiconductor Business Group UK, ITT Europe Belgium, Ferranti Defenses Systems UK, National Cash Register Company Germany, ITT Corp., New York, USA atì. Procesn˘ model pouÏívajú také firmy ako napr. GEC, Alsthom Engineering Systems. ZároveÀ ABC model bol s pln˘m úspechom zaveden˘ v Networked, Computer Manufacturing Operation of Hewlett-Packard, USA, ktoré vìaka tomuto modelu zredukovali svoje náklady na kvalitu o 25 % za 1 rok. Základn˘mi poloÏkami nákladov na kvalitu sú v jednotliv˘ch modeloch náklady na prevenciu, hodnotenie, náklady na externé a interné chyby, ãiÏe hlavné poloÏky modelu PAF. Autori, ktorí pouÏívajú triedenie len podºa PAF sú napr. Juran, Feigenbaum, Campanell, Nenadal, Iwasiewicz, Oyrzanowski, Dahlgaar, Kristesean, Kanji, norma BS 6143. ZároveÀ sú elementy PAF modelu brané do úvahy pri delení nákladov na kvalitu, napr. Skrzypek, TQM, ASQC, Harrington. Z dôvodov odli‰ného definovania nákladov na kvalitu sa stretávame s nezatriedením nákladov na prevenciu do nákladov na kvalitu. S t˘mto postupom sa stretávame

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Ekonomika a management v triedení nákladov na kvalitu u Sandholma a Sörqvista. Navy‰e sú autori, ktorí triedia náklady na kvalitu podºa procesného modelu –Crosby, ãi Bank. Mnohé z uveden˘ch triedení majú vo svojom delení nákladov na kvalitu poloÏky z PAF modelu a procesného modelu – napr. TQM, ASQC. ZároveÀ existujú tieÏ koncepcie, ktoré do triedenia nákladov na kvalitu Obr. 2:

pridávajú poloÏku „náklady straten˘ch príleÏitostí“, napr. Skrzypek, Bank a ASQC. ëal‰ia poloÏka, ktorá je uvedená samostatne sú náklady stratenej reputácie, uvedená u Haringtona a Skrzypkovej. PoloÏku „náklady spojené s vybavením a záväzkami voãi zákazníkom“ stretávame ako samostatnú poloÏku len u Haringtona (obr. 2).

Súãasné prístupy k triedeniu nákladov na kvalitu

Zdroj: vlastné spracovanie

Najväã‰ími nedostatkami väã‰iny prezentovan˘ch modelov, ktoré sledujú len náklady na kvalitu, sú: nedostatok zhodnosti alokácie nákladov na kvalitu, neúplné sledovanie nákladov na kvalitu a nezachytenie skryt˘ch nákladov, nedostatok informácií t˘kajúcich sa vyuÏitia pracovného ãasu zamestnancami na rôzne ãinnosti poãas pracovnej doby. Uvedené triedenia a modely sú orientované na náklady na kvalitu a majú poskytnúÈ ão najviac podkladov pre zniÏovanie nákladov (costs emphasis). Nedávajú v‰ak skoro Ïiadne podklady o strane príjmov. Na takomto základe nemoÏno zisÈovaÈ citlivosÈ na v˘kyvy ekonomiky. Preto je potrebné zvoliÈ in˘ prístup ku klasifikácii nákladov.

3. Prepojenie prístupu nákladov a zvy‰ovania trÏieb Náklady kvality sa tvoria v dôsledku existujúcej nekvality alebo snahou o udrÏanie kvality ãi snahou o predchádzanie vzniku nekvality. Pri pohºade na frekvenciu procesov moÏno kon‰tatovaÈ, Ïe absolútny poãet v˘skytu intern˘ch a extern˘ch ch˘b pri ich urãitej pravdepodobnosti závisí od mnoÏstva produkcie. Tieto náklady moÏno zaradiÈ medzi variabilné náklady kvality. Treba zároveÀ zdôrazniÈ, Ïe podnik nevie kedy a pri akom mnoÏstve v˘robkov dôjde k v˘skytu chyby. Kvôli tomu musí vynakladaÈ kon‰tantné náklady na zabezpeãenie kvality (napr. potreba meradiel a ich kalibrácia). S t˘m ãasto súvisia rôzne ‰kolenia a udrÏiavacie kurzy a s t˘m spojené náklady. Tieto druhy nákladov

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Ekonomika a management moÏno zaradiÈ medzi fixné náklady existujúce nezávisle od mnoÏstva produkcie. Medzi fixné náklady moÏno zaradiÈ aj náklady rastúce skokovito, ktoré sú spojené s jednorazov˘mi investíciami do kvality (napr. nákup novej technológie a dopady takejto investície na kvalitu i ìal‰iu konkurencieschopnosÈ podniku [12]). Na základe uveden˘ch závislostí môÏeme povedaÈ, Ïe náklady na kvalitu sa rozdeºujú na fixné a variabilné. Základom triedenia nákladov kvality na fixné a variabilné je ich závislosÈ od objemu predan˘ch v˘robkov. Pri porovnávaní s modelom PAF náklady na chyby (F) sú nákladmi variabiln˘mi a väã‰ina prevencie (P) aj zabezpeãovania (A) sú náklady fixné. V rámci nákladov na prevenciu a hodnotenie moÏno rozlí‰iÈ ako podskupinu fixné náklady rastúce skokom. Podnik by sa mal v rámci riadenia nákladov na kvalitu snaÏiÈ o zniÏovanie ich celkovej veºkosti v ãase. Práve to vedie k efektívnej‰iemu modelovaniu ‰truktúry nákladov na kvalitu. Porovnávanie ich v˘‰ky v jednotliv˘ch kategóriách, napr. nahrádzanie jedného druhu nákladov na prevenciu in˘m, ktor˘ je men‰í, ukáÏe, ãi je podnik schopn˘ niÏ‰ími nákladmi na prevenciu dosiahnuÈ odstránenie toho istého alebo väã‰ieho poãtu ch˘b. To je spojené s faktom, Ïe aj v rámci prevencie a hodnotenia má podnik straty a veºmi ãasto práve neefektívna prevencia a hodnotenie sú zdrojom opaãn˘ch v˘sledkov – rastu celkov˘ch nákladov na kvalitu. Uveden˘ pohºad na triedenie nákladov kvality dáva moÏnosÈ ukázaÈ, ako zmena vo v˘‰ke predaja vpl˘va na v˘‰ku operaãného zisku (EBIT). Zmena v˘‰ky predaja môÏe byÈ smerom nahor alebo smerom nadol a môÏe byÈ spôsobená napr. poklesom celkového dopytu, odchodom zákazníkov ku konkurencii alebo rastom predaja v dôsledku zlep‰enia ponuky podniku vy‰‰ou kvalitou. Skúmanou otázkou je, ãi podniky, ktoré investujú do vy‰‰ej úrovne kvality pri zachovaní rovnakej v˘‰ky celkov˘ch nákladoch ãi pri ich raste, sú citlivej‰ie na v˘kyvy ekonomiky alebo vykazujú vy‰‰iu stabilitu napriek uveden˘m zmenám. Pod citlivosÈou na v˘kyvy ekonomiky treba chápaÈ zmeny celkového dopytu, ktoré vyvolávajú zmeny v odbyte. Predpokladáme, Ïe zmena v ekonomike vpl˘va na v‰etk˘ch konkurentov rovnako, ão znamená, Ïe napr. pokles/rast odbytu je proporcionálny u v‰etk˘ch konkurentov.

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Podnik sa pri poklese odbytu snaÏí zvy‰ovaÈ kvalitu produktov a v súvislosti s t˘m zvy‰uje svoje náklady na prevenciu alebo náklady na zabezpeãenie kvality (P + A), ão v koneãnom dôsledku pôsobí na zv˘‰enie fixn˘ch nákladov. V rámci nákladov na udrÏanie a hodnotenie existujú aj variabilné náklady, av‰ak podstatné sú v tomto prípade investícií do kvality ako fixného nákladu (napr. nákup nového stroja, softvéru, technológií, zamestnanie expertov, platy zamestnancov útvaru riadenia kvality atì.) a dopad t˘chto nákladov na operaãn˘ zisk. O citlivosti na v˘kyvy ekonomiky, teda o tom, ako sa mení operaãn˘ zisk podniku (EBIT) pri raste alebo poklese odbytu v závislosti od ‰truktúry celkov˘ch nákladov, hovorí efekt operaãnej páky [6]. Táto súvislosÈ je vyjadrená vzorcom (1): DOL = % ∆EBIT / % ∆S

(1)

DOL

– stupeÀ operaãnej páky (degree of operating leverage), %∆EBIT – percentuálna zmena operaãného zisku (zisk pred úrokmi a zdanením), %∆S – percentuálna zmena predaja netto. DOL moÏno interpretovaÈ ako zv˘‰enie/ /zníÏenie operaãného zisku pri raste/poklese predaja o 1 %, ão moÏno chápaÈ ako citlivosÈ (senzibilitu) operaãného zisku na zmenu príjmov. ZároveÀ DOL hovorí o hodnote operaãného rizika, ktoré je spojené s úrovÀou fixn˘ch nákladov. V literatúre (opäÈ napr. [6])sa moÏno stretnúÈ aj s in˘m vzorcom na v˘poãet DOL (2), ktor˘ berie do úvahy úroveÀ fixn˘ch a variabiln˘ch nákladov: S – VAR DOL = ––––––––––––– S – VAR – FIX

(2)

Na základe vzorca (2) v˘poãtu stupÀa operaãnej páky moÏno úpravami (3) a (4) VAR = VARk + VARp

(3)

FIX = FIXk + FIXp

(4)

pouÏiÈ vzorec (5), ktor˘ berie do úvahy náklady na kvalitu:

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Ekonomika a management S – (VARk + VARp) DOL = –––––––––––––––––––––––––––––– (5) S – (VARk + VARp) – (FIXk + FIXp)

DOL S VAR FIX VARk VARp FIXk FIXp

– – – – – – – –

stupeÀ operaãnej páky predaj celkom variabilné náklady celkom, fixné náklady celkom náklady kvality variabilné celkom, ostatné variabilné náklady celkom, fixné náklady kvality celkom, ostatné fixné náklady celkom

Interpretácia vzorca (2),(5) je taká istá ako v prípade vzorca (1). ZároveÀ posledná forma vzorca DOL umoÏÀuje skúmaÈ zmeny citlivosti podniku na zmeny predaja prostredníctvom zmeny operaãného zisku.

4. Investície do kvality a citlivosÈ podniku na v˘kyvy ekonomiky Premietnutie investícií do kvality v zmysle prevencie a udrÏanie si zákazníka a citlivosÈ na v˘kyvy ekonomiky (zmeny odbytu) moÏno prezentovaÈ analyzovaním simulovan˘ch situácií (tab. 3) v ‰tyroch základn˘ch kombináciách (Kn; n = 1,2,3,4) rôznej ‰truktúry celkov˘ch nákladov a rôznej ‰truktúry nákladov na kvalitu v jednom podniku za predpokladu, Ïe celkové

Tab. 3:

náklady zostávajú na tej istej úrovni (sú kon‰tantné) a mení sa len ‰truktúra nákladov. V realizovanej simulácii predpokladáme, Ïe náklady na kvalitu tvoria 10 % z celkov˘ch nákladov a teda zmeny vo fixnosti a variabilnosti nákladov na kvalitu sa odráÏajú v zmene celkov˘ch fixn˘ch a variabiln˘ch nákladov. Zmena v ich ‰truktúre sa odráÏa v zmene ‰truktúry celkov˘ch nákladov. Ak podnik vyrába 100 kusov v˘robkov a cena v˘robku je kon‰tantná (napr. 20 Eur), potom rast alebo pokles zisku môÏe byÈ spôsoben˘ buì zmenami ‰truktúry celkov˘ch nákladov alebo zmenami predaného mnoÏstva. Pritom vychádzame z poznatku manaÏmentu kvality, ktor˘ hovorí, Ïe vhodná ‰truktúra nákladov na kvalitu je taká ‰truktúra, v ktorej sú náklady na prevenciu väã‰ie ako náklady na udrÏanie a chyby (P > A + F). Z tohto dôvodu simulácia v súlade s Juranov˘m odporúãaním predpokladá, Ïe podniky so správnou ‰truktúrou nákladov na kvalitu majú 90 % nákladov v skupine prevencia a ãasÈ nákladov na udrÏanie ako fixn˘ náklad, variabilné náklady tvoria zvy‰n˘ch 10 % a sú tvorené nákladmi na chyby a zvy‰nou ãasÈou nákladov na udrÏanie. V prípade nevhodnej ‰truktúry nákladov na kvalitu, 90 % z nich sú variabilné náklady, teda náklady na chyby a ãasÈ nákladov na udrÏanie a len 10 % sú fixné náklady na prevenciu a ãasÈ nákladov na udrÏanie.

Vplyv ‰truktúry nákladov kvality na rast operaãného zisku

Kn

Predaj

Celkové Fixné Celkové Variabilné Celkové náklady náklady fixné náklady variabilné (TC) na kvalitu náklady na kvalitu náklady (FIXk) (FIX) (VARk) (VAR)

Bod zvratu (BEP)

StupeÀ oper. páky (DOL)

Rast predaja o 10 %

Rast operaãného zisku

K1

2000

1600

144

1000

16

K2

2000

1600

16

872

144

600

44,0

3,50

2200

35,0 %

728

36,3

3,18

2200

31,8 %

K3

2000

1600

16

600

K4

2000

1600

144

728

144

1000

27,7

2,50

2200

25,0 %

16

827

20,0

2,64

2200

26,4 %

Zdroj: vlastné spracovanie.

Ako vidieÈ v tab. 3, podnik dosahuje najvy‰‰í rast operaãného zisku z jednotky produkcie pri kombinácii K1, v ktorej má vo svojej ‰truktúre nákladov viac fixn˘ch ako variabiln˘ch nákladov. To je spôsobené vy‰‰ími fixn˘mi nákladmi – vy‰‰ou alokáciou nákladov na prevencie a udrÏania kvality (P + A) ako na odstraÀovanie

vzniknut˘ch ch˘b (F). Podnik v kombinácii K2 má tieÏ viac fixn˘ch ako variabiln˘ch nákladov v ‰truktúre nákladov, av‰ak fixné náklady sú niÏ‰ie ako v prípade kombinácie K1, ako dôsledok niÏ‰ej alokácie nákladov na prevencie a udrÏania kvality (P + A) a rastom nákladov na chyby (F). To sa odráÏa aj v ‰truktúre celkov˘ch

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Ekonomika a management nákladov podniku aj v raste hodnoty operaãnej páky. Pri vy‰‰ej úrovni fixn˘ch nákladov, ktor˘ch v˘‰ka záleÏí od investovania do kvality (ão sa odráÏa na v˘‰ke nákladov na prevenciu), menia sa hodnoty bodu zvratu (BEP). Podnik pri kombinácii K1 v dôsledku zabezpeãenia i zlep‰ovania kvality potrebuje vytvoriÈ viac v˘robkov, aby nebol stratov˘ a dosiahol zisk, ako v kombinácii K2. V tejto súvislosti moÏno kon‰tatovaÈ, Ïe podnik, ktor˘ investuje do kvality (teda má viac fixného nákladu v nákladovej ‰truktúre) pri poklese predaja r˘chlej‰ie dosahuje stratu, ako podnik, ktor˘ ma menej fixn˘ch nákladov. V tab. 3 sú zároveÀ simulované situácie K3 a K4, ktoré môÏu v podniku nastaÈ. Podnik so ‰truktúrou zo situácie K3 je najmenej citliv˘ na pohyby predaja zo v‰etk˘ch ‰tyroch prezentoObr. 3:

van˘ch kombinácií v tabuºke 3. V situácii K3 pre podnik nie je prioritou zlep‰ovaním kvality, má veºmi nízke náklady na prevenciu (P) a vysok˘ poãet ch˘b (F). V ‰truktúre jeho nákladov na kvalitu v˘znamn˘ podiel tvoria variabilné náklady na kvalitu (VARk). Ako dôsledok nízkeho záujmu podniku o kvalitu tak˘to podnik nemôÏe rátaÈ s dlhodob˘m rastom, pretoÏe na slabej kvalite nemoÏno budovaÈ lojalitu svojich zákazníkov, ktorá má siln˘ vplyv na úroveÀ trÏieb v podniku. Zvy‰ovanie fixn˘ch nákladov v dôsledku investície do kvality sa prejavuje v zmene celkovej ‰truktúry nákladov, ão vidieÈ aj na obr. 3, ktor˘ ukazuje, ako zmena ‰truktúry nákladov ovplyvÀuje hodnotu operaãnej páky (DOL) a poukazuje na zmenu operaãného rizika.

Hodnota DOL pri rôznej ‰truktúre celkov˘ch nákladov vyvolan˘ch zmenami v oblasti nákladov kvality


Na obr. 3 je znázornená hodnota operaãnej páky DOL pri rôznom percentuálnom podiele variabiln˘ch nákladov na celkov˘ch nákladom. Ako vidieÈ z obr. 3, hodnota operaãnej páky je najmen‰ia v prípade, keì podnik má len variabilné náklady. MôÏeme teda povedaÈ, Ïe v tomto prípade je operaãné rizikom najmen‰ie a podnik je najmenej citliv˘ na v˘kyvy ekonomiky. ZároveÀ treba zdôrazniÈ, Ïe hlavn˘m cieºom sledovania nákladov na kvalitu je ich riadenie a to tak˘m spôsobom, aby ich celková hodnota klesala (TC1 > TC2), ão sa odráÏa na cel-

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kov˘ch nákladoch. Vìaka poklesu celkov˘ch nákladov sa zmení aj hodnota operaãnej páky a zmení sa teda aj citlivosÈ podniku na zmeny, ão predstavuje simulovaná situácia uvedená v tab. 4. V tab. 4 sú prezentované simulované situácie, pri ktor˘ch sa mení v˘‰ka a ‰truktúra celkov˘ch nákladov vyvolaná zmenami iba v oblasti nákladov na kvalitu. Anal˘za tabuºky 4 ukazuje, Ïe zmeny nákladov na kvalitu, ktoré pôsobia na zniÏovanie hodnoty celkov˘ch nákladov vpl˘vajú zároveÀ na pokles DOL

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Ekonomika a management Zmena hodnoty operaãnej páky spôsobená zmenami iba v oblasti nákladov na kvalitu

Tab. 4:

TC1=TC2

TC1>TC2

TC1
a

b

a

b

c

d

e

a

b

c

d

e

TC1

800

800

800

800

800

800

800

800

800

800

800

800

FIX1

400

400

400

400

400

400

400

400

400

400

400

400

VAR1

400

400

400

400

400

400

400

400

400

400

400

400

TC2

800

800

700

700

700

700

700

900

900

900

900

900

FIX2

360

440

350

300

400

280

420

500

400

450

380

520

VAR2

440

360

350

400

300

420

280

400

500

450

520

380

DOL1

3

3

3

3

3

3

3

3

3

3

3

3

DOL2

2,8

3,2

2,16

2

2,33

1,93

2,4

6

5

5,5

4,8

6,2

Vysvetlivky:

FIX – fixné náklady, VAR – varibilné náklady, TC –celkové náklady, DOL – hodnota operaãnej páky, 1– v˘chodiskov˘ stav, 2 – stav po zmene Zdroj: vlastné spracovanie.

a teda na pokles citlivosti operaãného zisku na zmenu objemu predaja. Táto zmena hodnoty DOL je spôsobená rastom zisku, ktor˘ podnik dosiahuje v dôsledku zmien v ‰truktúre nákladov na kvalitu. Obr. 4 zobrazuje priebeh troch hodnôt operaãnej páky pri rôznom percentuálnom podiele variabiln˘ch nákladov k fixn˘m nákladom. Hodnota DOLa predstavuje stav bez zmeny, v ktorom Obr. 4:

celkové náklady vo v˘chodiskovom období TC1 sa rovnajú TC2, hodnota DOLb ukazuje stav, kde celkové náklady klesli (TC1>TC2) a DOLc stav, kde celkové náklady vzrástli (TC1
Hodnota DOL pri rôznej ‰truktúre celkov˘ch nákladov


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Záver V snahe ukázaÈ ak˘ je vplyv nákladov kvality a ich ‰truktúry na operaãn˘ zisk (EBIT) v prípade zmien v ekonomike (jej v˘kyvov), je nevyhnutné triediÈ náklady na kvalitu z hºadiska ich fixnosti a variabilnosti. Takéto triedenie predstavuje nov˘ pohºad na riadenie nákladov na kvalitu. Pohºad cez fixnosÈ a variabilnosÈ nákladov kvality napomáha pri rozhodovaniach pri alokácii nákladov v oblastiach zlep‰ení kvality. Správne riadenie nákladov kvality vedie k poklesu nákladov na chyby (F) a raste nákladov na prevenciu (rast fixn˘ch a pokles variabiln˘ch nákladov). V súvislosti s t˘m je potrebné pripomenúÈ, Ïe stály rast fixn˘ch nákladov ako následok zlep‰ovania kvality v prípade poklesu predaja zvy‰uje podnikateºské riziko. V˘poãtom stupÀa operaãnej páky (DOL) podnik môÏe analyzovaÈ dopady zmeny v ‰truktúre nákladov na kvalitu na úroveÀ operaãného zisku. Z toho dôvodu riadenie podniku by sa malo snaÏiÈ zdokonaºovaÈ podnikateºské procesy a teda zniÏovaÈ nielen externé a interné chyby, ale aj straty v oblasti prevencie (P) a straty v oblasti hodnotenia (A). Vìaka zniÏovaniu a minimalizovaniu strát v oblasti prevencie a hodnotenie, ktoré generujú fixné náklady na kvalitu, podnik môÏe efektívnej‰ie (s niÏ‰ími nákladmi) odstraÀovaÈ nepodarky a chyby, ão zniÏuje podnikateºské riziko a zmen‰uje citlivosÈ podniku na v˘kyvy ekonomiky.

Literatúra [1] ANDERSSON, S., RYFORS, S. Poor quality costs – a case study in VBS. Göteborg: Graduate Business School, 2001. s. 62-63. ISSN 1403-851X. [2] BANK, J. Zarzàdzanie przez jakoÊç. Warszawa: Gebethner & Ska, 1996. 236 s. ISBN 83-85205-57-8. [3] CIECHAN – KUJAWA, M. Rachunek kosztów jakoÊci. Kraków: Oficyna Ekonomiczna, 2005. 61 s. ISBN 83-89355-68-X. [4] CROSBY, P. B. Quality Is Free. The art of making quality certain. 1. vyd. New York: McGraw -Hill, 1979. 309 s. ISBN 0-07014512-1. [5] DAHLGAARD, J. J., KRISTESEN, K., KANJI, G. K. Podstawy zarzàdzania jakoÊcià. Warszawa: PWN, 2004. ISBN 83-01-14324-X. [6] D¢BSKI, W. Teoretyczne i praktyczne aspekty zarzàdzania finansami przedsi´biorstwa. Warszawa: PWN, 2005, s. 158–159. ISBN 83-01-14290-1.

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[7] FAJCZAK – KOWALSKA, M. Koszty jakoÊci i ich rachunek. Problemy jakoÊci. 2004, roã. 36, ã.8, s. 34. ISSN 0137-8651. [8] GEORGIOS, G., ENKLAWA, T. a WASHITANI, K. Hidden quality costs and the distinction between quality costs and quality loss. Total Quality Management & Business Excellence. 2001, roã. 12, ã. 2, s. 179–190. ISSN 1478-3371. [9] HARRINGTON, H. J. Poor – Quality Cost. 1. vyd. New York: Mercel Dekker, 1987. 198 s. ISBN 978-0824777432. [10] HWANG, G. H., ASPINWALL, E. M. Quality costs models and their application a review. Total Quality Management. 1996, roã. 7, ã. 3, s. 267–282. ISSN 0954-4127. [11] IWASIEWICZ, A. Zarzàdzanie jakoÊcià. Warszawa – Kraków: PWN, 1999, 270 s. ISBN 8301129573. [12] JANEâEK, V., Hynek, J. Investování do vyspûl˘ch technologií. E+M Ekonomie a Management. 2006, roã. 9, ã. 1, s. 49–66. ISSN 1212-3609. [13] JURAN, J. M., GRYNA, F. M. JakoÊç – projektowanie – analiza. Warszawa: WNT, 1974. 732 s. [14] KAPLAN, R. S., BRUNS, W. Accounting and Management: A Field Study Perspective. Harvard Business School Press, 1987. 374 s. ISBN 0-87584-186-4. [15] KRASZEWSKI, R. Nowoczesne koncepcje zarzàdzania jakoÊcià. Toruƒ: Dom Organizatora, 2006. 368 s. ISBN 978-83-7285-286-1. [16] LINCZÉNYI, A. Návrh ukazovateºov rentability kvality. In: Jakost – Quality. Zborník z mezinárodní konference. Ostrava: DT Ostrava, 2005. ISBN 80-02-01729-3. [17] MAKARSKI, S. Uwarunkowania i koszty jakoÊci produktywna rynku. Praca zbiorowa pod. red. S. Makarski. Rynkowe mechanizmy kszta∏towania jakoÊci. Rzeszów: URZ, 2005. s. 121. ISBN 83-7338-295-X. [18] MARSH, J. Process modeling for quality improvement. In: Proceedings of the Second International Conference on Total Quality Management, s. 111. citované z: ROSS, D.T.: Structured analysis (SA): A language for communicating ideas, IEEE Tranasactions on Software Engineering, roã. SE-3, ã.1, s. 16. ISSN 1360-0613. [19] MASSER, W. J. The Quality Management and Quality Costs. Industrial Quality Control. May 1956, s. 5-8. ISSN 0884-822X. [20] MYSZEWSKI, J. M. Po prostu jakoÊç. Podrćznik zarzàdzania jakoÊcià. Warszawa: Wyd. WSPiZ, 2005. s. 116. ISBN 83-89437-38-4.

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Ekonomika a management [21] PIECHOTA, R. Projektowanie rachunku kosztów dzia∏a. Activity Based Costing. Warszawa: Difin, 2005. ISBN 83-7251-543-3. [22] PNIEWSKI, K. Koszty dzia∏aƒ pod kontrolà [online]. PCkurier 22/2000 [cit. 2007-02-20]. Dostupné z: . [23] POPESCO, B. Metodika aplikace kalkulace Activity-Based Costing v prÛmyslov˘ch firmách. E+M Ekonomie a Management. 2010, roã. 13, ã. 1, s. 103–114. ISSN 1212-3609. [24] SCHIFFAUEROVA, A., THOMSON, V. A review of research on cost of quality models best practices. Interantional Journal of Quality and Reliability Management. 2006, roã. 23, ã. 4, s. 10–12. ISSN 0265-671X. [25] SKRZYPEK, E. JakoÊç i efektywnoÊç. Lublin: Uniwersytet Marie Sklodowskej Curie, 2002, s. 249. ISBN 83-227-1626-5. [26] SÖRQVIST, L. Kvalitetsbristkostnader. Ett hjälpmedel för verksamhetsutveckling. Lund: Studentlitteratur, 1998. citované z: SANDHOLM a LENNART: Total Quality Management. Lund: Studentlitteratur, 2000. 286 s. ISBN 91-44-01164-4. [27] TEPLICKÁ, K. V˘znam a postavenie nákladov v systéme riadenia kvality [online]. [cit. 200804-10]. Dostupné z: . [28] TSAI, W. H. Quality cost measurement under activity-based costing. International Journal of Quality and Reliability Management. 1998, roã.15, ã. 7, s. 719–752. ISSN 0265-671X.

[29] WAWAK S. Zarzàdzanie jakoÊcià. Teoria i praktyka. Gliwice: HELION, 2002. 153 s. ISBN 83-7197-867-7. [30] Wydawnictwo Naukowe PWN [online]. [cit. 2007-12-12]. Dostupné z: . [31] ZYMONIK, Z. Koszty jakoÊci w zarzàdzaniu przedsi´biorstwem. 2. vyd. Wroc∏aw: Oficyna Wydawnicza Politechniki Wroc∏awskiej, 2003. 229 s. ISBN 83-7085-744-2.

doc. Ing. Martin Mizla, PhD. Ekonomická univerzita v Bratislave Podnikovohospodárska fakulta so sídlom v Ko‰iciach Katedra manaÏmentu [email protected] Mgr. Patrycja Pud∏o, PhD. Ekonomická univerzita v Bratislave Podnikovohospodárska fakulta so sídlom v Ko‰iciach Katedra manaÏmentu [email protected]


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Abstract QUALITY COSTS STRUCTURE AND COMPANY SENSITIVITY TO FLUCTUATION OF ECONOMY Martin Mizla, Patrycja Pud∏o Authors deals with influence and relations of quality costs with company economy. The article presents a new division of quality costs as fixed and variable quality costs. The new division is helpful to show influence of quality costs on changes in the earnings before interest and taxes (EBIT). Those changes can be defined thanks to known indicator of operating leverage (DOL). Value of this indicator helps to show how the structure of costs affects proportional changes of the EBIT. One of the most important rules in quality management is to minimize total quality costs and to avoid external and internal failures. Minimization of failure costs (variable costs) causes increasing of prevention and appraisal costs (fixed costs). In this situation, management of company must not forget that increasing of fixed costs and decreasing of total revenue causes increasing of company risk at the same time and near future. That is the reason why management must concentrate on the improvement of company processes. The improvement is possible thanks to minimization not only failures but also prevention and appraisal losses. Thanks to minimization of prevention and appraisal losses, the company can minimize prevention and appraisal costs as fixed costs. Company (by the improvement actions) is able to change the structure of quality costs which has direct influence on the EBIT. The level of EBIT has important role in the level of tax which company has to pay and also decides about decisions of further quality improvements. We can not forget that those quality costs changes lead to decrease of operation risk level of which have important role and position in situation of economic crisis, or fluctuation of economy, in general. Key Words: fluctuation of economy, quality costs, fixed costs, variable costs, degree of operating leverage. JEL Classification: E32, M21.

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HEXAGONAL STELLAR MODEL OF CRM – KEY ELEMENTS INFLUENCING THE CRM BUILDING Milan Kubina, Viliam Lendel

Introduction In developed countries the change of business processes are distinguished in the orientation to a customer. Enterprises base their actions in the market not on “suspicions” or “experience”, but on knowledge that is acquired by analysing customer’s data. However knowledge-based activity of an enterprise is possible only when having processed the data on their basis motivated decisions to find, attract and keep customers are taken. This explains why at present it has particularly become fashionable to speak about customer relationship management [43]. It’s a significant possibility to create a relevant competitive advantage and a possibility to be successful on chosen markets [39]. The scientific paper analyses variants of CRM conceptions by reviewing different models of CRM creation, analysis of which allowed envisaging typical elements influencing the CRM building in company. Analysis of scientific literature, comparative analysis and inductive method were main methods of the research. The analysis of scientific literature allowed revealing and theoretically finding the suitability of individual key elements for basic CRM system formation. By comparing different CRM models and applying the inductive method a hexagonal stellar model was designed. This scientific paper is organized as follows: we consider the concept of the CRM, present some definitions of CRM systems, as they are displayed in the literature. Main goal of the study is to contribute to the larger successfulness of organizations that decide for building of CRM. In the second and third section the paper considers the concept of CRM and how CRM systems are reported upon

in the literature. In the fourth section the paper describes the empirical research. The purpose of the research was to find and analyse the current level of Slovak companies in the CRM area on the base of identifying main factors that affect the level of using CRM information system and process of implementation in the company. The last section of the paper deals with identification of key linkages between management and CRM.

1. Analysis of the CRM Term CRM has a lot of definitions. Definitions of CRM are wide ranging and shall be explored in greater detail in the next section. CRM is everything what it is related to satisfaction of customer’s needs. Interesting view on the term CRM has been brought by Payne (2005). He understands customer relationship management as a strategic approach concerned with creating improved shareholder value through the development of appropriate relationships with key customers and customer segments. In his opinion CRM unites the potential of information technologies and relationship marketing strategies to deliver profitable, long-term relationships. CRM provides enhanced opportunities to use data and information both to understand customers and implement relationship marketing strategies better. This requires a cross-functional integration of people, operations, processes and marketing capabilities that is enabled through information technology and applications. Another view of CRM is that it is technologically orientated. In the aspect of information technologies CRM is understood as

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Ekonomika a management the complex of software and technologies automating and performing business processes in the following areas: sales, marketing, service and customer support. Kincaid (2003) defined CRM as the strategic use of information, processes, technology, and people to manage the customer's relationship with your company (marketing, sales, services, and support) across the whole customer life cycle. Choy et al. (2003) suggest that CRM is an information industry term for methodologies, software, and usually internet capabilities that help an enterprise manage customer relationships in an organized way. It focuses on leveraging and exploiting interactions with the customer to maximize customer satisfaction, ensure return business, and ultimately enhance customer profitability. Sandoe et al. (2001) argue that advances in database technologies such as data warehousing and data mining, are crucial to the functionality and effectiveness of CRM systems. Peppard (2000) suggests that technological advances in global networks, convergence and improved interactivity, are key to explaining the growth of e-business and CRM. Wide explanation variability of the term CRM may be documented also by these theses. Smith (2001) understands customer relationship management as business strategy combined with technology to effectively manage the complete customer life-cycle. In opinion of Stone and Woodcock (2001), the CRM represents a term for methodologies, Technologies and e-commerce capabilities used by companies to manage customer relationships. Also Khanna (2001) leans to this opinion by the thesis he said, that customer relationship management is an e-commerce application. In opinion of Buttle (2000), customer relationship management is about the development and maintenance of long-term mutually beneficial relationships with strategically significant customers. In opinion of Gosney and Boehm (2000), the basic theme is for the company to become more customer-centric. Role of relationship marketing in the CRM emphasized Peppers, Rogers and Dorf (1999), from their point of view, CRM can be viewed as an application of one-to-one marketing and relationship marketing, responding to an individual customer based on what the customer tell company and what else company know about customer. Hobby (1999) perceives

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customer relationship management as a management approach that enables organizations to identify, attract and increase retention of profitable customers by managing relationship with them. Couldwell (1999) emphasizes meaning of information about costumers and work with them, by the thesis, that CRM involves using existing customer information to improve company profitability and customer service. Glazer (1997) believes CRM as integrating element. In his opinion the customer relationship management provides strategic bridge between information technology and marketing strategies aimed at building long-term relationship and profitability. This requires informationintensive strategies. Kutner and Cripps (1997) have got the same opinion. They understand CRM as data-driven marketing. Following this definition (Tab. 1), we charted a short overview of different approaches in defining the CRM concept. Table 2 records in a concise form various view of several foreign authors on the definition of CRM. In opinion of Kopf (2000), CRM is scientific discipline which goal is profit maximization and which is interested in methods of increasing stability level of the riskiest customers and in methods of how to increase customer relationship value with decreasing costs at the same time. In our view, CRM is not science, because CRM hasn’t: Own conceptual base, (CRM uses conceptual base of marketing, informatics, management, sociology, psychology...); Own methodological base, (CRM uses models, methods and tools of marketing, informatics, management, psychology...). For the purpose of this paper, we offer the following definition: Customer relationship management is a comprehensive strategy and process of acquiring, retaining, and partnering with selective customers using information technology with aim to create superior value for the company and the customer. It involves the integration of marketing, sales, customer service supported by high-quality personnel and the supply-chain functions of the organization to achieve greater efficiency and effectiveness in delivering customer value by creating environment acceptable for customers.

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Definitions of CRM

Authors

Definition

Levine (2000)

CRM is the utilisation of customer-related information or knowledge to deliver relevant products or services to consumers.

Kumar & Reinartz (2006)

CRM is the strategic process of selecting the customers a firm can most profitably serve and of shaping the interactions between a company and these customers with the goal of optimizing the current and future value of the customers for the company.

Sandoe, Corbitt & Boykin (2001)

CRM is technologically oriented.

Chen & Popovich, (2003)

CRM is a combination of people, processes and technology that seeks to understand a company's customers and it is an integrated approach to managing relationships by focusing on customer retention and relationship development.

Bull (2003)

CRM is a complex combination of business and technological determinants.

Ramaseshan (2006)

CRM is the process for achieving a continuing dialogue with customers, across all available touch points, through differentially tailored treatment, based on the expected response from each customer to available marketing initiatives, such that the contribution from each customer to overall profitability of the company is maximized.

Strauss, El-Ansary and Frost (2003).

CRM is a holistic process of acquiring, retaining and growing consumers.

Chao et al. (2007),

CRM is technological contributions to companies and this technology surged into the market rapidly.

Chang et al. (2002)

CRM is e-business applications.

Gartner

CRM is a business strategy designed to optimise profitability, revenue and customer satisfaction.

Light (2001)

CRM evolved from business processes such as relationship marketing and the increased emphasis on improved customer retention through the effective management of customer relationships.

Girishankar (2000)

CRM is a holistic and complex strategy.

Piskar & Faganel (2009)

CRM is a new concept, characteristically centred to the customer and not to the product. Source: own elaboration

2. Methodological Premises of Model Formation Building of customer relationship management system in company is a complicated process. Different authors present a lot of variants of CRM implementation. They distinguish similar elements influencing the CRM building in company. A simple CRM model is presented by Sin, Yim and Tse (2005). It presents four elements groups: customer’s characteristics, management of knowledge (information about customers), CRM structure (organisation structure, sources,

human resources, etc.) and CRM substantiation by IT technologies. In opinion of Clark, McDonald and Smith (2002), successful CRM demands that members of different functions such as marketing, information technology and human resource management work together. Conditions necessary for effective CRM are appropriate marketing strategy, IT systems and organizational culture. Then they emphasized importance of change management in the process of building CRM in company. Of particular relevance to change management are the organizational

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Perceptions of CRM

Perception of CRM as

Authors

strategy

Smith (2001), Gartner, Anderson and Kerr (2002), Girishankar (2000)

process

Gosney & Boehm (2000), Peppers, Rogers & Dorf (1999), Kumar & Reinartz (2009), Ramaseshan (2006), Strauss, El-Ansary & Frost (2003), Light (2001)

strategic approach

Dick Lee (2001), Payne (2005), Kinaid (2003), Buttle (2000), Hobby (1999), Glazer (1997)

technology

Choy (2003), Stone & Woodcock (2001), Khanna (2001), Couldwell (1999), Kurtner & Cripps (1997), Bull (2003), Sandoe, Corbitt & Boykin (2001), Chao et al. (2007), Chang et al. (2002)

model

Piskar & Faganel (2009), Levine (2000), Sin, Yim & Tse (2005), Clark, McDonald & Smith (2002), Phelps (2001), Cohn (2002)

science

Kopf (2000) Source: own elaboration

culture and climate conditions. They determined four elements important for effective CRM: positive organizational climate, market-oriented culture, strong culture, learning climate. Anderson and Kerr (2002) are in the conviction that success of the CRM is in conformity of technology with CRM strategy. This affects and determines company’s organizational structure, which again has influence on choice of appropriate technology. This step order has to be kept. It is essential to begin the process of building CRM in company by defining CRM strategy. Interesting relationship management model presents Phelps (2001), it contains four basic steps: 1. Segmentation. 2. Present behavior analysis. 3. Development of the strategy for reaching target behavior. 4. Keeping of this behavior. In the beginning of the process it is essential to make audit of all systems, research, marketing knowledge, opportunities, historical records and other data sources that may exist in company. On the base of this analysis we can approach to four mentioned basic model steps. A clear organizational CRM model is presented by Payne (2005), which consists of five processes (Fig. 1): 1. Strategy development. 2. Value creation. 3. Multi-channel integration.

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4. Information management. 5. Performance assessment. These processes are positioned relative to four critical elements of a successful building of CRM in company: CRM readiness assessment, CRM change management, CRM project management and employee engagement. Payne (2005) puts emphasis on the evaluation of current situation in the way of readiness of the company and willingness of management apply company strategy oriented on customer, before the formulation of CRM strategy. CRM is not an appropriate strategy for a company to adopt if it does not have the leadership of the enterprise engaged in supporting CRM. He points at the fact that CRM is about leveraging relationships for mutual benefit through the skilful utilization of customer knowledge. But it is also about building stronger and more productive relationships with other stakeholders, particularly employees. In opinion of Payne (2005), the main source of competitive advantage today is customer intimacy achieved through excellent customer service. Here employees have a critical role to play in its delivery. Cohn (2002) developed a model of CRM focused more on organizational structure than on the technology itself. He argues that CRM is organizational model. His model of CRM centers on the structural concepts of formalization, centralization, complexity, and integration. The model proposes that organizations formalize

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Organizational model of CRM

Source: [29]

CRM practices such as the Technologies and practices that enable the collection and analysis of customer information. Summarising viewpoints of various authors, it would be possible to highlight that in order to successfully implement CRM, it is necessary to balance and integrate technologies, processes and people. These elements are closely related to company’s strategy.

3. Empirical Research – Situation in Slovak Enterprises From March 2007 to February 2009 we carried out the research specialized in diagnostics of the level of Slovak companies in the CRM area. For better understanding the value of the research, we addressed medium and large businesses. 230 top managers of Slovak medium (79 %) and large (21 %) businesses participated in the research. Search subject has been companies acting in all branches of national industry on area of Îilina self-administrative region. Specifically it is about companies, relegated as medium and large companies on the base of employee count by Statistical Office of the Slovak Republic. Target group of the research are companies. For these companies it is essential to fulfill below written criteria of assortment to be sorted as target group:

Acting on area of Îilina self-administrative region, Employees count higher than 50. On the base of these criteria it can be said, that target group consists of medium and large companies, acting on area of Îilina selfadministrative region. Object of the research (final respondents) are managers from middle or top management in these companies. Representative technique has been chosen as sample selection method. To be specific, technique of base selection has been used. This technique uses full-range searching. Sample size represents 210 of respondents (medium and large companies) by required 95% interval of reliability and maximal admissible fault 5 % [35]. Actual count – 230 respondents says that sample of asked companies may be considered as representative. Data gathering was running by two main ways – by personal questioning and by electronic questionnaire. Telephone contact or e-mail communication was made before personal questioning. Internet environment was also used for the data gathering. Electronic questionnaire was made through PHP and was placed on internet site of the faculty: http://fria.fri.uniza.sk/~lendel/dotaznik.php. By personal questioning it was gathered 121 questionnaires, which represents 53 % of all questionnaires. Electronic questionnaire was

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Ekonomika a management filled by 109 managers from medium and large companies. The purpose of the research was to find and analyse the current level of CRM area on the base of identifying main factors that affect Fig. 2:

the level of using CRM information system and process of implementation in the company. The current situation of CRM application based on results of the research is presented in Fig. 2.

Status of CRM in Slovakia

Source: own research

Almost one fourth of respondents did not deal with this problem. In the phase of study is 10 percent of respondents, 7 percent is in decision-making phase of CRM application Fig. 3:

importance for the company. 11 percent of respondents implements CRM in the company's practice. Almost half respondents (49 percent) said that CRM is in full operation in the company.

Most Important Phase of Process of CRM Implementation in Company's Opinion


70 percent of respondents said that conception is the most important phase of CRM implementation process (Fig. 3). This phase informs about necessity of exactly defined

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criteria and conditions. 4 percent said that phase of selection, but on the other hand 17 percent said that phase of implementation is the most important. Main target of this phase is

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Ekonomika a management to successfully adapt the software and organizational structure. The phase of implementation finishes with testing and system realisation. 9 percent said about phase of realisation that it’s the most important. For successful CRM information system implementation, it is necessary to have skilled employees. Assurance of regular communication is the most important in this phase. Respondents had available a 10-point scale, where 1 means "marked deterioration" Fig. 4:

and 10 means "significant improvement". As seen in Fig. 4, implementation of CRM in the company has greatly contributed to increasing the availability and quality of information processing. Also it has significantly improved response to customer requests and follow-up processes across the company. It was increased labour productivity and profitability. The smallest impact was the implementation of CRM on the number of complaints and the cost of advertising and marketing.

Impact of CRM Implementation by Individual Indicators


As seen in Fig. 5, respondents have considered the most important preconditions for successful implementation of CRM into the enterprise strategy and planning (8.66), effective work with information (8.55) and highquality customer base (7.93). The smallest importance they have attributed organizational structure (5.69). The top managers identified the key problem areas of CRM implementation in the company. They selected the following problems: Low level of staff motivation (47.8 %), Insufficient details about processes and information flow (31.3 %), Mismatched definition of requisites before implementation (34.4 %), Permanent distrust of new technology (30.4 %), Change of customer demands (27.83 %),

Loss of coordination (reason: very long process of implementation) (26.52 %), Insufficient trust between management and staff (25.65 %), Insufficient consulting before installation (25.22 %). As much as 54 % asked companies consider application of CRM into company as continuous process. 12 % of respondents quoted that CRM implementation lasted more than 12 months. 14 % of asked companies say that process lasted from 8 to 12 months, other 11 % claim that duration was from 4 to 8 months and about 10 % of respondents said the whole implementation process lasted less than 4 months. Besides building the CRM in the company, 24 % of asked companies used outsourcing services. 51 % of respondents implemented CRM using own sources.

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Preconditions for Successful CRM Implementation


4. Hexagonal stellar model of CRM Part of theoretical outputs of problem solution is also attempt to feature knowledge in the form of hexagonal stellar model of CRM (Fig. 6), which contains key elements influencing the CRM building in company. It consists of two triangles, each with elements representing main factors that influence CRM building in company. Referring to standpoints of many authors on the CRM implementation, empirical research and

Fig. 6:

having analysed the structure of CRM models presented by them, the following elements of model were chosen: People, Processes, Technologies, Strategy, Organizational structure, Management.

Hexagonal Stellar Model of CRM

Source: own elaboration

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Ekonomika a management Important thing is to keep all relations and relationships between single elements of the model proceeding with the orientation on the customer. Customer relationship management is in the sense of hexagonal stellar model understood as a compact system which includes vision, strategy, values of company culture and policy, company processes, their sources, goals and metrics with direct relation to customer. In the focus centre of hexagonal stellar model is customer and its needs. Output of the model is created value, which brings expected profits to customers. It is formed by product (service) attributes, company image and relation to the customer. The first triangle contains the key elements that form the main proportion (base) of the CRM. They are: People, Processes, Technologies. Customer relationship management aims to provide strategic connection between information technologies and company strategy, aimed at building long-term relations. Progress in the information technologies area provided, the companies with methods of collection, saving, analyzing and sharing information about customers, which rapidly increased their ability to react on each customer’s needs, to attract new and maintain current customers. Information technologies support and make easier customer relationship management, even with higher amount of customers. Other important and indispensable element in the triangle are processes. All business processes in company should be oriented on customer. There are processes that are directly connected with customers during purchase, payment or usage of company products and services. Communication and requests equipment must have set up clear processes, describing sequence of all actions needed to be done inside the company for equipment of the customer request in the shortest time period. The most important element within the triangle are people. Success rate of building long-term and mutually profitable relations with customers will depend on performance and approach of people. For customers they are first persons to have contact with. Customers can make picture about the whole company on

the base of conversation with them. Ability to satisfy customer needs depend on their knowledge and skills. Unqualified employees can harm not only customer, but especially company. Company cannot satisfy only with obtaining qualified employees, but has to develop their knowledge and skills at the same time. People have a huge impact on the success of the CRM processes. Successful and effective customer relationship management people tend to display the following characteristics: positive attitude, people orientation, organizational skills, analytical skills, customer focus (natural empathy), understanding of the link between CRM and profitability. Having a customer-focused mindset is important in providing exceptional customer service. Applying effective communication skills is equally important. Traut (2008) states a list of the most important communication abilities: presence, relating, questioning, and building rapport, listening, check backs, choosing words. The second triangle contains elements that are important from the view of securing CRM function in company and are essential for building the CRM model. They create environment, in which the CRM can be built, and also resources and ways to achieve it. These elements are as follows: Strategy, Organizational structure, Management. Support of top management and involvement of all employees is essential for successful CRM building in company. Creating the strategy is also important, same as in each key step in company run. Strategy with solid rules, but able to modify on the base of specific company conditions. These facts and changes have to reflect also in a new organizational structure of the company. Successful strategies require a properly matched organization structure. If an organization significantly changes its strategy, it needs to make appropriate changes in its overall structural design [16]. Key element of CRM building in company is management. It represents main element on the connection between company and customer. For its function it needs mainly information from the CRM system, collected by other departments. It has many tools insuring promptness in communication with customer and elimination of unnecessary contacting of specific departments

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Ekonomika a management in order to find basic information, by that it supports customer satisfaction, makes work more effective and saves time of all participated. Managers play meaningful and unsubstitutable role in the process of building CRM in company. A manager cannot carry out his/her decisions by himself/herself, without employees’ active and creative co-operation [27]. In terms of project managing it is essential to determine key roles: project manager, CRM manager and persons responsible for particular phases of implementation. Managers should have certain privileges to make decisions and also will to make decisions, all of that in the shortest time period. These persons are owners of the problems and make decisions between solution alternatives. In case that these roles wouldn’t be determined in company, it may harm all the CRM initiative. Project manager should be known in the phase of selection procedure. He should be responsible for the project from task takeover until achieving its goal. CRM manager represents initiative in the company. His role is not ending by successful implementation (as in position of project manager), but involves activities related to change management, cooperation with participated company managers on other development and operation of the CRM. Other important element of the triangle is CRM strategy. It is summary of strategic decisions, on the base of which the CRM in company is realizing. CRM strategy is closely connected with company strategy, therefore it has to involve basic characteristic of this strategy. Its main goal is providing successful relationship building with customers. Presumption of reaching this goal is orientation on customer in the whole company. Customer processes in company have to be supported by suitable organizational structure, which will allow managing relationship with customer and adjust the offer to their needs and wishes. These relationships have direct influence on rationalization, optimization and total streamline of all activities related with these relationships. Recommendation for successful CRM building in company resulting from hexagonal stellar model: creating two roles: CRM manager and project manager, conforming of three areas: people, processes and technology,

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CRM strategy has to come out from company strategy and therefore fully involve its basic characteristics.

5. Analysis of the linkages between management and CRM If in the model replace the elements of management area, we get a second level model, which shows the main linkages between management and CRM. Management represents a major element on the junction between businesses and customers. For its work needs from the CRM system information gathered primarily other departments. It has more instruments guaranteeing the speed of communicating with customers and eliminate unnecessary contact a specific department for investigation of elementary information, thereby promoting customer satisfaction, streamline work and saves time for all involved. There is double bond (Fig. 7) between management and CRM. CRM provides clear evidence needed for strategic decision-making. Management is reflected in CRM primarily through the following areas: strategic management, change management, project management, process management and human resources management.

5.1 Strategic Management and CRM Strategic management in the field of CRM applies particularly in formulating CRM strategy, preceded by a detailed analysis of the current situation in the enterprise (Fig. 8). The process of creation of CRM strategy by Payne (2005) includes determining the type of business and customer strategy and ensures their integration. The concepts of strategy in the context of CRM best describe Normann and Ramirez (1993). Their definition emphasizes the nature of customer relationships. Strategy considered a way of creating value. According to them, the strategy provides the intellectual structure, major conceptual models and ideas that allow business managers to learn ways to help customers and ways to be profitable by using [28]. Payne (2005) emphasizes that the task of customer relationship management is not creating a business strategy. Conversely, it is about understanding that to create an

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Illustration the Links between Management and CRM


appropriate CRM strategy. Also, the enterprise should indicate how it should be the strategy evolves over time. The CRM strategy is essential to be connected and support business strategy (Payne, 2005). In the process of creating a CRM strategy must apply strategic thinking, which is characterized by continuous analysis of environment, customer orientation, and readiness for change, integration, concentration and learning resources. To carry out the necessary analysis of the current situation inside and outside the enterprise are used, different methods of strategic management. The most common cause of CRM failure in the enterprise may be a lack of customer-driven strategy. Managers undertaking may be advisable to review business objectives and current business strategy and reflect the expectations and requirements of customers in business strategy.

5.2 Change Management and CRM Change management is applied in CRM in particular when assessing the readiness for CRM and business decisions to move to new or expanded CRM initiatives. When a company defines each of the key processes of CRM, such as development strategy, creating value, the integration of communication channels, information management and performance assessment must consider the implications of

any change in one process. The introduction of large and complex enterprise initiatives, CRM will have to undergo organizational and cultural changes. A crucial aspect of the business will therefore be an effective change management program. Changes necessary due to construction of CRM in the enterprise are clearly serious. There are a certain number of potential barriers that may prevent this change, for example, entrenched interest in maintaining the status quo. Understand and act on the basis of requirements change management is therefore a prerequisite for building a successful CRM. The most common problem is to eliminate human factor from the process of CRM building in business. It should be noted that the most important change is technical change. Top management should allow employees to participate in change, to welcome their ideas and explain the importance of CRM. Their actions should lead primarily to ensure trust between management and employees. Top management must consider the impact of change on people, involve them in preparing for change and warn them of the reasons leading to the change, including the effects and benefits resulting from the changes.

5.3 Project Management and CRM Successful CRM projects are based on CRM objectives, which are derived from business

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The Strategic Framework for CRM


goals and should support and complement the overall business strategy. Effective project management is in the process of CRM building in business much needed, as experience has shown that projects are over budget and time span can cause considerable damage. For executives and managers of CRM projects is important to understand the role of information technology in implementing CRM. There are several reasons why the use of information technology in CRM: ensuring efficiency, create more value for customers through a better understanding of customer needs and improve the customer experience and reduce costs. If there is a proposed investment on information

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technology in CRM authorized, it shall be again considered. Poorly drafted plan is often a common problem in CRM building process in the enterprise. Managers are too reliant on technology and based on insufficient documentation of business processes and information flows. Managers undertaking may be encouraged to focus their attention on three areas of security: people, processes and policies, use the tools of project management process, undertake a detailed analysis of the current situation in the enterprise and to understand the role of technology in CRM building (just as a support tool).

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5.4 Process Management and CRM The basis for the operation of any enterprise is its processes. According to Card and Kunstová (2001) are fundamental to the functioning of a modern competitive enterprise in the global society of automated processes. The basic objective of process management in CRM building in enterprise is performing in the customer process. Process management provides an integrated view of all business activities, which integrates into the various processes. For each activity knows who made it, how it is implemented, so it made as it restricted the implementation of activities, which comes into operation, as it acts, familiar to internal customers, they can spread the overhead costs to each activity processes, each activity is defined performance indicators, etc. Other strength of process management is to implement any changes induced in the process of CRM building in process-driven enterprise. The most common problem in process management is the automation of previous failed procedures. Managers must identify the missing processes; detailed knowledge processes related to customers, their evaluation and, if necessary, must accede to the re-engineering processes and to optimize them. Sufficient attention must be devoted to analyzing the current state of business processes. Creating own process model of CRM can be as a suitable tool for this analysis for managers. A key prerequisite for a successful transition to Relationship Marketing and CRM building in enterprise is a perfect mapping of current business processes. Attention focuses on processes for customers. It is necessary to identify and follow-up optimization.

5.5 Human Resources Management and CRM The most important element of CRM may be people (employees). From their performance and approach will depend on the success of building long-term mutually beneficial relationships with customers. They are the first people with whom customers come into contact. The customers are formed the image of the entire enterprise in an interview with them. From their knowledge and skills depend on their ability to meet customer needs. Unskilled workers can not only harm the customer, but also enterprise. He can not only satisfy the acquisition of skilled

staff, but must also develop their skills and abilities. There is scope for the application of human resource management. According to Bla‰ková (2003), employees and their potential are effective and strategic competitive advantage. Now people, their motivation, knowledge, skills, abilities, creativity, flexibility becomes the most important strategic resource of successful CRM building in the enterprise. Employees prepared an analysis of the situation, set goals of CRM; formulate CRM strategy, action plans and control system efficiency and effectiveness of CRM system. In the transition to enterprise CRM employees play a key role. Appropriate motivational program, providing opportunities for further education and creating a pleasant working environment, an enterprise can be achieved smoothly and subsequent implementation of the system is functioning customer relationship management.

Conclusion Customer relationship management is closely related to management. It provides valuable information forming the input for strategic decisions. Customer relationship management is primarily a process involving the complete reshaping corporate culture and value system. This is necessary to apply the principles of strategic management. Success in this area will depend on the management of the company, whether rightly understand the true importance of CRM to the enterprise. If the management of company understand it only as a CRM technology and not as business behaviour to customer, then the whole project will be doomed to failure and extinction. It should be noted that people are the most important element of customer relationship management. Applying appropriate motivational tools, creating favourable conditions and ensuring open communication between top management and employees, holding company only reaches smoothly the CRM implementation but also its continued use in the future. The implementation of CRM in the enterprise can’t underestimates the issue of process management. The enterprise must be analyzed in all processes of the customer relationship. Top management must feel ownership of each from the key processes and be responsible for their quality and performance.

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Ekonomika a management Implementing CRM in the enterprise is the organizational change. Change management helps achieve business success in implementing change. It is necessary to formally assess user requirements and visions. Managing change and CRM implementation must be closely linked. Managing CRM implementation can’t restrict only to manage the project. The enterprise must establish a quality project team and set project goals. Project manager is responsible for running the implementation, its performance, documentation and periodic reports for top management.

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Ing. Viliam Lendel, PhD. University of Zilina Faculty of Management Science and Informatics Department of Management Theories [email protected] Ing. Milan Kubina, PhD. University of Zilina Faculty of Management Science and Informatics Department of Management Theories [email protected]


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Abstract HEXAGONAL STELLAR MODEL OF CRM – KEY ELEMENTS INFLUENCING THE CRM BUILDING Viliam Lendel, Milan Kubina In present, most companies are conscious of evident contribution of CRM and almost every company is using CRM technologies that support their business or evaluating specific contributions of CRM for company and planning its realization in future. Implementation of CRM system does not assure change of single company processes. The company does not automatically become customer oriented and customers don’t become more loyal or gainful for company. The scientific paper analyses variants of CRM conceptions by reviewing different models of CRM creation, analysis of which allowed envisaging typical elements influencing the CRM building in company. The analysis of scientific literature allowed revealing and theoretically finding the suitability of individual key elements for basic CRM system formation. By comparing different CRM models and applying the inductive method a hexagonal stellar model was designed. Main goal of the study is to contribute to the larger successfulness of organizations that decide for building of CRM. In the first and second section the paper consider the concept of CRM and how CRM systems are reported upon in the literature. In the third section the paper describes the empirical research. The purpose of the research was to find and analyse the current level of Slovak companies in the CRM area on the base of identifying main factors that affect the level of using CRM information system and process of implementation in the company. We addressed medium and large businesses. 230 top managers of Slovak medium and large businesses participated in the research. The last section of the paper deals with identification of key linkages between management and CRM. Management is reflected in CRM primarily through the following areas: strategic management, change management, project management, process management and human resources management. CRM provides clear evidence needed for strategic decisionmaking. Key Words: CRM, management, customer, strategy, employees, research. JEL Classification: M15, M31.

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MATICOVÉ MODELY NA MERANIE V¯KONNOSTI PRODUKâN¯CH SYSTÉMOV Michal Grell, Eduard Hyránek

Úvod DôleÏitou podmienkou investiãného a finanãného rozhodovania v podniku je meranie jeho v˘konnosti. Pri meraní v˘konnosti podniku moÏno aplikovaÈ moderné prístupy, ktor˘ch úãelom je posúdiÈ efekty vstupov a v˘stupov produkãn˘ch systémov (podnikov). V súãasnosti, v záujme detailnej‰ieho posúdenia v˘konnosti podniku, nie je moÏné uspokojiÈ sa s klasick˘mi ukazovateºmi finanãnej anal˘zy – úãinnosti, nároãnosti alebo rentability. MôÏu byÈ v‰ak základom na ìal‰ie alebo hlb‰ie skúmanie v˘konnosti matematick˘mi metódami, aj keì na rozhodovanie o investíciách a financovaní pre finanãn˘ch manaÏérov firmy moÏno pouÏiÈ aj ukazovatele, zaloÏené na hodnotovom riadení podniku, ako napr. EVA (Economic Value Added), CVA (Cash Value Added), MVA (Market Value Added), CFROI (Cash Flow Return on Investment), SVA (Shareholder Value Added) a iné. V˘konnosÈ ekonomiky podniku analyzujeme vyuÏitím maticového systému ukazovateºov, v ktorom sa aplikuje ich rôzna kombinácia. Maticov˘ model umoÏÀuje rie‰enie ne‰tandardn˘ch úloh operaãného v˘skumu, ktoré vyÏadujú interdisciplinárny prístup odborníkov rozliãn˘ch profesií. Maticov˘ model reprezentuje urãitú ekonomickú ‰truktúru, ktorej opis je moÏn˘ kombináciou ukazovateºov vstupov a v˘stupov podnikovej ekonomiky. Anal˘zou takejto ‰truktúry moÏno v˘poãtami získaÈ aj ìal‰ie ukazovatele, ktoré charakterizujú v˘konnosÈ podniku. V˘sledky sú vyuÏiteºné pre externé subjekty – investorov, finanãné in‰titúcie (banky), dodávateºov a tieÏ na správu a riadenie spoloãnosti (corporate government). MôÏu byÈ súãasÈou pravidiel a princípov upravujúcich vzÈahy medzi exekutívnym vedením spoloãnosti (manaÏéri, zamestnanci) a jej ‰tatutárnymi orgánmi, akcionármi a ìal‰ími zainteresovan˘mi

stranami. UmoÏní sa tak transparentnej‰ie monitorovaÈ realizáciu stanoven˘ch cieºov a zisÈovaÈ príãiny ich neplnenia.

1. Základné problémy merania v˘konnosti produkãn˘ch systémov V systémovej teórii sa systém chápe ako úãelovo definovaná mnoÏina prvkov a mnoÏina väzieb medzi prvkami, ktoré spoloãne urãujú vlastnosti celku. Produkãn˘ systém chápeme ako otvoren˘ systém, ktor˘ vstupy zo svojho okolia transformuje na v˘stupy, ktoré opäÈ poskytuje okoliu. Vstupy do systému sú zdroje, ktoré potrebuje podnik (organizácia) na vytváranie poÏadovan˘ch v˘stupov. MôÏu to byÈ ºudské zdroje, suroviny, materiál, stroje, zariadenia, energia, kapitál, informácie a pod. V˘stupy zo systému môÏu byÈ nielen v˘robky, ale aj sluÏby alebo informácie pre zákazníkov. Tieto v˘stupy súhrnne naz˘vame produkty. Transformaãn˘ proces je spôsob transformácie vstupov na poÏadované v˘stupy [1]. Produkt a transformaãn˘ proces sú základné zloÏky produkãného systému. V‰eobecne moÏno kon‰tatovaÈ, Ïe meranie a zlep‰ovanie v˘konnosti podniku predstavuje posudzovanie jeho schopnosti dosahovaÈ ciele optimálnym spôsobom. Anal˘za vzájomného vzÈahu cieºov, vstupov a v˘stupov produkãného systému umoÏÀuje meranie a zlep‰ovanie v˘konnosti produkãn˘ch systémov z dvoch hºadísk: a) z hºadiska efektívnosti dosahovania podnikov˘ch cieºov (úspornosÈ, úãelnosÈ), b) z hºadiska spôsobu realizácie (úãinnosÈ). Na základe kvantifikácie vstupov, v˘stupov, cieºov a transformaãného procesu moÏno potom definovaÈ jednotlivé ukazovatele v˘konnosti, a to [1]:

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úspornosÈ – minimalizovaním nákladov pri obstarávaní vstupov so zreteºom na ciele, úãinnosÈ – vyjadrením vzÈahov medzi vstupmi a v˘stupmi v transformaãnom procese, úãelnosÈ – vzÈahuje sa na v˘stupy so zameraním na uspokojovanie potrieb zákazníkov. Je zrejmé, Ïe ukazovatele úspornosti, úãinnosti a úãelnosti moÏno kon‰truovaÈ rozliãn˘m spôsobom. V procese merania v˘konnosti podniku (Performance Measurement) [14] predstavujú systém finanãn˘ch a nefinanãn˘ch ukazovateºov – kºúãov˘ch indikátorov v˘konnosti (KPI – Key Performance Indicators). V súãasnosti sú tieto systémy zaloÏené na integrácii finanãnej anal˘zy a anal˘zy strategick˘ch faktorov úspe‰nosti podniku. Príkladom takéhoto systému je Balanced Scorecard (BSC), ktor˘ predstavuje prepojenie cieºov finanãnej v˘konnosti podniku s jeho strategick˘mi cieºmi [5]. Nástrojom merania v˘konnosti sú metriky, t.j. presne vymedzené finanãné alebo nefinanãné ukazovatele alebo hodnotiace kritériá, ktoré sa pouÏívajú na hodnotenie úrovne v˘konnosti v konkrétnej oblasti podniku [14]. Metriky vytvárame spravidla uplatnením dvoch hºadísk [14]: hºadisko pridanej hodnoty; Meriame produkt procesu (v˘stup), vytvorenú pridanú hodnotu a vloÏené zdroje, spotrebu na vytvorenie pridanej hodnoty (napr. EVA, MVA a pod.).

Tab. 1:

hºadisko parametrizácie; Meriame parametre procesu, ako napr. priebeÏná doba procesu (doba od prvého vstupu zdroja do procesu aÏ do vytvorenia v˘stupu), priechodnosÈ procesu (mnoÏstvo produktu vytvoreného v danom ãase) a pod. Parametre by mali poskytovaÈ objektívne a presné informácie o priebehu jednotliv˘ch procesov (napr. univerzálne parametre v˘konnosti procesov, parametre v˘konnosti v˘robn˘ch a nev˘robn˘ch procesov, meranie v˘konnosti podºa odch˘lok alebo indexu v˘konnosti a pod.). Rovnako dôleÏitá je jednoznaãná formulácia väzieb ukazovateºov, ktorá má na kvantitatívne hodnotenie ekonomickej reality rovnak˘ vplyv ako jednoznaãná definícia jednotliv˘ch ukazovateºov (metrík), prvkov systému. Potom vymedzenie väzieb prvkov je súãasÈou definície akéhokoºvek systému. Ak teda hovoríme o systéme ukazovateºov, mali by byÈ medzi príslu‰n˘mi ukazovateºmi definované väzby. Formulácia väzieb ukazovateºov je predpokladom na prechod od sústavy ukazovateºov k vy‰‰iemu kvalitatívnemu stupÀu: systému ukazovateºov [16]. Základné ãlenenie ukazovateºov uvádzame v tab.1. Väzby ukazovateºov (indikátorov) môÏu byÈ v podstate vyjadrené slovn˘m opisom, graficky a matematicky. ëalej sa zaoberáme matematickou formuláciou väzieb.

Základné ãlenenie ekonomick˘ch ukazovateºov Ukazovateº

Struãná charakteristika

Absolútny

Vyjadrenie urãitého javu bez vzÈahu k inému javu.

Relatívny

Vyjadrenie veºkosti jedného javu, pripadajúceho na mernú

Podielov˘

VzÈahov˘

Index

Stavov˘

Tokov˘

Syntetick˘

Analytick˘

jednotku druhého javu. ZávislosÈ hodnôt ukazovateºov od dæÏky ãasového obdobia, o ktorom vypovedá. KomplexnosÈ v˘povede o ekonomickej realite. Zdroj: Vlastné spracovanie podºa [16]

Podrobnej‰ie sa zaoberáme ukazovateºmi z hºadiska pridanej hodnoty a vychádzame z toho, Ïe v˘poãty sú urãené ist˘m koneãn˘m súborom ãíseln˘ch údajov (vstupy) a koneãn˘m súborom vyãísliteºn˘ch funkcií t˘chto vstupov. Na‰ím cieºom je urãiÈ hodnoty t˘chto funkcií (v˘stupy). Formálne to moÏno zapísaÈ ako transformáciu typu:

74

y = f (x) (1) kde x je vektor vstupn˘ch údajov, y – vektor v˘stupn˘ch údajov, f = fi, i=1, ..., k – transformácia, ktorá môÏe zah⁄ÀaÈ cel˘ komplex procedúr transformácií.

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2. Maticové usporiadanie ukazovateºov v˘konnosti

náciou ukazovateºov súvahy a v˘sledovky, vstupov a v˘stupov a pod. Príkladom môÏe byÈ sústava ukazovateºov, ktorá vznikne kombináciou ukazovateºov súvahy a v˘sledovky, napríklad vektor ukazovateºov súvahy s = (aktíva, kmeÀové imanie, dlhy, investiãn˘ majetok, hmotn˘ investiãn˘ majetok, obeÏn˘ majetok, zásoby) a vektor ukazovateºov v˘sledovky v = (v˘nosy, pridaná hodnota, ãisté v˘kony, hrub˘ a ãist˘ zisk) podºa tab. 2. Samozrejme, jednotlivé prostriedky formulácie väzieb moÏno kombinovaÈ. Napríklad v pyramídovej sústave moÏno zapísaÈ aj matematické väzby ukazovateºov. V niektor˘ch sústavách nie je v‰ak typ matematickej väzby explicitne uveden˘, ale môÏe vypl˘vaÈ z názvu typu sústavy a slovného opisu väzby (napr. v bilancii sa automaticky predpokladajú aditívne väzby ukazovateºov). ëalej sa budeme zaoberaÈ matematick˘m modelom systému ukazovateºov.

Matematická formulácia väzieb môÏe byÈ reprezentovaná napr. maticovou sústavou, ktorá vznikne vertikálnou a horizontálnou kombináciou ukazovateºov [16]. Ukazovatele usporiadané vertikálne predstavujú riadky matice a stæpce matice vytvárajú ukazovatele usporiadané horizontálne. Ich kombinácia tvorí prvky matice, ktoré môÏu reprezentovaÈ rozliãné typy pomerov˘ch ukazovateºov.

2.1 Matica ukazovateºov a kon‰trukcia maticového modelu Pomerové ukazovatele môÏu opisovaÈ ekonomické javy na makroekonomickej, ale aj mikroekonomickej úrovni. ëalej sa zaoberáme mikroekonomickou (podnikovou) úrovÀou, a potom prvky matice môÏu byÈ reprezentované kombi-

Tab. 2:

Maticová sústava ukazovateºov

s–v

v1

v2

s1

v1/ s1

v2/ s1

s2

v1/ s2

v2/ s2

v1/ si

v1/ sm

...

vj

...

vn

vj/ s1

vn/ s1

v2/ si

vj/ si

vn/ si

v2/ sm

vj/ sm

vn/ sm

. . . si . . . sm

Zdroj: Vlastn˘ formálny zápis podºa [16]

Maticov˘ model podniku formálne opisuje závislosÈ medzi jeho ekonomick˘mi veliãinami. Modelom moÏno rie‰iÈ rozliãné reálne problémy, priãom mnohé z nich sa vyskytujú opakovane. Majú charakter typov˘ch problémov a v operaãnom v˘skume – ktor˘ predstavuje rozvoj a aplikáciu metód urãen˘ch na podporu manaÏérskych rozhodnutí sa v súvislosti s pouÏívaním matematick˘ch modelov a metód v riadení, najmä v USA, operaãn˘ v˘skum naz˘va priamo veda o manaÏmente (management science) – sú prezentované ako ‰tandardné

problémy a zodpovedajú im aj ‰tandardné modely (napríklad ‰truktúrne modely, modely a metódy matematického/optimálneho programovania, modely a metódy dynamického programovania, sieÈového plánovania, stochastické a simulaãné modely a metódy) [15]. Na rie‰enie úloh, ktoré sú spojené so ‰tandardn˘mi modelmi slúÏia aj ‰tandardné, dobre prepracované a softvérovo podporované metódy. Ne‰tandardné úlohy sa odchyºujú od ‰tandardn˘ch ‰truktúr a metód rie‰enia. Ak akceptujeme lineárnosÈ vzÈahov medzi ekonomick˘mi veliãinami,

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Ekonomika a management potom v‰eobecne sú maticové modely (napr. vo forme úloh lineárneho programovania alebo v inom tvare) vhodné na kvantitatívnu anal˘zu tak˘chto vzÈahov. Prezentovan˘ maticov˘ model zaraìujeme medzi ne‰tandardné úlohy operaãného v˘skumu, ktoré vyÏadujú interdisciplinárny prístup odborníkov rôznych profesií. V˘chodiskom na formuláciu maticového modelu sú absolútne ukazovatele, ktoré usporiadame podºa tab. 3. Ak tieto ukazovatele ìalej rozdelíme na ukazovatele v˘sledkov (v˘stupov) – v – a nárokov (vstupov) – n – ekonomiky napr. v oblasti v˘konnosti podniku, tak moÏno kon‰truovaÈ ãíselné indikátory pomocou vzÈahu v/n, ktoré sú z hºadiska charakteru relatívne ukazovatele a z hºadiska kon‰trukcie sú kombináciou vzÈahov medzi absolútnymi ukazovateºmi typu vstup a v˘stup. V˘znam pouÏit˘ch oznaãení v tab. 3 je tak˘to [2]: v je stæpcov˘ vektor v˘stupov rozmeru n, n – stæpcov˘ vektor vstupov rozmeru m,

Tab. 3:

A – matica úãinnosti vstupov rozmeru m.n, kde aij = vj/ni, C – matica nároãnosti v˘stupov rozmeru n.m, kde clk = nk/vl, B – matica ‰truktúry vstupov rozmeru m.m, kde bik = nk/ni, D – matica ‰truktúry v˘stupov rozmeru n.n, kde dlj = vj/vl.

2.2 Formulácia základn˘ch vzÈahov v maticovom modeli Rozdelenie ukazovateºov podºa tab. 3 umoÏÀuje odvodiÈ niekoºko vzájomn˘ch vzÈahov medzi maticami A, B, C a D a vektormi v a n. Platia napr. tieto vzÈahy: A v′ = n n′ (2) Cn′ = mv′ (3) AC = nB (4) (5) ATn = mv (6) CTv = nn

Maticov˘ zápis ukazovateºov v–n

1, 2, 3, .., j, ..., n

n–v

1, 2, 3, ..., k, ..., m

v1, v2, v3, ..., vj, ...vn

1

n1

2

n2

.

.

.

.

i

ni

m

nm

n1, n2, n3, ..., nk, ...nm 1 B = (bik)

A = (aij) 1 1

.

1

v1

2

v2

. .

.

l

vl

D = (dlj)

C = (clk)

. n

vn

1 Zdroj: Vlastn˘ formálny zápis podºa [2]

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Ekonomika a management UÏitoãné je aj sledovanie v˘voja ∆v/∆n. V‰eobecne platí, Ïe ∆ môÏe predstavovaÈ vzÈah absolútny, prírastkov˘, diferenciálny, diferenãn˘ a pod., priãom / môÏe vyjadrovaÈ typ podielového, rozdielového, prípadne iného vzÈahu. Uvádzame niektoré moÏné vzÈahy v prírastkovom tvare podielového typu (napr. vzÈah (11) moÏno odvodiÈ vhodnou úpravou vzÈahu (7) a to: AT∆n∆n′T = m ∆v∆n′T): (7) AT∆n = m ∆v (8) CT∆v = n ∆n (9) ∆BT = ∆n∆n′T (10) ∆AT = ∆v∆n′T (11) ∆AT = m-1 AT∆BT VzÈahy v maticovom modeli moÏno potom prezentovaÈ aj kompaktn˘m maticov˘m zápisom: A

B

v′

D

C

n′

n′ = (n + m)

(12) v′

kde v′ je vektor, pre ktorého prvky platí vj′ = 1/vj, n′ – vektor, pre ktorého prvky platí ni′ = 1/ni.

2.3 Základná matematická anal˘za Nech platí, Ïe m, n sú prirodzené ãísla a pj, qi (kde i = 1, 2, ..., m a j = 1, 2, ..., n) sú reálne ãísla rôzne od nuly, potom definujeme stæpcové vektory pT = (p1, p2, ..., pn), qT = (q1, q2, ..., qm), p′T = (p′1, p′2, ..., p′n) a q′T = (q′1, q′2, ..., q′m), priãom p′j = 1/pj a q′i = 1/qi (T je znak transponovania). Definujeme súãinom q′pT súbor m . n reálnych ãísel, ktoré oznaãíme sij. Potom S = (sij) je matica o m riadkoch a n stæpcoch, pre ktorú platí (13) S = q′pT ëalej sa budeme zaoberaÈ spôsobom transformácie vektora p na vektor q alebo naopak. Úpravou vzÈahu (13) môÏeme tieto transformácie zapísaÈ takto: S p′ = n q′ (14) (15) resp.qTS = m pT Vo vzÈahoch (14) a (15) S vystupuje ako matica transformácie. Za predpokladu, Ïe poznáme maticu S a jeden z vektorov p, q je rie‰enie takejto transformácie veºmi jednoduché. Takáto interpretácia poskytuje tieÏ obmedzen˘ priestor na kvantitatívnu anal˘zu takejto transformácie.

Upravme preto vzÈah (15) na tvar (15a) ST q = m p poloÏme x(1) = p′, d(1) = n q′, x(2) = q, d(2) = m p. Potom, ak máme zadanú maticu S, vzÈah (16) S x(1) = d(1) predstavuje v˘poãet vektora p na základe zadaného vektora q a vzÈah (17) ST x(2) = d(2) predstavuje v˘poãet vektora q na základe vektora p a tieto vzÈahy rie‰ime ako v‰eobecnú sústavu m(n) lineárnych rovníc pre n(m) neznámych. Matica S má niektoré ‰peciálne charakteristiky, ktoré treba maÈ na zreteli z hºadiska formulácie typov v˘poãtov˘ch vzÈahov. Samozrejme, Ïe matice A, B, C a D v maticovom modeli sú ‰peciálnym prípadom matice S. Niektoré ‰peciálne charakteristiky matice S Matica S má tieto charakteristiky: 1. lineárna závislosÈ riadkov si-k,j = qi/qi-k . sij, kde i = k+1, k+2,..., m (18a) j =1, 2, ..., n k<m 2. lineárna závislosÈ stæpcov si, j-k = pj-k/pj . sij, kde i = 1, 2,..., m (18b) j = k+1, k+2,..., n k
m

m

1 / ∑b1k + 1/ ∑b2k + ... + 1/ ∑bmk = 1 k=1

m

k=1

(19)

k=1

m

m

1 /∑ (1/ bi1) + 1/ ∑(1/ bi2) + ... + 1/ ∑(1/ bik) = 1 i=1 i=1 i=1 (20) Niektoré aspekty rie‰enia systému lineárnych rovníc Maticov˘ zápis ukazovateºov podºa tab. 3 predpokladá realizáciu rozliãn˘ch typov v˘poãtov podºa vzÈahu (1), priãom v mnoh˘ch ekonomick˘ch aplikáciách jeho rie‰enie spoãíva v tom, Ïe zloÏitej‰í problém sa linearizuje. Algebrická linearizácia má tieto vlastnosti:

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Ekonomika a management f (x + y) = f (x) + f (y), (21) f (c x) = c f (x), kde c je ºubovoºné ãíslo. (22) Ak vyjadríme skúmané javy pomocou funkcie f, ktorá má vlastnosti uvedené vo vzÈahoch (21) a (22) tak vznikli podmienky, aby jadrom rie‰enia t˘chto problémov bolo rie‰enie systému lineárnych rovníc (SLR). Z hºadiska v˘poãtov na báze rie‰enia systému lineárnych rovníc budeme ìalej pracovaÈ so vzÈahom (16). ??Podºa predpokladu pj ≠ 0, qi ≠ 0, a teda SLR je nehomogénna. Podºa Frobeniovej vety [13] SLR má rie‰enie vtedy a len vtedy, ak hodnosÈ h(S) matice sústavy sa rovná hodnosti h(Sr) matice roz‰írenej. Ekvivalentn˘mi úpravami sústavy (16) moÏno dokázaÈ, Ïe platí (23) h (S) = h (Sr) = 1
Tab. 4:

alebo obdæÏnikovú, existuje jediná pseudoinverzná matica, ktorú tieÏ naz˘vame MoorePenroseova inverzia matice [4] s niektor˘mi vlastnosÈami inverzn˘ch matíc, pomocou ktor˘ch moÏno v kaÏdom prípade urãiÈ, ãi SLR má rie‰enie). Pseudoinverzná matica PS spæÀa niektoré z t˘chto podmienok: S PS S = S PS S PS = PS (S PS)T = PS (PS S)T = PS

(24a) (24b) (24c) (24d)

Matica PS, ktorá spæÀa v‰etky podmienky (24a) – (24d) je urãená jednoznaãne a oznaãovaná symbolom S+ ako zov‰eobecnená MoorePenroseova inverzia matice S. V praktick˘ch aplikáciách majú veºk˘ v˘znam mnoÏiny pseudoinverzn˘ch matíc, ktoré spæÀajú niektoré z vy‰‰ie uveden˘ch podmienok, napr.: S{a} – mnoÏina matíc PS, ktoré spæÀajú podmienku (24a), S{a,c} – mnoÏina matíc PS, ktoré spæÀajú podmienky (24a), (24c), S{a,d} – mnoÏina matíc PS, ktoré spæÀajú podmienky (24a), (24d), S{a,b,c,d} = S+ – mnoÏina matíc PS, ktoré spæÀajú v‰etky podmienky.

2.4 Ekonomická anal˘za – moÏnosti aplikácií Vzhºadom na vlastnosti matice S sú v podstate moÏné rie‰enia, ktor˘ch v˘chodiskom je pôvodná matica S alebo modifikovaná matica S (tab.4).

Prehºad v˘poãtov˘ch postupov v maticovom modeli

Matica S

Typy v˘poãtov

Pôvodná

A1. VyuÏitie pseudoinverzn˘ch matíc Variantné rie‰enia vstupov a v˘stupov A2. Extrapolácia ãasov˘ch radov

Zameranie v˘poãtov Prognózovanie

A3. Anal˘za ‰truktúry vzÈahov

Stabilita ‰truktúry

A4. Ekonometrické v˘poãty

Anal˘za príãinn˘ch vzÈahov

Modifikovaná B1. Rie‰enie SLR B2. Lineárna viackriteriálna optimalizácia

V˘poãet vstupov a v˘stupov Anal˘za vlastn˘ch ãísel matice Meranie efektívnosti vstupno-v˘stupn˘ch transformácií Zdroj: Vlastné spracovanie

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Ekonomika a management A. rie‰enie s pôvodnou maticou S A1. V˘poãet pomocou pseudoinverzn˘ch matíc 1.1 Ak je (16) konzistentná sústava m lineárnych rovníc pre n neznámych, definujeme pre k = 1, 2, ..., m vektory xk (x0 = 0) podºa postupu, ktor˘ je uveden˘ v [4]. Pritom v kaÏdej iterácii je xk rie‰ením prv˘ch k rovníc s minimálnou euklidovskou normou. Tento postup rie‰enia umoÏÀuje napísaÈ v‰eobecné rie‰enie konzistentnej sústavy v tvare x (u) = xm + Sm . u ,

(25)

kde Sm je matica, ktorej explicitn˘ v˘poãet nie je v tomto prípade potrebn˘ a zodpovedá príslu‰nej pseudoinverznej matici PS, ktorá spæÀa podmienky (24a), (24b), (24d), u – ºubovoºn˘ stæpcov˘ vektor reálnych ãísiel. Na základe vhodne zvolen˘ch kritérií pre vektor u môÏe tento vzÈah vytváraÈ predpoklady na v˘poãet variantn˘ch rie‰ení. 1.2 Ak postaãuje, aby matica PS vyhovovala podmienke (24a), potom jej v˘poãet realizujeme podºa vzÈahu: (26) PS = (STS)-1ST A2. Maticu S môÏeme získaÈ prognózovaním, ão predstavuje v praxi problém extrapolácie ãasov˘ch radov koeficientov sij, napríklad: na základe lineárnej funkcie (rovnomern˘ rast) ãasu v tvare g0 + g1t, kde g0, g1 sú regresné koeficienty a t je ãas, exponenciálnej funkcie (progresívny rast) e(g0 + g1t), kde e je základ prirodzen˘ch logaritmov, logaritmickej funkcie (degresívny rast) ln (g0 + g1t), kde ln oznaãuje prirodzen˘ logaritmus. MoÏno pouÏiÈ aj iné typy funkcií ãasu (parabolické, hyperbolické, logistické a pod.), ale praktické skúsenosti ukazujú, Ïe je vhodnej‰í v˘ber jednej z uveden˘ch funkcií a jej prípadné doplnenie o autoregresívnu transformáciu (hodnoty ãasového radu v beÏnom období sú priamo vyjadrené v závislosti od hodnôt v predchádzajúcich obdobiach). Posúdenie presnosti prognózy moÏno vykonaÈ známymi metódami, priãom najvhodnej‰ím základom pre extrapolaãné metódy bude tá funkcia, ktorá vedie

k minimálnej hodnote ‰tandardnej odch˘lky za v‰etky obdobia ãasového radu. MôÏeme v‰ak vychádzaÈ aj z toho, Ïe pre v˘chodiskové obdobie napr. platí DC = nC, potom na anal˘zu presnosti prognózy vyuÏijeme vzÈah (D – G) C = O, kde G je diagonálna matica, ktorá má prvky na hlavnej diagonále rovné ãíslu n a O je nulová matica. A3. Vychádzame z toho, Ïe matica S vyjadruje charakteristiky urãitej ‰truktúry vzÈahov v ekonomike podniku. Predpokladáme, Ïe tieto vzÈahy sa v ãase menia. Nech ‰truktúra v období t je reprezentovaná maticou t = 1, 2, ..., r S(t) = sij(t), i, j = 1, 2, ..., n (27) Potom v˘voj ‰truktúr v jednotliv˘ch obdobiach moÏno opísaÈ maticami S(1), S(2), ..., S(t), ..., S(r). Na vyjadrenie zmien ‰truktúry moÏno pouÏiÈ rôzne syntetické charakteristiky. Zaoberáme sa definovaním vzdialenosti t˘chto ‰truktúr, a to napríklad v zmysle (kde i, j a t spæÀajú podmienky podºa vzÈahu (27)): metriky (28)

koeficientu (29)

VzÈahy v ‰truktúre sú t˘m stabilnej‰ie, ãím je vzdialenosÈ t˘chto ‰truktúr men‰ia. ëalej je zrejmé, Ïe β ≥ 0. Rast hodnoty β signalizuje nestabilitu ‰truktúr, men‰ej hodnote zodpovedá väã‰ia stabilita a v prípade β = 0 sú vzÈahy v ‰truktúre kon‰tantné. Z hºadiska skúmania stability ‰truktúry môÏeme uvaÏovaÈ, ktorá kombinácia vstupov a v˘stupov vytvára väã‰iu stabilitu, ão môÏe ovplyvniÈ stratégiu podniku. A4. Maticov˘ zápis ukazovateºov predstavuje súãasne matematick˘ zápis urãit˘ch vzÈahov medzi ukazovateºmi, a preto umoÏÀuje formulovaÈ aj ekonometrick˘ model, ktor˘ môÏe vyjadrovaÈ konkrétny funkãn˘ vzÈah medzi vybrat˘mi ukazovateºmi. VzÈahy (2) – (6), prípadne aj (7) – (11) môÏu byÈ v˘chodiskom na definovanie jednorovnicového ekonometrického modelu. V‰eobecn˘ jednorovnicov˘ lineárny model má tvar [10]:

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Ekonomika a management B. rie‰enie s modifikovanou maticou S (30) kde yi je i-té pozorovanie závisle premennej Y, xij – reprezentujú i-té pozorovanie vysvetºujúcich premenn˘ch X1, X2, ..., Xk, βk – odhadnuté parametre rovnice, ui – náhodná zloÏka i-tého pozorovania. Maticov˘ zápis (12) moÏno pouÏiÈ aj na formuláciu viacrovnicového ekonometrického modelu [10]. Na základe (12) ìalej platí: Av′+ Bn′= (n + m) n′ Dv′+ Cn′= (n + m) v′

(31a) (31b)

V˘raz (31a) ukazuje, Ïe je moÏné uvaÏovaÈ o vzÈahu úãinnosti v˘stupov a ‰truktúry vstupov na strane jednej a podielu jednotky vstupu na vytvorení jednotky v˘stupu na strane druhej. V˘raz (31b) vytvára predpoklad, Ïe existuje vzÈah medzi ‰truktúrou v˘stupov a nároãnosÈou vstupov na strane jednej a podielu jednotky v˘stupu na spotrebe jednotky vstupu na strane druhej [6]. Viacrovnicov˘ ekonometrick˘ model vznikne vhodn˘m v˘berom rovníc z obidvoch vzÈahov.

Tab. 5:

Modifikácia matice S spojená s rie‰iteºnosÈou systému lineárnych rovníc Modifikácia matice S je spojená s rie‰iteºnosÈou SLR alebo s ekonomickou aplikáciou, ktorá je vyjadrená pomocou SLR. Tieto dva aspekty pôsobia vzájomne, a preto z dôvodov prehºadnosti zaoberáme sa obidvomi hºadiskami osobitne. a) hºadisko rie‰iteºnosti B1. Vychádzame z toho, Ïe SLR s n neznámymi, ktorej roz‰írená matica je trojuholníkového tvaru, je vÏdy rie‰iteºná. Ak je h(Sr) hodnosÈ tejto roz‰írenej matice, tak v prípade, Ïe h(Sr) = n, resp. h(Sr)
Príklad modifikovanej matice V˘nosy (Vy)

TrÏby (T)

V˘roba (V)

Zisk (Z)

Pracovníci (P)

Produktivita práce z v˘nosov

Produktivita práce z trÏieb

Produktivita práce z v˘roby

Rentabilita práce

Vlastn˘ kapitál (VK)

0

ÚãinnosÈ vlastného VyuÏitie vlastného kapitálu kapitálu

Rentabilita vlastného kapitálu

Náklady (Na)

0

0

Rentabilita nákladov

VyuÏitie nákladov

Zdroj: [2]

Takouto úpravou sa, samozrejme, potom modifikuje aj v˘chodiskov˘ vzÈah (16), ão moÏno vyjadriÈ zápisom SM x (1) = R q′, kde SM je matica, ktorá vznikne úpravou matice S a R je diagonálna matica, ktorá má na hlavnej diagonále prvky r, r-1, r-2, ..., r-m+1, kde r = n. V súlade s predpokladom m ≤ n zaoberáme sa zvlá‰È prípadom m = n a zvlá‰È prípadom, keì platí m < n.

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1.1 Prípad m = n Z hºadiska rie‰enia SLR môÏeme postupovaÈ v podstate dvomi spôsobmi: (i) vychádzame z toho, Ïe m-tá rovnica je vlastne priamym vyjadrením n-tej neznámej a postupn˘m dosadzovaním do predchádzajúcich rovníc dostaneme rie‰enie,

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Ekonomika a management (ii) rie‰ime pomocou inverznej matice, ktorá vÏdy existuje a ºahko moÏno odvodiÈ jej v‰eobecn˘ tvar. V ‰peciálnom prípade, napr. S = B, dostávame homogénnu SLR v tvare (B – R) x = 0, ktorá má nekoneãne mnoho rie‰ení, pretoÏe determinant | B – R | rovná nule. Tieto rie‰enia závisia od jedného parametra. Priestor na anal˘zu vytvára tak˘ prístup, ktor˘ vychádza z poznatku, Ïe ak S je horná alebo dolná trojuholníková matica n-tého rádu, tak diagonálne prvky sii sú práve v‰etky vlastné ãísla matice S. Obmedzíme sa na prípad, keì matica S má n lineárne nezávisl˘ch vlastn˘ch vektorov v1, v2, ..., vn, ktoré zodpovedajú vlastn˘m ãíslam s11, s22, ..., snn. Oznaãíme ∆skj (| ∆skj | ≤ ε) ?malé zmeny v prvkoch matice S. Nech ìalej sii (ε) = sii + ∆sii sú vlastné ãísla zmenenej matice S (ε) = S + ∆S. Za uveden˘ch predpokladov sa dá odvodiÈ pribliÏn˘ odhad | ∆sii | < χiε

(32)

kde χi = l/|cosαi| a αi je uhol vektoru vi a vlastného vektoru matice ST, ktor˘ zodpovedá jej vlastnému ãíslu sii. Problémom je stanovenie ε. MoÏno odvodiÈ vzÈah ∆s0 = (q0 ∆p0 – p0 ∆q0)/q0 . q1

(33)

kde index 0 je v˘chodiskové obdobie a index 1 je beÏné obdobie. Potom moÏno voliÈ ε = max {∆s0} (pre v‰etky i, j), priãom ε vyhovuje tzv. normálov˘m vzÈahom. Ekonomické normály predstavujú skupinu nerovností medzi medziroãn˘mi tempami rastu jednotliv˘ch ukazovateºov, napr.: zisk > trÏby > náklady > materiálové náklady > mzdové náklady > pracovníci, zisk > trÏby > zásoby, zisk > trÏby > DHM > mzdové náklady > pracovníci. 1.2 V prípade m < n systém rovníc nemá nikdy jediné rie‰enie. MôÏeme aplikovaÈ v˘poãet pomocou pseudoinverzn˘ch matíc, napr. podºa vzÈahu (26).

Nech r je prirodzené ãíslo a pjk, qik (kde k = 1, 2, ..., r) sú reálne ãísla rôzne od nuly, pre ktoré platí r

∑ pjk = pj

(34)

k=1

a r

∑ qjk = qj

(35)

k=1

Definujeme stæpcové vektory vTj = (pj1, pj2, ..., pjr) a uTi = (qi1, qi2, ..., qir), v′Tj = (p′jk), u′Ti = (q′ik), kde p′jk = 1/pjk a q′ik = 1/qik. Maticu S môÏeme potom modifikovaÈ takto: SjM = uTi v′Tj

(36)

alebo SiM = vTj u′Ti

(37)

SLR bude maÈ tvar SjM vj = q

(38)

alebo SiM ui = p

(39)

ão moÏno interpretovaÈ ako v˘poãet vektora vj pri zadanej matici SjM a vektora q, resp. v˘poãet vektora ui na základe zadanej matice SiM a vektora p (j, i je poãet tak˘chto matíc, resp. moÏn˘ch SLR). Na základe vzÈahov (38) alebo (39) môÏeme problém formulovaÈ aj ako úlohu lineárneho programovania. V teórii sa rozpracovali metódy na meranie efektívnosti pri viacer˘ch vstupoch a v˘stupoch. V˘sledky tejto teórie aplikujeme na meranie efektívnosti vstupno-v˘stupn˘ch transformácií v ekonomike podniku. MoÏno sa napr. zaoberaÈ formuláciou a anal˘zou úlohy lineárneho programovania na podklade vzÈahu (38) ako lineárnej viackriteriálnej optimalizaãnej úlohy: max Cvj {vj | SJMvj ≤ q; vj ≥ 0}

(U.1)

b) hºadisko ekonomickej aplikácie B2. ëalej sa budeme zaoberaÈ nasledujúcim prípadom:

kde C = {crj} je matica s.n rozmerná. Agregáciou kritérií voºbou vektora t = {t1, t2, ..., ts} > 0,

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Ekonomika a management kde ∑tr = 1a r = 1, 2, ..., s, preformulujeme r úlohu (U.1) na jednokriteriálnu: max tCvj {SJMvj ≤ q; vj ≥ 0}

(U.2)

V praktick˘ch aplikáciách rie‰ime e‰te jednoduch‰iu úlohu, kedy minimalizujeme odch˘lky medzi v˘stupmi (v) a vstupmi (n), priãom vektor t dostaneme ako rie‰enie:

j

za podmienok

Ukazovatele z pohºadu súvahy aj v˘sledovky rozdelíme na vstupy (náklady) a v˘stupy (v˘nosy) ekonomiky podniku takto:

∑ui SJM, ij – ∑trcrj – wj = 0 r

∑tr = 1 ui, tr, wj ≥ 0

(U.3)

V˘kaz

Vstupy

V˘stupy

Súvaha

ZDROJE (Pasíva)

MAJETOK (Aktíva)

V˘sledovka

NÁKLADY

V¯NOSY

Z pohºadu súvahy ukazovatele predstavujú informácie o stave a ‰truktúre majetku podniku a o zdrojoch jeho krytia k urãitému momentu analyzovaného obdobia (stavové veliãiny). Z pohºadu v˘sledovky ukazovatele predstavujú informácie o v˘‰ke podnikov˘ch v˘nosov a nákladov (tokové veliãiny). V matici A sú ukazovatele typu v˘stup/vstup a vyjadrujú efektívnosÈ. Je potrebné, aby v‰etky rástli. V matici C sú ukazovatele typu vstup/v˘stup, ktoré je na zabezpeãenie rastu efektívnosti potrebné minimalizovaÈ. V matici B sú ukazovatele typu vstup/vstup, ktoré vyjadrujú vzÈahy medzi podnikov˘mi vstupmi. Za najpouÏívanej‰í je povaÏovan˘ ukazovateº vybavenosti pracovnej sily dlhodob˘m majetkom meran˘ pomerom stavu dlhodobého majetku a poãtu pracovníkov. V matici D sú ukazovatele typu v˘stup/v˘stup a prezentujú vzÈahy medzi podnikov˘mi v˘stupmi. Medzi najdôleÏitej‰ie ukazovatele patria rentabilita v˘roby a rentabilita v˘nosov (ziskovosÈ v˘nosov). Za pozitívny trend moÏno povaÏovaÈ rast t˘chto ukazovateºov. Budeme uvaÏovaÈ údaje podniku XY v ãasovom období rokov 2005 aÏ 2008 (tab. 6).

82

Vstupné údaje maticového modelu uvádzame v tab. 6. V˘poãty realizujeme kombináciou ukazovateºov vstupov (pracovníci, materiálové náklady, dlhodob˘ hmotn˘ majetok) a v˘stupov (v˘nosy spolu, zisk ãist˘, v˘roba) fiktívneho podniku XY.

3.1 Charakteristika ukazovateºov v maticiach A, B, C, D. Údajová základÀa maticového modelu

min ∑wj = z

i

3. Realizácia modelov˘ch v˘poãtov. Anal˘za v˘sledkov rie‰enia

3.2 Realizácia a anal˘za modelov˘ch v˘poãtov Prezentujeme v˘sledky v˘poãtov A4 a B2 (tab. 4). Na realizáciu v˘poãtov sme vyuÏili nástroje Excelu (Anal˘za dát, Rie‰iteº). Ekonometrické modelovanie (v˘poãet A4) VyuÏitím vzÈahu (5) formulujeme jednorovnicov˘ ekonometrick˘ model. Vstupné údaje sú v tabuºke 7. Model má tvar (regresná priamka v˘nosov): ^y = 449510,7795 – 374,86879994x – 1 (40) – 1,22101762 x2 + 3,92455372 x3 V lineárnej regresnej rovnici (40) má prv˘ parameter charakter kon‰tantného ãlena a kaÏd˘ ìal‰í parameter vyjadruje o koºko sa zv˘‰i závisle premenná, ak sa príslu‰ná vysvetºujúca premenná zv˘‰i o jednotku. ·tandardné chyby regresn˘ch koeficientov a kon‰tanty sa rovnajú 0. Koeficient determinácie má hodnotu 1, to znamená, Ïe medzi odhadovan˘mi a skutoãn˘mi hodnotami nie je Ïiaden rozdiel.

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Vstupné údaje maticového modelu

Obdobie

Ukazovatele vstupov a v˘stupov v tis. Sk VSTUPY Pracovníci (poãet)

Materiálové náklady

V¯STUPY

n1

n2

Dlhodob˘ hmotn˘ majetok n3

V˘nosy spolu

Zisk ãist˘

V˘roba

v1

v2

v3

2005

346

624155

316383

799364

-42814

699421

2006

306

607016

320860

852856

-16192

716029

2007

270

510476

209294

546384

-142061

540922

2008

240

465193

200360

577857

15776

565332

Zdroj: Vlastné spracovanie

Tab. 7:

Ekonometrické modelovanie Závisle premenná (yi)

Obdobie

Vysvetºujúce premenné (xij)

V˘nosy spolu

Pracovníci

Materiálové náklady

DHM

2005

799364

346

624155

316383

2006

852856

306

607016

320860

2007

546384

270

510474

209294

2008

577857

240

465193

200360 Zdroj: vlastn˘

Model lineárneho programovania (v˘poãet B2) Úlohu (U.3) sme pouÏili na meranie efektívnosti vstupno-v˘stupn˘ch transformácií v podniku XY v rokoch 2005 aÏ 2008. Rie‰ime dva prípady: 1. prípad Na meranie efektívnosti pouÏívame nároãnosti jednotliv˘ch vstupov a to: mzdovú, materiálovú a fondovú. Ako v˘stupy vystupujú v˘nosy spolu, zisk ãist˘ a majetok spolu na 1 mil. Sk v˘nosov. Tieto údaje sú uvedené v tabuºke vstupn˘ch údajov (tab. 8). Formulácia úlohy s konkrétnymi údajmi je uvedená v tab. 9 a v˘sledky rie‰enia sú zhrnuté v tab. 10, kde na základe vzÈahu Ej = ∑trcrj / ∑ui SJM, ij r

i

sme vypoãítali efektívnosÈ a poradie v jednotliv˘ch rokoch. Ide o efekt vstupno-v˘stupn˘ch transformácií, priãom uveden˘ podnik je v sledovanom

období rovnako efektívny. Z realizácie ºav˘ch strán modelu v‰ak moÏno usudzovaÈ, Ïe podnik je v roku 2007 menej efektívny. Ako kritériá v podstate vystupujú ãist˘ zisk a majetok spolu, priãom v˘poãet je realizovan˘ na báze v˘nosov spolu. V rie‰ení zo vstupov získali vy‰‰ie ocenenie (váhu) vlastné imanie (u3) a z v˘stupov majetok spolu (t3). MoÏno teda usudzovaÈ, Ïe podnik: napriek tomu, Ïe je efektívny, príãinou je vy‰‰í podiel majetku, men‰iu efektívnosÈ vstupno-v˘stupn˘ch transformácií v roku 2007 moÏno zdôvodniÈ vy‰‰ími nárokmi na vstupy (vlastné imanie). 2. prípad Na meranie efektívnosti pouÏívame nároãnosti jednotliv˘ch vstupov a to: pracovnú, materiálovú a fondovú (meranú DHM). Ako v˘stupy vystupujú v˘nosy spolu, zisk ãist˘ a v˘roba na 1 mil. Sk v˘nosov. Tieto údaje sú uvedené v tabuºke vstupn˘ch údajov (tab. 11).

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Vstupné údaje 1. prípadu

Obdobie

Vstupy

V˘stupy

u1 – mzdové náklady

u2 – materiálové náklady

u3 – vlastné imanie

t1 – v˘nosy spolu

t2 – zisk ãist˘

t3 – majetok spolu

mzdová nároãnosÈ

materiálová nároãnosÈ

fondová nároãnosÈ

na 1 mil. Sk v˘nosov



2005 2006 2007 2008

73099

624155

535566

799364

-42814

535566

0,09145

0,78081

0,66999

1

-0,05356

0,66999

74268

607016

533176

852856

-16192

533176

0,08708

0,71175

0,62517

1

-0,01899

0,62517

81661

510474

416235

546384

-142061

416235

0,14946

0,93428

0,76180

1

-0,26000

0,76180

88553

465193

444757

577857

15776

444757

0,15324

0,80503

0,76967

1

0,02730

0,76967

Zdroj: Vlastné v˘poãty

Tab. 9:

Formulácia úlohy 1. prípadu u1

u2

u3

t1

t2

t3

w1

w2

w3

w4

x1

x2

x3

x4

x5

x6

x7

x8

x9

x10

1

2

3

4

5

6

7

8

9

10

1

0,09145 0,78081 0,66999

-1

0,05356 -0,66999

2

0,08708 0,71175 0,62517

-1

0,01899 -0,62517

3

0,14946 0,93428 0,76180

-1

0,26000 -0,76180

4

0,15324 0,80503 0,76967

-1

-0,02730 -0,76967

u1

u2

u3

1

1

1

t1

t2

t3

-1

=0 -1

=0 -1

=0 -1

=0 =1

w1

w2

w3

min w1

+ w2

+ w3

+

w4

≥0

w4

=z

Zdroj: Vlastné spracovanie Vysvetlivky: stæpec 1 – mzdová nároãnosÈ 2 – materiálová nároãnosÈ 3 – fondová nároãnosÈ 7 aÏ 10 – roky 2005 aÏ 2008

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stæpec

4 – v˘nosy spolu 5 – zisk ãist˘ 6 – majetok spolu

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EfektívnosÈ vstupno-v˘stupn˘ch transformácií v 1. prípade Váhy ui, tr

Odch˘lky wj

Poradie efektívnosti

EfektívnosÈ

u1 = 0

w1 = 0

1

1

u2 = 0

w2 = 0

1

1

u3 = 1

w3 = 0

1

1

t1 = 0

w4 = 0

1

1

t2 = 0 t3 = 1 Zdroj: Vlastné v˘poãty

Tab. 11:

Vstupné údaje 2. prípadu

Obdobie

Vstupy

V˘stupy

u1 – pracovníci

u2 – materiálové náklady

u3 – dlhodob˘ hmotn˘ majetok

t1 – v˘nosy spolu

t2 – zisk ãist˘

t3 – v˘roba

pracovná nároãnosÈ

materiálová nároãnosÈ

fondová nároãnosÈ




346

624155

316383

799364

-42814

699421

0,00043

0,78081

0,39579

1

-0,05356

0,87497

306

607016

320860

852856

-16192

716029

0,00036

0,71175

0,37622

1

-0,01899

0,83957

2005 2006

270

510474

209294

546384

-142061

540922

0,00049

0,93428

0,38305

1

-0,26000

0,99000

240

465193

200360

577857

15776

565332

0,00042

0,80503

0,34673

1

0,02730

0,97833

2007 2008


Formulácia úlohy je analogická ako v 1. prípade a v˘sledky rie‰enia sú zhrnuté v tab. 12, kde na základe vzÈahu Ej = ∑trcrj / ∑ui SJM, ij sme vypoãítali efektívnosÈ a poradie v jednotliv˘ch rokoch. r

Tab. 12:

i

EfektívnosÈ vstupno-v˘stupn˘ch transformácií v 2. prípade Váhy ui, tr

u1 = u2 = u3 = t1 = t2 = t3 =

0 0,52579 1,66332 1 0 0

Odch˘lky wj w1 w2 w3 w4

= = = =

0,06888 0 0,12837 0

Poradie efektívnosti

EfektívnosÈ

2 1 3 1

0,93112 1 0,87163 1


85

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Ekonomika a management Ide o efekt vstupno-v˘stupn˘ch transformácií, priãom uveden˘ podnik je v sledovanom období efektívny v rokoch 2006 a 2008. Aj realizácia ºav˘ch strán modelu potvrdzuje, Ïe podnik je menej efektívny v rokoch 2005 a 2007. Ako kritériá v podstate vystupujú ãist˘ zisk a v˘roba, priãom v˘poãet je realizovan˘ na báze v˘nosov spolu. V rie‰ení zo vstupov získali vy‰‰ie ocenenie (váhu) materiálové náklady (u2) a dlhodob˘ hmotn˘ majetok (u3) a z v˘stupov v˘nosy spolu (t1). MoÏno teda usudzovaÈ, Ïe podnik: vy‰‰í podiel v˘nosov spôsobil efektívnosÈ v rokoch 2006 a 2008, vysoké nároky na vstupy (materiálové náklady, dlhodob˘ hmotn˘ majetok) spôsobili men‰iu efektívnosÈ v rokoch 2005 a 2007. V oboch prípadoch sa podnik pozerá na efekty transformácie vstupov na v˘stupy z pohºadu v˘nosov a potvrdzuje sa vysoká nároãnosÈ na majetkové vstupy. MoÏno kon‰tatovaÈ, Ïe tieto v˘poãty poskytujú syntetick˘ pohºad a identifikujú slabé aj silné miesta. ëal‰ia anal˘za môÏe byÈ potom cielenej‰ia a efektívnej‰ia.

Záver Maticové usporiadanie ukazovateºov vytvára priestor na rôzne v˘poãtové postupy. Pri formulácii v˘poãtov˘ch postupov vychádzame z toho, Ïe ekonomické problémy môÏu maÈ podobnú matematickú ‰truktúru [8], [9]. Napr. ak sa dostaneme na urãitú úroveÀ abstrakcie, tak maticová rovnica a integrálna rovnica opisujú podobné matematické situácie t.j. problémy majú podobnú matematickú ‰truktúru. Súbor transformácií vo vzÈahu (1) moÏno vytváraÈ tak, Ïe meníme vlastnosti operátora. Aj keì existuje mnoho druhov operátorov, je uÏitoãné zaoberaÈ sa tak˘mi, ktoré ak vynecháme nepodstatné detaily, môÏeme povaÏovaÈ za transformácie normovaného lineárneho priestoru do normovaného lineárneho priestoru. Toto nám umoÏÀuje skúmaÈ rovnak˘m spôsobom maticové rovnice, integrálne rovnice, diferenciálne rovnice, diferenãné rovnice a náhodné procesy. NajdôleÏitej‰ím faktom je to, Ïe v‰etky normované lineárne priestory majú geometrickú ‰truktúru veºmi podobnú obyãajnej dvojrozmernej alebo trojrozmernej Euklidovej geometrii. Dá sa dokázaÈ, Ïe geometrická ‰truktúra normovaného lineárneho priestoru

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v skutoãnosti obsahuje tri rôzne druhy ‰truktúr: mnoÏinovú, topologickú a algebrickú [11]. Modifikácia matice S môÏe byÈ spojená nielen s rie‰iteºnosÈou SLR, ale aj s in˘mi v˘poãtov˘mi postupmi. MoÏno sa napr. zameraÈ na skúmanie ‰truktúrnych vzÈahov (27) rozpracúvaním pouÏitia Markovov˘ch reÈazcov na skúmanie stability a predikciu ‰truktúrnych vzÈahov. Na základe vzÈahu (28) moÏno za urãit˘ch podmienok formulovaÈ úlohu cieºového programovania minimalizácie vzdialenosti ‰truktúr. V˘poãtové postupy na báze rie‰enia SLR moÏno roz‰íriÈ aj na súãasné porovnávanie v˘konnosti viacer˘ch podnikov, priãom vektory q, p vo vzÈahoch (38) a (39) budú predstavovaÈ úroveÀ odvetvia hospodárstva (skupiny podnikov). Príspevok bol spracovan˘ v rámci úlohy VEGA 1/165/08 „ Interakcie rozpoãtovo-kapitálov˘ch a finanãn˘ch rozhodnutí a ich vplyv na rast trhovej hodnoty podniku“ na Fakulte podnikového manaÏmentu Ekonomickej univerzity v Bratislave.

Literatúra [1] FIALA, P. Modelování a anal˘za produkãních systémÛ. 1. vyd. Praha: Professional Publishing, 2002. 259 s. ISBN 80-86419-19-3. [2] GRELL, M. Informaãná ekonomika. 1. vyd. Bratislava: Vydavateºstvo EKONÓM, 2002. 163 s. ISBN 80-225-1561-2. [3] HEBÁK, P., HUSTOPECK¯, J. Vícerozmûrné statistické metody s aplikacemi. 1. vyd. Praha: SNTL/ALFA, 1987. 452 s. [4] HLAVÁâEK, L. Hodnocení adaptability v˘robního organismu na alternativní cílové zámûry. Ekonomicko-matematick˘ obzor. 1984, roã. 20, ã. 2, ISSN 0013-3027. [5] KAPLAN, R., S., NORTON, D., P. Balanced Scorecard. Strategick˘ systém mûfiení v˘konnosti podniku. 3. vyd. Praha: Management Press, 2002. 267 s. ISBN 80-7261-063-5. [6] KLAS, A. a kol. Ekonometrické modelovanie. Bratislava: Vydavateºstvo ALFA, 1979. 335 s. [7] KRÁªOVIâ, J., VLACHYNSK¯, K. Finanãn˘ manaÏment. Bratislava: IURA EDITION 2006. 455 s. ISBN 80-8078-042-0. [8] LANGE, O. Úvod do ekonomické kybernetiky. 1. vyd. Praha: ACADEMIA, 1968. 173 s. [9] LANGE, O. Celek a v˘voj ve svûtle kybernetiky. 1. vyd. Praha: Nakladatelství Svoboda, 1966. 128 s. [10] LUKÁâIKOVÁ, A, LUKÁâIK, M. Ekonometrické modelovanie s aplikáciami. Bratislava: Vydava-

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Ekonomika a management teºstvo EKONÓM, 2008. 343 s. ISBN 978-80-2252614-2. [11] NAYLOR, A. W., SELL, G. R. Teória lineárnych operátorov v technick˘ch a prírodn˘ch vedách. 1. vyd. Bratislava: ALFA, 1981. 629 s. [12] ¤EPA, V. Podnikové procesy. Procesní fiízení a modelování. 2. aktualizované a roz‰ífiené vyd. Praha: Grada Publishing, 2007. 281 s. ISBN 97880-247-2252-8. [13] SVÄTOKRÍÎNY, P. Lineárna algebra v úlohách. 1. vyd. Bratislava: ALFA,1985. 466 s. [14] UâE≈, P. a kol. Metriky v informatice. Jak objektivnû zjistit pfiínosy informaãního systému. 1. vyd. Praha: Grada Publishing, 2001. 139 s. ISBN 80-247-0080-8. [15] UNâOVSK¯, L., IVANIâOVÁ, Z., BREZINA, I. Základy operaãného v˘skumu. Bratislava: Ediãné stredisko EU, 1993. 236 s. ISBN 80-225-0500-5. [16] ZALAI, K. a kol. Finanãno-ekonomická anal˘za podniku. 7. pfieprac. a roz‰ír. vyd. Bratislava: Sprintdva, 2010. 446 s. ISBN 978-80-89393-15-2.

Ing. Michal Grell, PhD. Ekonomická univerzita v Bratislave Fakulta hospodárskej informatiky Katedra aplikovanej informatiky [email protected] Ing. Eduard Hyránek, PhD. Ekonomická univerzita v Bratislave Fakulta podnikového manaÏmentu Katedra podnikov˘ch financií [email protected]


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Abstract MATRIX MODELS FOR PRODUCTION SYSTEMS EFFICIENCY’S MEASUREMENT Michal Grell, Eduard Hyránek In the contribution we deal with firm efficiency′s measurement by the matrix system of indicators and various combination of these indicators is applying in this system. In the calculations we apply the indicators in term of added value and formulate relations of these indicators. It is the system of financial and non-financial indicators. Nowadays these systems are based on the integration of financial analysis and the analysis of strategical factors of enterprise fruitfulness. An example of this system is Balanced Scorecard which represents an interconnection of enterprise financial efficiency′s goals with its strategical goals. The instrument of efficiency′s measurement are metrics i.e. strict financial or non-financial indicators or evaluative criteria which use efficiency′s levels in specific area of enterprise. Metrics are created normally in term of added value and of parametrization. We deal more closely with indicators in term of added value. The important thing is also clear formulation of indicator′s relations which has the same influence on quantitative evaluation of economic reality as the clear definition of indicators (metrics), system elements. We deal with the mathematical formulation of relations. The mathematical formulation of relations is represented by the matrix system which is created by vertical and horizontal combination of indicators. Indicators co-ordinating vertically represents matrix rows and indicators co-ordinating horizontally represents matrix columns. Its combination generates matrix elements which can represent various types of ratio indicators. We analyse specific computing procedures in matrix model. Enterprise matrix model formally decribes correlation between economic variables of the model. The starting point for the formulation of matrix model are absolute indicators organized in the table. In the table are differentiated by four essential matrix: imput efficiency matrix, output intensity matrix, imput structure matrix and output structure matrix. There are possible e.g., applications based on calculations using the pseudoinverse matrix, single-equation econometric model, multi-equation econometric model, modified matrix, etc. The presented matrix model belongs to operation research’s nonstandard problems, which need cross-disciplinary approach of experts from various professions. Key Words: firm efficiency, metrics, matrix system of indicators, matrix model, computing procedures in matrix model. JEL Classification: C02, D59, D89.

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DETERMINANTY KAPITÁLOVÉ STRUKTURY âESK¯CH PODNIKÒ Pavlína Prá‰ilová

Úvod V roce 1958 publikovali Miller a Modigliani zcela zásadní práci pro v˘voj teorie kapitálové struktury. Za urãit˘ch pfiedpokladÛ, mimo jiné neexistence daní, nákladÛ úpadku a asymetrick˘ch informací, argumentovali nezávislost hodnoty podniku na kapitálové struktufie. Toto tvrzení v roce 1963 roz‰ífiili o existenci daní, coÏ následnû zdÛvodnilo preferenci cizího kapitálu kvÛli vyuÏití efektu daÀového ‰títu. Na tato tvrzení navazovaly dal‰í v˘zkumy kapitálové struktury, mezi které patfií napfiíklad práce Myerse, Donaldsona, Jensena, Masulise, Mecklinga, Titmana, Wesselse, Franka, Goyala, Rajana a Zingalese. Tyto prÛzkumy vyústily ve vznik nûkolika teorií kapitálové struktury, pfiiãemÏ tradiãní, kompromisní teorie, hledající optimální kapitálovou strukturu, vychází právû z tvrzení Millera a Modiglianiho. Na kompromisní teorii pak navázali dal‰í teorie, napfiíklad teorie hierarchického pofiádku, teorie volného cash flow, nebo teorie signalizování. Platnost tûchto teorií byla ovûfiována zejména americk˘mi ekonomy, v posledních letech pak i v ostatních ekonomikách v rámci celého svûta. Zpravidla se v‰ak dosud jednalo o zkoumání vefiejnû obchodovan˘ch spoleãností, a to kvÛli dostupnosti relevantních dat. Tento pfiíspûvek zkoumá kapitálovou strukturu a její determinanty ãesk˘ch podnikÛ, pfiiãemÏ se nezamûfiuje pouze na vefiejnû obchodované spoleãnosti. Problematika je zde fie‰ena v rámci vybran˘ch odvûtví ãeské ekonomiky. Cílem je prozkoumat kapitálovou strukturu ãesk˘ch spoleãností a zjistit, zda urãité determinanty, konkrétnû podíl fixních aktiv, zadrÏené zisky, úroková míra, rentabilita aktiv, velikost podniku, podíl hmotn˘ch aktiv a stáfií podniku, mají na kapitálovou strukturu vliv, a míru tohoto vlivu. Dále je cílem prozkoumat platnost kompromisní teorie a teorie hierarchického pofiádku

u ãesk˘ch spoleãností. V práci budou ovûfiovány tyto hypotézy: H1: Uvedené determinanty mají vliv na kapitálovou strukturu ãesk˘ch podnikÛ, resp. na míru dlouhodobého dluhu. H2: Uvedené determinanty mají vliv na kapitálovou strukturu ãesk˘ch podnikÛ, resp. na míru celkového dluhu. H3: Míra vlivu determinant na úroveÀ celkového zadluÏení se li‰í pro jednotlivá odvûtví. Hypotézy budou ovûfiovány na vzorku dat 299 ãesk˘ch spoleãností ‰esti odvûtví z období let 2006 a 2007 pomocí regrese. Struktura pfiíspûvku je následující: nejprve budou popsány determinanty kapitálové struktury a v˘sledky dosavadních v˘zkumÛ, zab˘vajících se touto problematikou. Následnû budou definovány dvû základní teorie kapitálové struktury a v˘sledky ovûfiování jejich platnosti. Poslední ãást pfiíspûvku se vûnuje ovûfiování vysloven˘ch hypotéz.

1. Determinanty kapitálové struktury podnikÛ Kapitálová struktura podniku mÛÏe b˘t ovlivnûna mnoha rÛzn˘mi faktory, subjekty, které lze rozdûlit na tzv. vnitfiní, tedy ty, mající souvislost s typem a hospodafiením spoleãnosti, a na tzv. vnûj‰í, které spí‰e vypl˘vají z charakteru hospodáfiské politiky a stupnû rozvoje ekonomiky zemû, v níÏ podnik pÛsobí, a které vût‰inou nemÛÏe zcela ovlivnit. Faktory vnitfiní vypl˘vají zejména ze strategie a zamûfiení spoleãnosti, jejího pfiístupu k riziku a stupni zájmu o udrÏení kontroly nad podnikem. Konkrétnû to je zejména struktura aktiv, rentabilita aktiv, stabilita zisku, stabilita cash flow, dividendová politika, jedineãnost produktu, rÛstové pfiíleÏitosti spoleãnosti, odvûtvová pfiíslu‰nost podniku a stáfií podniku. Faktory vnûj‰í jsou ãasto podnikem neovlivnitelné

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Ekonomika a management a odvíjí se zejména od hospodáfiské politiky zemû, obecnû od jednání subjektÛ vnû podniku. Aktivity vnûj‰ích subjektÛ v první fiadû ovlivÀují samotn˘ pfiístup podnikÛ k externím zdrojÛm financování a tím v podstatû „deformují“ finanãní rozhodování spoleãností ve smyslu preference konkrétních finanãních zdrojÛ, vypl˘vající z pÛsobení vnitfiních faktorÛ. Vnûj‰í faktory jsou ovlivnûny monetární politikou, úrovní kapitálového trhu, pfiístupem vlády k podpofie podnikání a dal‰ími vládními zásahy, odvíjejícími se od stupnû rozvoje zemû a politického systému. Patfií sem úroveÀ daÀov˘ch a úrokov˘ch sazeb, úroveÀ informaãní asymetrie, nákladÛ finanãní tísnû, vliv konkurence a poÏadavky vûfiitelÛ a majitelÛ. Lze tvrdit, Ïe na pomezí tûchto dvou skupin faktorÛ urãujících kapitálovou strukturu podniku stojí náklady kapitálu, které jsou ovlivnûny vnitfiními i vnûj‰ími determinanty, respektive z nich vypl˘vají.

1.1 Vnitfiní a vnûj‰í determinanty kapitálové struktury Jednou z nejãastûji zkouman˘ch determinant kapitálové struktury je rentabilita aktiv podniku, pfiiãemÏ vût‰ina dosavadních prací prokázala negativní vztah mezi rentabilitou aktiv a zadluÏeností podniku, napfiíklad práce tûchto ekonomÛ: Myers, Kester, Friend a Lang, Titman a Wessels, Rajan a Zingales, Fama a French, Bevan a Danbolt, Cassar a Holmes, Mahakud a Bhole, Vasiliou a kolektiv, Frank a Goyal. Voulgaris, Asteriou a Agiomirgianakis (2002) v‰ak zkoumali fiecké spoleãnosti v období let 1986–1998 a neobjevili mezi rentabilitou aktiv a kapitálovou strukturou Ïádn˘ vztah [65], stejnû jako Nguyen a Ramachandran (2006) nena‰li jasn˘ dopad rentability aktiv na míru dluhu na zkoumaném vzorku vietnamsk˘ch spoleãností [53]. Miglo (2010) se pak ve své práci pfiiklání k pozitivní korelaci mezi rentabilitou aktiv a mírou dluhu, a to kvÛli niÏ‰ím oãekávan˘m nákladÛm úpadku [47]. Stejnû tak Tao a Jianhui (2008) na základû prÛzkumu vefiejnû obchodovan˘ch spoleãností uvádí, Ïe podniky s dobrou rentabilitou aktiv, tedy s pozitivním pÛsobením finanãní páky, zintenzivÀují její pÛsobení navy‰ováním dal‰ího dluhu [61], Neumaierovi (1996) ve své práci tvrdí, Ïe velikost zisku není závislá na zadluÏenosti, ale na tom, jak˘m zpÛsobem je podnik schopen zhodnotit majetek, resp. na lukrativnosti podnikatelského zámûru [52].

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Dal‰í v˘znamnou determinantou kapitálové struktury je struktura aktiv, a to z toho dÛvodu, Ïe zejména hmotn˘ majetek mÛÏe slouÏit jako zástava úvûru. Jako jeden z nejdÛleÏitûj‰ích faktorÛ ovlivÀujících kapitálovou strukturu uvádí tento napfiíklad Harris a Raviv nebo Cassar a Holmes. Pozitivní korelaci mezi podílem fixních aktiv a mírou dluhu prokázali Myers, Jensen a Meckling, Titman a Wessels, Rajan a Zingales, Ghosh a kolektiv. Bevan a Danbolt (2002) na‰li pozitivní vztah mezi dlouhodob˘m zadluÏením a hmotn˘mi aktivy, ale negativní vztah pro krátkodob˘ dluh a hmotná aktiva [4]. Farhat, Cotei a Abugri (2009) konkrétnû uvádí, Ïe pozitivní vztah mezi podílem hmotn˘ch aktiv a mírou zadluÏení byl prokázán v 74 % [22] zemí. Ov‰em napfiíklad Ferri a Jones (1979) [23] naopak potvrdili negativní korelaci mezi celkov˘m dluhem a mírou fixních aktiv, Voulgaris, Asteriou a Agiomirgianakis (2002) pak ve své práci uvedli, Ïe struktura aktiv nemûla na kapitálovou strukturu fieck˘ch spoleãností v letech 1986–1998 Ïádn˘ vliv [65], Nguyen, Ramachandran (2006) rovnûÏ nena‰li vztah mezi strukturou aktiv a kapitálovou strukturou u vietnamsk˘ch podnikÛ [54]. Struktura aktiv má úzkou spojitost s dal‰í determinantou, s odvûtvovou pfiíslu‰ností, pfiiãemÏ v pfiípadû této jde zejména o kapitálovou intenzitu daného odvûtví. Kapitálovû intenzivnûj‰í odvûtví pak mají, jak uvádí napfiíklad Bradley, Jarrell, Kim (1984), vy‰‰í míru zadluÏení [7]. Vztah mezi odvûtvovou pfiíslu‰ností a kapitálovou strukturou potvrdil znaãn˘ poãet prÛzkumÛ, napfiíklad práce Schwartze a Aronsona, Scotta a Martina, Bowena, Daleyho a Hubera, Harrise a Raviva, Ghoshe a Caie, Eldomiatyho a Ismaila. Napfiíklad Harris a Raviv (1991), a i dal‰í, uvádí, Ïe podniky patfiící do stejného odvûtví mají podobné míry zadluÏení [33]. Bowen, Daley a Huber (1982) prokázali, Ïe v ãasovém horizontu pûti let spoleãnosti konvergují k odvûtvovému prÛmûru [6]. Cai a Ghosh (2003) ve své práci uvádí, Ïe moÏnost konvergence podnikové zadluÏenosti k odvûtvovému prÛmûru se v˘znamnû neli‰í od moÏnosti divergence od prÛmûru, pfiiãemÏ tato pravdûpodobnost zmûn je vysoká pro spoleãnosti pohybující se nad odvûtvov˘m prÛmûrem zadluÏení [10]. RovnûÏ Claggett ml. (1992) zjistil, Ïe dlouhodobá zadluÏenost tendovala k odvûtvovému prÛmûru v období jednoho roku, pfiiãemÏ typiãtûj‰í bylo pfiibliÏování se k prÛmûru

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Ekonomika a management pro spoleãnosti pohybující se nad odvûtvov˘m prÛmûrem, neÏ pro spoleãnosti podprÛmûrnû zadluÏené [14]. Remmers, Toy, Stonehill, Wright a Beekhuisen (1974) v‰ak prokázali, Ïe míry zadluÏení se v˘znamnû neli‰í pro podniky v rámci rÛzn˘ch odvûtví v Kanadû, Nizozemí, Norsku a USA, zatímco ve Francii a Japonsku se li‰í [55]. Gibson (2002) [29], Balakrishnan a Fox (1993) [2] tvrdili, Ïe odvûtvová pfiíslu‰nost nemá tak zásadní vliv na kapitálovou strukturu jako podniková specifika. Lze tvrdit, Ïe stejnû jako se strukturou aktiv, je zde spojitost i s velikostí podniku. Podle Jordana a kol. (1998) nemá odvûtvová pfiíslu‰nost v˘znamnûj‰í vliv na kapitálovou strukturu mal˘ch a stfiedních podnikÛ, a to z toho dÛvodu, Ïe tyto spoleãnosti vût‰inou podnikají v aktivitách vyplÀujících trÏní mezery, proto podle autora pro tyto podniky neplatí odvûtvové prÛmûry [39]. Kapitálovou strukturu mohou ovlivÀovat i rÛstové pfiíleÏitosti podniku. Pozitivní vztah mezi rÛstov˘mi pfiíleÏitostmi a dlouhodob˘m zadluÏením potvrdili Bhaduri, Bevan a Danbolt, Tao a Jianhui. Naopak Titman a Wessels, Rajan a Zingales, Farhat, Cotei a Abugri objevili negativní vztah mezi rÛstov˘mi pfiíleÏitostmi a úrovní celkového a dlouhodobého dluhu. Dal‰í determinantou kapitálové struktury, resp. míry zadluÏení, je velikost podniku. Vût‰ina prací v tomto pfiípadû potvrzuje pozitivní vztah. Toto ve sv˘ch pracech uvádí napfiíklad Ferri a Jones, Rajan a Zingales, Bevan a Danbolt, Mahakud a Bhole nebo Momani, Alsharayri a Dandan. Rajan a Zingales (1995) argumentovali tendencí k diverzifikaci u vût‰ích podnikÛ [54], Ferri a Jones (1979) jednodu‰‰ím pfiístupem vût‰ích spoleãností k cizímu kapitálu a niÏ‰ím nákladÛm kapitálu [23]. Bevan a Danbolt (2002) [4], stejnû jako Titman a Wessels (1988) [62] tvrdili, Ïe velké podniky vyuÏívají dlouhodob˘ dluh, zatímco malé spoleãnosti jsou více závislé na krátkodobém dluhu. Farhat, Cotei, Abugri (2009) uvádí, Ïe pozitivní vztah mezi zadluÏením a velikostí podniku byl prokázán v 55 % [22] zemí. Heyman, Deloof, Ooghe (2007) v‰ak potvrdili ve své práci negativní závislost mezi zadluÏeností a velikostí podniku [35], zatímco Cassar a Holmes (2003) nena‰li dÛkaz o vlivu velikosti podniku na kapitálovou strukturu [11]. DaÀová a úroková sazba hraje dÛleÏitou roli zejména kvÛli efektu daÀového ‰títu, kter˘ je zpÛsoben daÀovou odeãitatelností úrokov˘ch

nákladÛ. Vztah mezi daÀovou sazbou a zadluÏením je uvádûn jako pozitivní. Miglo (2010) tvrdí, Ïe s rÛstem daÀové sazby se zvy‰uje zadluÏení podnikÛ kvÛli rostoucímu daÀovému zv˘hodnûní [47]. Pozitivní závislost uvádí i DeAngelo a Masulis. Graham a Harvey (2001) ve své práci poukazují na to, Ïe 45 % zkouman˘ch podnikÛ potvrdilo velk˘ v˘znam daní v rámci rozhodování o kapitálové struktufie, a Ïe znaãn˘ v˘znam mají danû zejména pro velké spoleãnosti [31]. Lze tvrdit, Ïe v˘znam daní, respektive daÀového ‰títu, v ãase klesal v zemích, které zaznamenaly sníÏení úrokov˘ch a daÀov˘ch sazeb. V rámci dal‰ích determinant, majících vliv na kapitálovou strukturu, lze zmínit likviditu, kde byl prokázán její negativní vliv, dále pozitivní vztah mezi zadluÏením a náklady vlastního kapitálu, nebo inverzní vztah mezi zadluÏením a náklady dluhu. Podniky se stabilním cash flow mají tendenci k vy‰‰ímu zadluÏení, spoleãnosti s vy‰‰ím rizikem naopak k niÏ‰ímu zadluÏení. Inverzní vztah byl prokázán mezi volatilitou zisku a zadluÏením. Farhat, Cotei a Abugri (2009) tvrdí, Ïe informaãní asymetrie vykazuje zápornou korelaci s mírou zadluÏení [22]. Graham a Harvey (2001) objevili inverzní vztah mezi jedineãností produkce a dluhem u high-tech firem, které zadluÏením nechtûli dát negativní signál jejich zákazníkÛm a dodavatelÛm [31]. Jak jiÏ bylo v˘‰e zmínûno, existují urãité determinanty vypl˘vající z rÛzného stupnû rozvoje ekonomik a jin˘ch rozdílÛ v zemích. Bancel a Mittoo (2004) zjistili, Ïe kapitálová struktura je ovlivnûna specifiky jednotliv˘ch zemí, jde zejména o existenci rozvinutého dluhopisového trhu a akciového trhu [3]. Farhat, Cotei a Abugri (2009) se zab˘vali rozdíly mezi zemûmi s kontinentálním právním systémem a zemûmi s anglosask˘m právním systémem. V˘sledkem bylo zji‰tûní, Ïe v první skupinû zemí vyuÏívají podniky k financování ménû soukromého kapitálu, protoÏe jsou zde finanãní trhy ménû rozvinuté a je zde vy‰‰í míra informaãní asymetrie [22]. Na v˘znam pfiístupu podnikÛ na finanãní trhy upozorÀuje rovnûÏ Stenbacka a Tombak (2002). V˘sledky prÛzkumÛ se ãasto li‰í u prÛmyslovû vyspûl˘ch zemí a u transformujících se ekonomik. U prÛmyslovû vyspûl˘ch zemí existuje obvykle pozitivní vztah mezi v˘‰í hmotného majetku, dynamikou rÛstu podniku a zadluÏením, zatímco vztah mezi velikostí trÏeb – tedy

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Ekonomika a management velikostí podniku – a zadluÏením není jednoznaãn˘; rentabilita aktiv podniku je pfieváÏnû negativnû korelována s mírou zadluÏení. U transformujících se ekonomik je vût‰inou v˘‰e hmotného majetku negativnû korelována s mírou dluhu; dynamika rÛstu má stejn˘ vztah k zadluÏenosti jako u prÛmyslovû vyspûl˘ch zemí. Vztah mezi velikostí podniku a mírou zadluÏení se jeví jako nev˘razn˘ a rentabilita aktiv podniku má negativní dÛsledky na míru zadluÏení. K zajímav˘m v˘sledkÛm do‰li F. a M. Deari (2009), ktefií zkoumali zvlá‰È makedonské vefiejnû obchodované spoleãnosti a neobchodované podniky. U kótovan˘ch spoleãností bylo prokázáno, Ïe hmotná aktiva, rÛst a velikost podniku nemají Ïádn˘ vliv na kapitálovou strukturu [17], velikost podniku nemûla vliv ani na neobchodované podniky. Brav (2009) uvádí, Ïe nekótované podniky pak vyuÏívají ve velké mífie zadrÏené zisky a bankovní úvûry, mají vy‰‰í míry zadluÏení [8]. Malé vefiejnû obchodované podniky preferují podle Franka a Goyala (2007) financování soukrom˘m kapitálem, velké vefiejnû obchodované podniky primárnû vyuÏívají zadrÏené zisky a podnikové dluhopisy [25]. Valach (2008) shrnuje, Ïe nejv˘raznûji na zadluÏení pÛsobí velikost podniku, rentabilita, v˘‰e hmotného majetku a dynamika rÛstu podniku. Ménû v˘znamnûj‰ími faktory se jeví podnikové danû a odvûtvová pfiíslu‰nost podniku [63]. Na základû rÛzn˘ch prÛzkumÛ jednotliv˘ch faktorÛ lze tvrdit, Ïe v˘sledky prací jsou nejednoznaãné, názory rÛzn˘ch autorÛ na vliv dan˘ch determinant na kapitálovou strukturu se znaãnû li‰í, mimo jiné z dÛvodu odli‰n˘ch pouÏit˘ch metod.

1.2 Teorie kapitálové struktury Na tvrzení Millera a Modiglianiho, ktefií poukázali na v˘hody vyuÏití dluhu, navázaly práce Millera, DeAngela a Masulise, Myerse a dal‰í pfiíspûvky a prÛzkumy, které umoÏnily vznik teorií zab˘vajících se kapitálovou strukturou. Tyto lze ãlenit do dvou základních skupin, na statické, kam patfií tzv. kompromisní teorie, a dynamické, kam patfií tzv. teorie hierarchického pofiádku. Zásadním rozdílem mezi nimi je tvrzení, zda existuje urãitá optimální kapitálová struktura, respektive optimální míra zadluÏení podniku. Statické teorie se pfiiklání k existenci optimální kapitálové struktury, zatímco dynamické teorie popírají, Ïe by se spoleãnosti snaÏily

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pfiiblíÏit k jakémusi cílovému zadluÏení. Podle dynamick˘ch teorií je totiÏ kaÏdá spoleãnost unikátní a funguje ve specifick˘ch a mûnících se podmínkách. Valach (2009) k otázce teorií kapitálové struktury uvádí, Ïe v prÛbûhu posledních let se zkoumání kapitálové struktury podnikÛ posunulo od hledání optimální kapitálové struktury pomocí nákladÛ kapitálu [resp. minimalizace nákladÛ, jak uvádí klasická teorie „U“ kfiivky (optimální kapitálová struktura je v minimu prÛmûrn˘ch nákladÛ kapitálu), jejíÏ vznik je spojen s prací Duranda z roku 1952] ke zkoumání hlavních faktorÛ, determinujících kapitálovou strukturu [63]. Tento pfiíspûvek se zamûfiuje na kompromisní teorii, která vychází z „balancování“ mezi v˘hodami a nev˘hodami dluhu, a na teorii hierarchického pofiádku, která je zaloÏená na existenci asymetrick˘ch informací. Existují v‰ak i dal‰í pfiístupy, jako je teorie signalizování, která také vychází z existence asymetrick˘ch informací, teorie ekonomick˘ch subjektÛ a teorie volného cash flow, které jsou zaloÏené na konfliktech zájmÛ mezi vlastníky a manaÏery. Obû skupiny teorií byly zkoumány a testovány v mnoha zemích. Zpravidla v‰ak tyto prÛzkumy probíhaly v prÛmyslovû vyspûl˘ch zemích a na vefiejnû obchodovan˘ch spoleãnostech. Vût‰ina z nich pochází z USA, ale v posledních letech se dané teorie a determinanty kapitálové struktury zaãaly testovat i v dal‰ích ekonomikách. Vût‰inou v‰ak jde o ovûfiování platnosti rÛzn˘ch souvisejících hypotéz na jiÏ zmínûn˘ch vefiejnû obchodovan˘ch spoleãnostech kvÛli dostupnosti dat, coÏ nemusí poskytovat obecnû relevantní v˘sledky. Kompromisní teorie má svÛj poãátek v práci Millera a Modiglianiho z roku 1963, ve které byla zmínûna v˘hoda daÀové odãitatelnosti úrokÛ z dluhu. Nelze v‰ak opomenout, Ïe s rÛstem dluhu se zvy‰ují i náklady finanãní tísnû. Toto bylo dále rozvinuto v prÛzkumech Millera, DeAngela a Masulise, ve kter˘ch se otázka optimální kapitálové struktury, k jejíÏ existenci se tato teorie pfiiklání, fie‰í. Dále byla tato teorie zkoumána v pracech napfiíklad Brennana a Schwarze, DeAngela a Masulise, Bradleyho, Jarrella a Kima. Podstata teorie spoãívá v hledání kompromisu mezi v˘hodami dluhu a náklady finanãní tísnû. V˘hody dluhu pfiedstavuje zejména daÀov˘ ‰tít, sníÏení problémÛ s voln˘m cash flow a potenciální konflikty mezi manaÏery a akcionáfii [22]. Náklady finanãní tísnû, jak uvádí F. a M.

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Ekonomika a management Deari (2009), závisí na vztahu podnikového fiízení k riziku, na nákladech úpadku a agenturních nákladech [17]. Spoleãnost by tak mûla vyuÏívat dluhu do té doby, dokud se „mezní uÏitek“ z nûj nevyrovná „meznímu nákladu“ dluhu. Pokud jde o náklady dluhu, Miller (1977) tvrdil, Ïe náklady úpadku a agenturní náklady jsou tak malé, Ïe je daÀová v˘hoda dluhu pfievy‰uje, a proto je hodnota podniku nezávislá na kapitálové struktufie i za podmínky nedokonal˘ch trhÛ [48]. Miller (1977), Graham, Harvey (2000) uvádí, Ïe pfiímé náklady úpadku jsou velice nízké a Ïe obecnû je úroveÀ zadluÏení pod optimální úrovní [48].Neumaierovi (1996) uvaÏují na stranû v˘hod daÀov˘ ‰tít a uvádí, Ïe optimální zadluÏenost, za pozitivního pÛsobení finanãní páky, lze vyjádfiit rovností pfiírÛstku souãasné hodnoty daÀového ‰títu a pfiírÛstku souãasné hodnoty nákladÛ finanãní tísnû [53]. Tedy rovností mezních hodnot. Problém spoãívá v definování, respektive ve vyãíslení, nákladÛ finanãní tísnû, jejich hodnota je v‰ak relativnû vysoká. Pfiímo související náklady jsou administrativní v˘daje, ztráta dÛvûry zákazníkÛ a dal‰í. Nepfiímo souvisejícími napfiíklad konflikty mezi rÛzn˘mi investory, negativní vnímání finanãními trhy, oslabení své pozice vÛãi konkurenci, atd. Andrade a Kaplan (1998) uvádí, Ïe náklady finanãní tísnû pfiedstavují 10–23 % [1] hodnoty podniku pro vysoce zadluÏené firmy. Ziskové spoleãnosti mohou lépe vyuÏít daÀového ‰títu prostfiednictvím dluhového financování, protoÏe je zde niÏ‰í pravdûpodobnost bankrotu. Proto mají podle této teorie vy‰‰í míru zadluÏení. Lze tvrdit, Ïe v˘znam kompromisní teorie mohl v ãase klesat v zemích, kde klesaly daÀové a úrokové sazby, ãímÏ se zmen‰il rozsah daÀového zv˘hodnûní dluhu. Miglo (2010) potvrzuje, Ïe spoleãnosti ãelící vy‰‰ím daÀov˘m sazbám by mûly mít vy‰‰í míru zadluÏení a naopak [48]. Slabinou této teorie, podle Neumaierov˘ch (1996), je neschopnost vysvûtlit, proã nejziskovûj‰í podniky v odvûtví mívají nejvût‰í podíl vlastního kapitálu, protoÏe podle této teorie znamenají vysoké zisky vût‰í moÏnost vyuÏít daÀového ‰títu díky niÏ‰í úrokové sazbû [52]. Stejnû tak Miglo (2010) uvádí, Ïe kompromisní teorie je schopná vysvûtlit nûkterá fakta t˘kající se kapitálové struktury, ale má jednu zásadní slabinu, a to je neschopnost vysvûtlit negativní korelaci mezi dluhem a rentabilitou aktiv [47]. Teorie, která toto vysvûtlení poskytuje, je teorie hierarchického pofiádku.

Teorie hierarchického pofiádku vznikla na základû v˘zkumu Donaldsona z roku 1961. Dále byla v roce 1984 rozvinuta Myersem a Majlufem, ktefií uvedli (1994), Ïe pfiedchozí testy potvrzující platnost kompromisní teorie nemûly vypovídací schopnost [58]. Zab˘vali se jí ve sv˘ch pracech napfiíklad i Titman a Wessels (1988) nebo Shyam-Sunder a Myers (1999) nebo Cosh a Hughes (1994). Její podstatou je urãitá preferenãní hierarchie finanãních zdrojÛ podniku a popírání existence optimální míry zadluÏení. Shyam-Sunder a Myers (1994) k otázce optimality uvedli, Ïe pomûr dluhu na financování kumulativním v˘sledkem hierarchického financování v ãase a neexistuje Ïádná cílová optimální hodnota [58]. Podle teorie hierarchického pofiádku podniky jako první vyuÏijí vnitfiní vlastní zdroje, tedy zejména zadrÏené zisky, poté externí cizí kapitál, a aÏ jako poslední moÏnost externí vlastní kapitál. Tím pádem spoleãnosti s vy‰‰í rentabilitou aktiv budou ménû zadluÏené. Napfiíklad Kamath (1997) zkoumal spoleãnosti kótované na burze a v˘sledkem bylo zji‰tûní, Ïe témûfi 85 % spoleãností prvotnû preferovalo v dlouhodobém financování interní zdroje, 75 % na druhé místo fiadilo rÛzné formy pfiímého dluhu a více neÏ 80 % uvedlo na posledním místû rÛzné formy kmenov˘ch a prioritních akcií [63]. Preference cizího kapitálu pfied vlastním je podle Myerse a Majlufa zpÛsobena asymetrick˘mi informacemi, tedy tím, Ïe manaÏefii podniku mají lep‰í informace o hospodafiení spoleãnosti neÏ investofii. Z tohoto dÛvodu investofii vûfií, Ïe kdyÏ spoleãnost vydává akcie, je nadhodnocená, ãehoÏ chtûjí manaÏefii touto cestou vyuÏít. Platí, Ïe ãím asymetriãtûj‰í jsou informace mezi subjekty, tím ménû spoleãnost bude vyuÏívat vlastní kapitál, jakoÏto na informace senzitivní nástroj [8].

1.3 Platnost teorií v rámci dosavadních prÛzkumÛ Pfii porovnání dosavadních prÛzkumÛ lze tvrdit, Ïe jejich v˘sledky se znaãnû li‰í, nûkteré uznávají kompromisní teorii, jiné teorii hierarchického pofiádku. V poslední dobû rÛzné práce také uvádí, Ïe tyto teorie nejsou vzájemnû vyluãitelné, a Ïe je vhodné v rámci finanãního rozhodování uplatnit pfiedpoklady obou teorií. Ve prospûch kompromisní teorie, tedy toho, Ïe spoleãnosti usilují o urãitou optimální míru

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Ekonomika a management zadluÏení, jsou práce Jalilvanda a Harrise, Shyam-Sundera a Myerse, Byouna a dal‰ích. Mahakud a Bhole (2003) prokázali, Ïe se spoleãnosti z jejich vzorku relativnû rychle pfiibliÏovaly sv˘m cílov˘m hodnotám zadluÏení [5]. Naopak F. a M. Deari (2009) tvrdí, Ïe dosud neexistuje pfiesn˘ zpÛsob determinace optimální kapitálové struktury pro jednotlivé spoleãnosti, coÏ potvrdili i Dhankar a Boora (1996) na vzorku indick˘ch spoleãností. Shyam-Sunder a Myers (1994) uvádí, Ïe zmûny v úrovni zadluÏení nejsou zpÛsobeny snahou o optimální kapitálovou strukturu, ale potfiebou externích zdrojÛ [58]. Dotazníkové ‰etfiení, které provedli Horová a Hrd˘ (2007) prokázalo, Ïe dlouhodobû se o optimalizaci kapitálové struktury snaÏí 20 % ãesk˘ch podnikÛ [36]. Podporu teorii hierarchického pofiádku ve sv˘ch pracech prostfiednictvím prokázání negativního vztahu mezi mírou zadluÏení a rentabilitou vyjádfiili Titman a Wessels, Rajan a Zingales, Shyam-Sunder a Myers, Cai a Ghosh. Frank a Goyal (2003) na‰li nejvût‰í platnost teorie u velk˘ch spoleãností, které vykazují niÏ‰í úroveÀ informaãní asymetrie [24], Bancel a Mittoo (2004) prokázali teorii naopak pouze slabou podporu [3]. Platnost teorie neprokázali Helwege a Liang, Fama a French. Leary a Roberts (2008) uvádí, Ïe „pouze“ cca 36 % spoleãností z jejich zkoumaného vzorku odpovídá pfiedpokladÛm teorie hierarchického pofiádku [63]. Goyal a Frank (2003) prokázali, Ïe teorie selhává zejména u mal˘ch spoleãností, naopak Cosh a Hughes (1994) tvrdí, Ïe teorie je na malé a stfiední podniky aplikovatelná, a to ãásteãnû i kdyÏ nejsou vefiejnû obchodované [15]. Chirinko a Singha (2000) uvádí, Ïe k potvrzení platnosti teorie hierarchického pofiádku je tfieba adekvátnûj‰ích postupÛ, neÏ byly do té doby provedeny a kritizovali závûry Myerse a Shyam-Sunderse [13]. DÛkaz platnosti obou teorií objevil napfiíklad Gaud a kolektiv (2005), Haan a Hinloopen (2003), tito uvedli, Ïe obû teorie mají empirick˘ v˘znam v rámci finanãního rozhodování [47]. Potvrzení koexistence kompromisní teorie i teorie hierarchického pofiádku je i v práci Majumdara (2010). RovnûÏ Farhat, Cotei, Abugri nebo Ghosh a Cai uvádí, Ïe obû teorie nejsou vzájemnû vyluãitelné. Podle Farhata, Coteie a Abugriho (2009) mohou podniky usilovat o urãitou cílovou míru zadluÏení a v rámci toho mohou jejich dílãí finanãní rozhodování odpovídat

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pfiedpokladÛm teorie hierarchického pofiádku, pfiípadnû se mohou v ãase mezi tûmito teoriemi pohybovat [22]. Huang a Ritter (2009) pak obdobnû tvrdí, Ïe Ïádná z jednotliv˘ch teorií není schopna vysvûtlit v‰echny skuteãnosti t˘kající se kapitálové struktury v ãase a mezi odvûtvími [37]. Naopak Kamath (1997) prokázal na spoleãnostech kótovan˘ch na burze, Ïe 65 % z nich uplatÀovalo v dlouhodobém financování princip finanãní hierarchie a 45 % usilovalo o hledání cílové optimální kapitálové struktury [40]. Miglo (2010) je také pro to, aby se teorie nestavûly oddûlenû, protoÏe pak nejsou schopny vysvûtlit v‰echna fakta ohlednû kapitálové struktury [47]. Fama a French (2005) projevili v dosavadní práce, ovûfiující obû teorie, vÛbec nedÛvûru a uvedli, Ïe je dobré brát v úvahu obû dvû s tím, Ïe kaÏdá ãásteãnû vysvûtluje nûjaké aspekty finanãního rozhodování [21]. Stejnû tak Eldomiaty a Ismail (2008) tvrdí, Ïe Ïádná z tûchto teorií nemÛÏe poskytnout komplexní obraz o realitû faktorÛ urãujících kapitálovou strukturu [19]. Dále uvádí, Ïe pfiístup spoleãnosti ke konkrétní teorii mÛÏe b˘t ovlivnûn zmûnou podmínek, ve kter˘ch se spoleãnost pohybuje. Napfiíklad pokud je daÀová sazba vysoká, podnik mÛÏe vyuÏívat vût‰ího objemu cizího kapitálu a touto cestou vyuÏít efektu daÀového ‰títu, coÏ by bylo ve prospûch kompromisní teorie. V situaci, kdy by daÀové zv˘hodnûní nebylo tak markantní, mÛÏe spoleãnost vyuÏívat zejména interní zdroje, coÏ odpovídá pfiedpokladÛm teorie hierarchického pofiádku. Gilson (1997) [30] do‰el k závûru, Ïe chování spoleãností s finanãními potíÏemi neodpovídá pfiedpokladÛm ani jedné z teorií, Graham a Harvey (2001) [31] poukázali na rozdíly mezi teoretick˘mi v˘chodisky a praxí spoleãnosti. Za zváÏení stojí také fakt, Ïe obû teorie jsou zaloÏeny na urãit˘ch pfiedpokladech, t˘kajících se preferencí spoleãností na základû urãit˘ch charakteristik. Nelze v‰ak spoléhat na naprostou racionalitu tûchto subjektÛ, navíc kaÏdá spoleãnost mÛÏe mít rÛzné cíle, nehledû na to, Ïe manaÏefii spoleãnosti vÏdy nejednají ve prospûch vlastníkÛ, ale mohou prosazovat svoje zájmy. Napfiíklad se nechtûjí dûlit o kontrolu nad podnikem, tedy o svoji moc, a proto upfiednostÀují cizí kapitál pfied vydáním akcií. Tyto skuteãnosti pak vytváfií odklon reality od teorií, coÏ zmiÀuje i Xu a Birge (2008) [66]. Navíc se jednotlivé prÛzkumy mohou li‰it i na základû pouÏit˘ch metod testování dané teorie. Vût‰ina

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Ekonomika a management prací vyuÏívá metod regresní anal˘zy, lze vyuÏít i korelaãní anal˘zy, pouÏita byla v dosavadních prÛzkumech napfiíklad i dotazníková anketa, model CAPM, atd.

2. V˘sledky v˘zkumu determinant zadluÏenosti Platnost hypotéz tohoto v˘zkumu byla ovûfiována na vzorku dat 299 ãesk˘ch spoleãností v letech 2006 a 2007 bez ohledu na jejich právní formu, velikost nebo jinou charakteristiku. Spoleãné mûly pouze to, Ïe v dobû v˘bûru nebyla Ïádná z tûchto spoleãností v likvidaãním fiízení. Podniky jsou zde zastoupeny rovnomûrnû podle ‰esti vybran˘ch odvûtví dle klasifikace CZ-NACE, a to: A – Zemûdûlství, lesnictví a rybáfiství; C – Zpracovatelsk˘ prÛmysl; F – Stavebnictví; G – Velkoobchod a maloobchod, opravy a údrÏba motorov˘ch vozidel; I – Ubytování,

stravování a pohostinství; J – Informaãní a komunikaãní ãinnosti. Data byla získána z úãetních v˘kazÛ jednotliv˘ch spoleãností z Obchodního rejstfiíku, pfiiãemÏ stavové veliãiny byly zprÛmûrovány za dva roky. Následující tabulka 1 a tabulka 2 ilustrují charakteristiky jednotliv˘ch spoleãností, resp. odvûtví, t˘kající se míry zadluÏení. Z hlediska v˘voje v âeské republice poptávka po úvûrech v˘raznû rostla po roce 2002, podle Stavárka a Vodové (2010) dokonce nabídka úvûrÛ pfievy‰uje dlouhodobû rovnováÏnou úroveÀ. [59] Tabulka 1 pfiedstavuje prÛmûrné hodnoty (aritmetick˘ prÛmûr) a intervaly minimálních a maximálních hodnot míry celkového zadluÏení, tedy pomûr cizího kapitálu a celkového kapitálu, a míry dlouhodobého zadluÏení, kter˘ pfiedstavuje podíl dlouhodobého dluhu a celkového kapitálu. Zv˘raznûné buÀky v tab. 1 pfiedstavují nejvy‰‰í hodnoty.

PrÛmûrné, minimální a maximální hodnoty zadluÏení ãesk˘ch podnikÛ dle vybran˘ch odvûtví pro období 2006–2007

Tab. 1:

Odvûtví

Kriterium Míra celkového zadluÏení

Míra dlouhodobého zadluÏení

PrÛmûr

<MIN;MAX>

PrÛmûr

<MIN;MAX>

A

0,4916

<0,0688;0,9710>

0,1972

<0,0000;0,6647>

C

0,5797

<0,0714;1,1787>

0,0978

<0,0000;0,5717>

F

0,6657

<0,2026;1,0826>

0,0960

<0,0000;1,0534>

G

0,6840

<0,0896;1,2766>

0,0959

<0,0000;0,8678>

I

0,7978

<0,0297;2,3949>

0,2644

<0,0000;1,4247>

J

0,7222

<0,0333;5,3004>

0,0709

<0,0000;0,7491> Zdroj: vlastní zpracování

Je patrné, Ïe z hlediska míry zadluÏení vykázalo nejvy‰‰í podíl jak celkového, tak i dlouhodobého dluhu odvûtví Ubytování, stravování a pohostinství. V rámci celkového zadluÏení lze brát jako srovnávací kritérium ãasto uvádûn˘ podíl cizího a vlastního kapitálu 60:40. RovnûÏ lze tvrdit, Ïe zkoumané spoleãnosti vykázaly znaãn˘ objem krátkodobého cizího kapitálu. Tabulka 2 obsahuje tyto vybrané charakteristiky: procento pfiedluÏen˘ch spoleãností v kaÏdém odvûtví, pfiiãemÏ tento údaj byl vypoãítán na základû bodu indiference; procento spoleãností s negativním pÛsobením finanãní

páky; procento podnikÛ s nepfiimûfien˘m úrokov˘m krytím; hrubou pfiidanou hodnotu z jedné koruny úvûru; prÛmûrnou rentabilitu aktiv. Vybarvené buÀky pfiedstavují odvûtví s nejhor‰ím v˘sledkem pro dané kritérium, ohraniãené buÀky naopak odvûtví s nejlep‰ím hodnocením kritéria. První tfii charakteristiky pfiímo popisují pozici spoleãnosti z hlediska únosnosti míry zadluÏení, resp. situaci daného odvûtví. Lze vidût, Ïe kaÏd˘ z parametrÛ poskytuje odli‰né v˘sledky, pfiiãemÏ nejpfiísnûj‰ím ukazatelem je hodnocení na základû bodu indiference. Naopak „nejmírnûj‰í“ faktor je ukazatel pfiimûfienosti úrokového krytí.

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Determinanty ãesk˘ch podnikÛ dle odvûtví v období 2006–2007

Odvûtví

PfiedluÏené podniky (%)

Negativní pÛsobení FL (%)

Nepfiimûfiené úrok. krytí (%)

HPH z 1 Kã úvûru (Kã)

ROA

A

80

67

31

2,3043

0,0472

C

79

38

25

4,0182

0,1320 0,1041

F

61

22

12

3,0803

G

72

38

28

1,7199

0,1072

I

82

60

56

2,1276

-0,0233

J

90

27

16

3,6373

0,1374 Zdroj: vlastní zpracování

Legenda: A = Zemûdûlství, lesnictví a rybáfiství C = Zpracovatelsk˘ prÛmysl F = Stavebnictví G = Velkoobchod a maloobchod, opravy a údrÏba motorov˘ch vozidel I = Ubytování, stravování a pohostinství J = Informaãní a komunikaãní ãinnosti FL = Finanãní páka; Financial Leverage HPH = Hrubá pfiidaná hodnota ROA = Rentabilita kapitálu; Return on Assets

Bod indiference vychází ze vztahu EBIT
96

hodnota z jedné koruny úvûru ukazuje, Ïe nejménû efektu pfiinesl dluh v odvûtví Velkoobchod a maloobchod, opravy a údrÏba motorov˘ch vozidel, naopak nejvíce uÏitku pak ve Zpracovatelském prÛmyslu. Je tfieba vzít v úvahu, Ïe v pfiípadû této determinanty se vycházelo ze zru‰ené odvûtvové klasifikace OKEâ, tato data pochází z âeského statistického úfiadu, viz [12]. Napfiíklad je patrné, Ïe odvûtví Informaãní a komunikaãní ãinnosti se vyznaãuje velk˘m podílem pfiedluÏen˘ch spoleãností, alespoÀ na základû prvního kriteria, ov‰em efekt z koruny dluhu je pomûrnû vysok˘. DÛleÏitá je rentabilita aktiv, respektive návratnost kapitálu, vlastního i cizího. Toto kritérium ukazuje, jak efektivnû spoleãnost kapitál, tedy i dluh, vyuÏívala, a také tento ukazatel poukazuje na schopnost splácet závazky. Opût, odvûtví Informaãní a komunikaãní ãinnosti sice vykazuje na základû dan˘ch kriterií znaãné zadluÏení, ov‰em v rámci vybran˘ch odvûtví také nejvy‰‰í rentabilitu aktiv. Odvûtví Ubytování, stravování a pohostinství vykazovalo rentabilitu dokonce zápornou. Bez ohledu na stanovení vah jednotliv˘ch parametrÛ lze tvrdit, Ïe

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Ekonomika a management toto odvûtví pravdûpodobnû z hlediska zadluÏení, resp. kapitálové struktury vykazovalo v rámci hodnocen˘ch odvûtví nejhor‰í stav.

zadluÏení se li‰í pro jednotlivá odvûtví. Zmínûné determinanty byly uvaÏovány jako nezávisle promûnné a pfiedmûtem zkoumání bylo, zda existuje statisticky v˘znamn˘ vliv, a jaká je míra toho vlivu, na závisle promûnnou míru zadluÏení jakoÏto charakteristiku kapitálové struktury. Faktor podíl fixních aktiv pfiedstavuje podíl fixních aktiv na celkov˘ch aktivech podniku, zadrÏené zisky se nachází pfiímo v dané v˘‰i na stranû pasiv, úroková míra pfiedstavuje „efektivní úrokovou sazbu“, která odpovídá pomûru nákladov˘ch úrokÛ a cizího kapitálu; rentabilita aktiv je podíl zisku pfied zdanûním a úroky a aktivy; za velikost podniku byl uvaÏován objem trÏeb, za podíl hmotn˘ch aktiv to byl pomûr tûchto na celkov˘ch aktivech. Stáfií podniku se uvaÏovalo ode dne zápisu do Obchodního rejstfiíku, tedy ode dne vzniku. V‰echna statická data z úãetních v˘kazÛ byla za dva roky zprÛmûrována. Následující tabulky ukazují v˘sledky regrese, pfiiãemÏ zabarvené buÀky pfiedstavují statisticky v˘znamné hodnoty.

3. Testování hypotéz StûÏejní metodou k ovûfiení platnosti hypotéz bylo pouÏití regresní anal˘zy pomocí mnohonásobné regrese, která byla provedena v programu Statistica. Lineární model má tuto podobu: Y = α + βx1 + βx2+ βx3 + βx4+ βx5+ βx6 + βx7 + ε, kde xi jsou nezávisle promûnné, tedy determinanty jako je podíl fixních aktiv, zadrÏené zisky, úroková míra, rentabilita aktiv, velikost podniku, podíl hmotn˘ch aktiv, stáfií podniku, ε je náhodná sloÏka lineárního modelu, α a β jsou neznámé parametry. Pomocí regrese byla testována H1: Uvedené determinanty mají vliv na kapitálovou strukturu ãesk˘ch podnikÛ, resp. na míru dlouhodobého dluhu; H2: Uvedené determinanty mají vliv na kapitálovou strukturu ãesk˘ch podnikÛ, resp. na míru krátkodobého dluhu; H3: Míra vlivu determinant na úroveÀ celkového Tab. 3:

Regresní anal˘za pro sedm nezávisle promûnn˘ch a celkovou zadluÏenost

N=299

Regression Summary for Dependent Variable: Var8 (Spreadsheet 11) R= ,36374531 R2= ,13231065 Adjusted R2= ,11143840 F(7,291)=6,3391 p< ,00000 Std.Error of estimate: ,45008 Beta

Std.Err.of Beta

Intercept

B

St.Err.of B

t(291)

p-level

-18,2755

6,580341

-2,77730

0,005837

Podíl fixních aktiv

-0,039471

0,135094

-0,0707

0,242053

-0,29218

0,770358

ZadrÏené zisky

-0,076324

0,058650

-0,0000

0,000000

-1,30136

0,194166

Úroková míra

-0,000909

0,056045

-0,0001

0,004944

-0,01622

0,987066

Rentabilita aktiv

-0,289455

0,056499

-0,6190

0,120831

-5,12322

0,000001

Velikost podniku

-0,007676

0,058361

-0,0000

0,000000

-0,13153

0,895447

Podíl hmotn˘ch aktiv

-0,136817

0,130965

-0,2594

0,248261

-1,04468

0,297038

Stáfií podniku

0,172411

0,059348

0,0096

0,003294

2,90506

0,003953

Zdroj: vlastní zpracování

Na základû v˘sledkÛ lineární regrese lze vidût pozitivní vliv stáfií podniku a negativní vliv rentability, kter˘ je statisticky v˘znamn˘ na hladinû v˘znamnosti α = 0,05. V˘sledky tedy ukazují na inverzní vztah nezávisle promûnné rentabilita a závisle promûnné celkové zadluÏení

a pozitivní vztah stáfií podniku a celkového zadluÏení. Nicménû koeficient determinace, R2, kter˘ urãuje, jaké procento variability dat je vysvûtlováno dan˘m modelem, je velmi nízk˘, proto v˘sledek nelze brát za pfiíli‰ relevantní.

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Regresní anal˘za pro sedm nezávisle promûnn˘ch a dlouhodobou zadluÏenost

N=299


Std.Err.of Beta

Intercept

B

St.Err.of B

t(291)

p-level

-0,946654

2,991980

-0,316397

0,751928


0,412373

0,131664

0,344701

0,110058

3,132001

0,001913

ZadrÏené zisky

-0,032995

0,057161

-0,000000

0,000000

-0,577232

0,564229

Úroková míra

-0,005810

0,054622

-0,000239

0,002248

-0,106364

0,915367

Rentabilita aktiv

-0,044589

0,055064

-0,044489

0,054940

-0,809762

0,418739

Velikost podniku

-0,032509

0,056879

-0,000000

0,000000

-0,571542

0,568074


-0,005567

0,127641

-0,004924

0,112881

-0,043618

0,965239

Stáfií podniku

0,018962

0,057842

0,000491

0,001498

0,327822

0,743282


Regrese byla provedena i k ovûfiení vztahu sedmi nezávisle promûnn˘ch a dlouhodobé zadluÏenosti. V tomto pfiípadû se projevil pozitivní vliv podílu fixních aktiv, kter˘ je statisticky v˘znamn˘ na hladinû v˘znamnosti α=0,05. Koeficient determinace je v‰ak opût velmi nízk˘, nicménû vy‰‰í, neÏ u pfiedchozí situace, rovnûÏ míra vlivu je v tomto pfiípadû vy‰‰í. Následnû byl zkoumán vliv jednotliv˘ch nezávisle promûnn˘ch na celkovou zadluÏenost v rámci jednotliv˘ch odvûtví. V odvûtví Ubytování, Tab. 5:

stravování a pohostinství, Stavebnictví, Velkoobchod a maloobchod, opravy a údrÏba motorov˘ch vozidel a Zpracovatelsk˘ prÛmysl nebyl nalezen Ïádn˘ vliv statisticky v˘znamn˘ na hladinû v˘znamnosti α=0,05. Pomûrnû znaãná míra vlivu i v˘znam modelu pro daná data se v‰ak projevila v odvûtví Informaãní a komunikaãní sluÏby. Zde byl potvrzen pozitivní vliv promûnné zadrÏené zisky a negativní vliv promûnné velikost podniku jako statisticky v˘znamn˘ opût na hladinû v˘znamnosti α=0,05, viz tabulka 5.

Regresní anal˘za pro sedm nezávisle promûnn˘ch a celkovou zadluÏenost v rámci odvûtví Informaãní a komunikaãní ãinnosti

N=49


Std.Err.of Beta

Intercept Podíl fixních aktiv

B

St.Err.of B

t(291)

p-level

-10,5242

8,062660

-1,30530

0,199068

0,2138

0,208628

1,02468

0,311523

0,34077

0,332564

ZadrÏené zisky

2,90701

0,772244

0,0000

0,000000

3,76437

0,000524

Úroková míra

-0,05922

0,119462

-0,0018

0,003552

-0,49575

0,622720

Rentabilita aktiv

-0,10390

0,124549

-0,0842

0,100882

-0,83425

0,408977

Velikost podniku

-2,88355

0,770066

-0,0000

0,000000

-3,74455

0,000556


0,37232

0,322116

0,2788

0,241171

1,15587

0,254427

Stáfií podniku

0,16410

0,125661

0,0053

0,004039

1,30586

0,198879


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Ekonomika a management Urãitá závislost byla je‰tû nalezena v odvûtví Zemûdûlství, lesnictví a rybáfiství, a to pozitivní vliv promûnné podíl fixních aktiv na závisle promûnnou Tab. 6:

celkové zadluÏení. Nejde v‰ak o pfiíli‰ v˘znamn˘ v˘sledek, protoÏe parametr Beta i koeficient determinace dosahují nízk˘ch hodnot, viz tabulka 6.

Regresní anal˘za pro sedm nezávisle promûnn˘ch a celkovou zadluÏenost v rámci odvûtví Zemûdûlství, lesnictví a rybáfiství

N=49


Std.Err.of Beta

Intercept

B

St.Err.of B

t(291)

p-level

1,679056

3,586089

0,468210

0,642111


0,52854

0,174408

0,370187

0,122156

3,030450

0,004217

ZadrÏené zisky

0,83544

0,926053

0,000000

0,000000

0,902160

0,372244

Úroková míra

0,03285

0,131663

0,000924

0,003705

0,249500

0,804219

Rentabilita aktiv

-0,06864

0,131663

-0,179722

0,354701

-0,506690

0,615090

Velikost podniku

-1,06108

0,927868

0,000000

0,000000

-1,143570

0,259436


-0,26386

0,155499

-0,194225

0,114463

-1,696840

0,097308

Stáfií podniku

-0,06889

0,156526

-0,000792

0,001799

-0,44011

0,662166


Regrese byla doplnûna v˘poãtem charakteristiky variability, konkrétnû smûrodatné odchylky pro míru celkového zadluÏení podle odvûtví. Situace je znázornûna pomocí Box Plotu, vytvofieném v programu Statistica, kter˘ ukazuje Obr. 1:

rozpt˘lení nebo koncentraci okolo urãité hodnoty, pfiiãemÏ vzhledem k existenci odlehl˘ch hodnot zde není za tuto hodnotu uvaÏován aritmetick˘ prÛmûr, ale medián, viz obr. 1.

Box Plot pro celkové zadluÏení v rámci jednotliv˘ch odvûtví


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Ekonomika a management Je patrné, Ïe v odvûtví Informaãní a komunikaãní ãinnosti (na obr. 1 „Komunikace“) se hodnoty, aÏ na pfiípad odlehl˘ch hodnot, koncentrovaly okolo urãitého „bodu“. CoÏ by mohlo slouÏit jako argument pro potvrzení existence kompromisní teorie, která pfiedpokládá, Ïe spoleãnosti cílí urãitou optimální v˘‰i zadluÏení, resp. optimální kapitálovou strukturu. Dal‰ím argumentem, kter˘ rovnûÏ následuje pfiedpoklad kompromisní teorie, je pozitivní vztah mezi rentabilitou a mírou zadluÏení v tomto odvûtví, viz tab. 5. Kompromisní teorie pfiitom tvrdí, Ïe ziskovûj‰í spoleãnosti si mohou dovolit se více zadluÏovat. Pro vzorek v‰ech spoleãností se v‰ak projevil negativní vztah rentability aktiv a zadluÏení, coÏ odpovídá pfiedpokladu teorie hierarchického pofiádku. Proto se lze pfiiklánût k tomu, co tvrdí napfiíklad Gaud, Haan, Hinloopen, Majumdar, Farhat, Cotei, Abugri, Ghosh a Cai, Miglo a dal‰í, tedy Ïe by se mûly brát v úvahu obû teorie, Ïe mohou koexistovat, a Ïe Ïádná jednotlivû nevysvûtluje v‰echny skuteãnosti t˘kající se finanãního rozhodování podnikÛ. RovnûÏ lze tvrdit, Ïe tfietí hypotéza mÛÏe b˘t na základû v˘sledkÛ regrese potvrzena, pfiiãemÏ první dvû hypotézy lze potvrdit jen ãásteãnû.

Závûr Kapitálová struktura podniku mÛÏe b˘t ovlivnûna mnoha faktory, aÈ uÏ tzv. vnitfiními nebo tzv. vnûj‰ími. Vliv rÛzn˘ch determinant byl ovûfiován v mnoha v˘zkumech, jejichÏ v˘sledky se znaãnû li‰ily. Proto nelze vyslovit obecnou platnost vlivu konkrétních faktorÛ na kapitálovou strukturu podnikÛ, pfiestoÏe v urãit˘ch vztazích se ekonomové spí‰e shodují. Zájem o kapitálovou strukturu podnikÛ, kter˘ vzrostl po vydání tvrzení Millera a Modiglianiho v letech 1958 a 1963, vedl k vymezení teorií kapitálové struktury, pfiiãemÏ dvû zásadní, zdánlivû proti sobû stojící, jsou kompromisní teorie a teorie hierarchického pofiádku. KaÏdá z nich má mezi ekonomy své zastánce i odpÛrce. V posledních letech v‰ak pfievládá názor, Ïe tyto teorie se nevyluãují a Ïe finanãní rozhodování podnikÛ lze vysvûtlit pomocí pfiedpokladÛ obou teorií. DÛleÏité je také zmínit, Ïe v˘znamné v rámci ovûfiování jejich platnosti jsou pouÏité metody. Vzhledem k vysloven˘m hypotézám byla

100

v tomto pfiíspûvku k jejich testování vyuÏita „klasická“ metoda mnohonásobné regrese. V˘sledkem regrese je zji‰tûní, Ïe na celkovou zadluÏenost podnikÛ má pozitivní vliv stáfií podniku a negativní vliv rentabilita aktiv. Ov‰em vypovídací hodnota je vzhledem ke koeficientu determinace a mífie vlivu velmi nízká. Dlouhodobá zadluÏenost podnikÛ je pak podle v˘sledkÛ regrese ovlivnûna pouze podílem fixních aktiv, pfiiãemÏ opût vypovídací hodnota není vysoká. V˘sledky regrese determinant a míry celkového zadluÏení se v rámci jednotliv˘ch odvûtví li‰í. Kromû odvûtví Informaãní a komunikaãní ãinnosti a Zemûdûlství, lesnictví a rybáfiství nebyl nikde prokázán vliv Ïádné z nezávisle promûnn˘ch na celkové zadluÏení. Relevantní v˘sledky byly získány z provedené regrese v odvûtví Informaãní a komunikaãní ãinnosti, které jiÏ vykázaly pomûrnû v˘znamné hodnoty parametru beta a koeficientu determinace. Byl zde nalezen negativní vliv velikosti podniku a pozitivní vliv objemu zadrÏen˘ch ziskÛ na zadluÏení. Tento pozitivní vztah odpovídá pfiedpokladu kompromisní teorie, pfiiãemÏ toto je umocnûno v˘poãtem smûrodatn˘ch odchylek, kdy pro odvûtví Informaãní a komunikaãní ãinnosti lze tvrdit, Ïe podniky se koncentrovaly okolo urãité míry zadluÏení. Pokud se v‰ak v úvahu bere negativní vliv rentability aktiv na celkové zadluÏení v rámci v‰ech odvûtví, hovofií to ve prospûch teorie hierarchického pofiádku. Pro pfiesnûj‰í posouzení by bylo vhodné provést dále i jinou regresi neÏ lineární. Lze tedy tvrdit, Ïe tfietí hypotéza byla ovûfiena, první dvû ãásteãnû. RovnûÏ je patrné, Ïe lze nalézt jisté dÛkazy pro platnost kompromisní teorie i teorie hierarchického pofiádku, ãímÏ se tento pfiíspûvek pfiiklání k v˘sledkÛm prÛzkumÛ posledních let, tedy Ïe obû teorie ãásteãnû vysvûtlují finanãní rozhodování spoleãností. Ov‰em vzhledem k relevanci, kterou regrese ve vût‰inû pfiípadÛ v tomto v˘zkumu prokázala, lze více akcentovat pfiedpoklad dynamick˘ch teorií, kam patfií teorie hierarchického pofiádku, tedy Ïe jednotlivé spoleãnosti se sv˘mi charakteristikami, v˘chozími determinanty, cíli a strategiemi li‰í, a proto nelze hledat optimální kapitálovou strukturu. Respektive na základû této jedineãnosti nelze vyjádfiit unifikované pravidlo finanãního rozhodování podnikÛ.

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Ekonomika a management [24] FRANK, M., GOYAL V. Testing the Pecking Order Theory of Capital Structure. Journal of Financial Economics. 2003, Vol. 67, Iss. 2, pp. 217-248. ISSN 0304-405X. [25] FRANK, M. Z, GOYAL, V. K. [Chap.] 12. Trade-off and Pecking Order Theories of Debt. In ECKBO, B. E. (ed.). Handbook of Corporate Finance: Empirical Corporate Finance. Vol. 2. Amsterdam: North-Holland, 2008. p. 578. ISSN 1568-4997. [26] FRIEND, I., LANG, L. Anempirical test of the impact of managerial self-interest on corporate capital structure. The Journal of Finance. 1988, Vol. 43, Iss. 2, pp. 271-281. ISSN 1540-6261. [27] GAUD, P., HOESLI, M., BENDER, A. Debt Equity Choice in Europe. FAME Research Paper Series. 2005, p. 41, rp 152. [28] GHOSH, A., CAI, F. Capital Structure: New Evidence of Optimality and Pecking Order Theory. American Business Review. 1999, Vol. 17, Iss. 1, pp. 32-38. ISSN 07432348. [29] GIBSON, B. An international comparison of small firm financial structure council for small business. Paper presented at 47th World Conference, San Juan, Puerto Rico June 16-19, 2002, ICSB, 2002-031. [30] GILSON, S. C. Transactions Costs and Capital Structure Choice: Evidence from Financially Distressed Firms. The Journal of Finance. 1997, Vol. 52, Iss. 1, pp. 161-196. ISSN 1540-6261. [31] GRAHAM, J. R., HARVEY, C. R. The theory and practice of corporate finance: Evidence from the field. Journal of Financial Economics. 2001, Vol. 60, Iss. 2-3, pp. 187-243. ISSN 0304-405X. [32] HAAN, L., HINLOOPEN, J. Debt or equity? An empirical study of security issues by Dutch companies. Research Memorandum WO&E: Tinbergen Institute Discussion Papers, 1999, p. 25, Iss. 577/9910. [33] HARRIS, M., RAVIV, A. The theory of capital structure. The Journal of Finance. 1991, Vol. 46, Iss. 1, pp. 297-355. ISSN 1540-6261. [34] HELWEGE, J., LIANG, N. Is There a Pecking Order? Evidence from a Panel of IPO Firms. Journal of Financial Economics. 1994, Vol. 40, Iss. 3, pp. 429-458. ISSN 0304-405X. [35] HEYMAN, D., DELOOF, M., OOGHE, H. The financial structure of privately held Belgian firms. Small Business Economics. 2008, Vol. 30, Iss. 3, pp. 301-313. ISSN 1573-0913. [36] HRD¯, M., HOROVÁ, M. Aktuální problémy strategického finanãního fiízení podnikÛ v âR. E+M Ekonomie a Management. 2007, roã. 10, ã. 4, s. 80-86. ISSN 1212-3609.

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[37] HUANG, R., RITTER, J. R. Testing Theories of Capital Structure and Estimating the Speed of Adjustment. Journal of Financial and Quantitative Analysis. 2009, Vol. 44, Iss. 2, pp. 237-271. ISSN 0022-1090. [38] JALILVAND, A., HARRIS, R. R. Corporate Behaviour in Adjusting to Capital Structure and Dividend Targets: An Econometric Study. The Journal of Finance. Vol. 39, Iss. 1, pp. 127-145. ISSN 1540-6261. [39] JORDAN, J., LOWE, J., TAYLOR, P. Strategy and financial policy in UK small firms. Journal of Business Finance and Accounting. 1998, Vol. 25, Iss. 1-2, pp. 1-27. ISSN 1468-5957. [40] KAMATH, R. R. Long-Term Financing Decisions: Views and Practices of Financial Managers of NYSE Firms. The Financial Review. 1997, Vol. 32, Iss. 2, pp. 331-356. ISSN 1540-6288. [41] KESTER, C. W. Capital and ownership structure: a comparison of United States and Japanese manufacturing corporations. Financial Management. 1986, Vol. 15, Iss. 1, pp. 5-16. ISSN 1755-053X. [42] KISLINGEROVÁ, E. a kol. ManaÏerské finance. 2. dopl. vyd. Praha: C. H. Beck, 2007. 746 s. ISBN 978-80-7179-903-0. [43] MAJUMDAR, R. Corporate Borrowing and Growth Opportunities: Evidence from Indian Manufacturing Sector. The IUP Journal of Applied Finance. 2010, Vol. 16, Iss. 5, pp. 23-35. ISSN 0972-5105. [44] MARSH, P. The Choice between Equity and Debt: An Empirical Study. The Journal of Finance. Vol. 37, Iss. 1, pp. 121-144. ISSN 1540-6261. [45] MAZUR, K. The Determinants of Capital Structure Choice: Evidence from Polish Companies. International Advances in Economic Research. 2007, Vol. 13, Iss. 4, pp. 495-514. ISSN 1573-966X. [46] MECKLING, W. H., JENSEN. M. C. Theory of the Firm: Managerial Behavior, Agency Costs and Ownership Structure. Journal of Financial Economics. 1976, Vol. 3, Iss. 4, pp. 305-360. ISSN 0304-405X. [47] MIGLO, A. The Pecking Order, Trade-off, Signaling, and Market-Timing Theories of Capital Structure: A Review. Working paper, 2010. Available from: . [48] MILLER, M. H. Debt and Taxes. The Journal of Finance. 1977, Vol. 32, Iss. 2, pp. 261-75. ISSN 1540-6261. [49] MODIGLIANI, F, MILLER, M. H. Corporate Income Taxes and the Cost of Capital: A Correction. The American Economic Review. 1963, Vol. 53, Iss. 3, pp. 433-443. ISSN 0002-8282.

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Ekonomika a management [50] MOMANI, G. F., ALSHARAYRI, M. A., DANDAN, M. M. Impact of Firm’s Characteristics on Determining the Financial Structure On the Insurance Sector Firms in Jordan. Journal of Social Science. 2010, Vol. 6, Iss. 2, pp. 282-286. ISSN 1549-3652. [51] MYERS, S. C., MAJLUF, N. S. Corporate Financing and Investment Decisions When Firms Have Information That Investors Do Not Have. Journal of Financial Economics. 1984, Vol. 13, Iss. 2, pp. 187-221. ISSN 0304-405X. [52] NEUMAIEROVÁ, I., NEUMAIER, I. Úvaha o optimální zadluÏenosti. Finance a úvûr. 1996, roã. 46, ã. 1, s. 51-61. ISSN 0015-1920. [53] NGUYEN, T. D. K., RAMACHANDRAN, N. Capital structure in small and medium-sized enterprises: the case of Vietnam. ASEAN Economic Bulletin. 2006, Vol. 23, Iss. 2, pp. 192-211. ISSN 1793-2831. [54] RAJAN. R. G., ZINGALES, L. What Do We Know About Capital Structure? Some Evidence from International Data. The Journal of Finance. 1995, Vol. 50, Iss. 2, pp. 1421-1459. ISSN 1540-6261. [55] REMMERS, L., TOY, N., STONEHILL, A., WRIGHT, R., BECKHUISEN, T. Industry and size as debt ratio determinants in manufacturing internationally. Financial Management. 1974, Vol. 3, pp. 24-32. ISSN 1755-053X. [56] SCHWARTZ, E., ARONSON, R. Some Surrogate Evidence in Support of the Concept of Optimal Financial Structure. The Journal of Finance. 1967, Vol. 22, pp. 10-18. ISSN 1540-6261. [57] SCOTT, D. F., MARTIN, J. D. Industry Influence on Financial Structure. Financial Management. 1975, Vol. 4, Iss. 1, pp. 67-73. ISSN 1755-053X. [58] SHYAM-SUNDER, L., MYERS, S. C. Testing Static Trade-off against Pecking Order Models of Capital Structure. Journal of Financial Economics. 1999, Vol. 51, Iss. 2, pp. 219-244. ISSN 0304-405X. [59] STAVÁREK, D., VODOVÁ, P. Anal˘za dlouhodob˘ch vazeb na ãeském trhu úvûrÛ. E+M Ekonomie a Management. 2010, roã. 13, ã. 3, s. 83-95. ISSN 1212-3609.

[60] STENBACKA, R., TOMBAK, M. Investment, Capital structure, and Complementarities between Debt and New Equity. Management Science. 2002, Vol. 48, Iss. 2, pp. 257-272. ISSN 1526-550. [61] TAO, L., JIANHUI, J. A. Research on Debt Financing Effects Based on the Power Companies. In The 2008 International Conference on Risk Management & Engineering Management. 1st ed. Los Alamitos: IEEE Computer Society, 2008, Vol. 5, pp. 665-669. ISBN 978-0-7695-3402-2. [62] TITMAN, S., WESSELS, R. The Determinants of Capital Structure Choice. The Journal of Finance. 1988, Vol. 43, Iss. 1, pp. 1-19. ISSN 1540-6261. [63] VALACH, J. K diskuzi o optimalizaci a determinantech kapitálové struktury podniku. âesk˘ finanãní a úãetní ãasopis. 2008, roã. 3, ã. 1, s. 99-102. ISSN 1802-2200. [64] VASILIOU, D., ERIOTIS, N., DASKALAKIS, N. Testing the pecking order theory: the importance of methodology. Qualitative Research in Financial Markets. 2009, Vol. 1, Iss. 2, pp. 85-96. ISSN 1755-4179. [65] VOULGARIS, F., ASTERIOU, D., AGIOMIRGIANAKIS, G. Capital Structure, Asset Utilization, Profitability and Growth in the Greek Manufacturing Sector. Applied Economics. 2002, Vol. 34, Iss. 11, pp. 1379-1388. ISSN 1466-4283. [66] XU, X., BIRGE, J. R. Operational Decisions, Capital Structure, and Managerial Compensation: A News Vendor Perspective. The Engineering Economist. 2008, Vol. 53, Iss. 3, pp. 173-196. ISSN 1547-2701. Ing. Pavlína Prá‰ilová Univerzita Pardubice Fakulta ekonomicko-správní Ústav ekonomiky a managementu [email protected]


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Abstract DETERMINANTS OF CAPITAL STRUCTURE WITHIN CZECH COMPANIES Pavlína Prá‰ilová Determinants of capital structure of companies have been studied since Miller’s and Modigliani’s work was published in 1958. There was researched impact of various determinants as taxes, asymmetric information, return on assets and others on capital structure. With this issue are associated theories of capital structure which validity has been tested. There are two basic theories, the trade-off theory and the pecking order theory. Interesting is, that results often differ in various researches. This paper deals with determinants of capital structure within czech companies and also tries to verify the validity of capital structure theories. Specifity is, that this paper is also industryoriented, there were researched 299 companies from 6 industries. First, there was performed a background research among works of economists who are related with this topic. The research of czech companies was performed by means of multiple regression. There was surveyd impact of proportion of fixed assets, retained earnings, interest rate, return on assets, size of company, proportion of tangible assets and company age on capital structure or on long-term debt and on total debt of company, and level of cross-sectional impact of these determinants. Relevant results were found for one industry: the Information and communication activities. There was found a negative impact of company size and a positive impact of retained earnings on total debt. This acknowledges the trade-off theory, which was stressed by studying of standard deviations. But when is accepted a cross-sectional negative impact of return on assets on total debt, it highlights the validity of the pecking order theory. With consideration of all results and their relevance, there could be declared that both theories explain something of capital structure decisions. There could be stated the impossibility of searching any unified rule for financial decisions of companies. Key Words: capital structure, financial decisions, trade-off theory, pecking order theory, regression analysis. JEL Classification: G32.

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Marketing a obchod

ANTECEDENTS AND OUTCOMES OF PERCEIVED SERVICE VALUE: EVIDENCE FROM SLOVENIA Aleksandra Pisnik Korda, Boris Snoj, Vesna Îabkar

Introduction The world economy is becoming intensely service-oriented. This trend is evidenced by the vast number of marketing research projects that focus on services [8]. Further, the downturn in today’s economic markets underscores the importance of perceived value for customers. Also, for firms operating in former socialist economies of Central Europe, some authors [25] pointed to evidence of a high degree of environmental turbulence, as demonstrated by rising unemployment levels, high inflation and fluctuating growth rates. Due to global crisis today, situation in these economies has changed even to the worse. Directly related to economic pressures, customers are increasingly price sensitive and demand better quality products and services. According to the economic theory and practical experience, the importance of the perceived value of products and services grows during periods of economic recession [51]. In such circumstances, customers are more sensitive to »value-for-money« deals. It is well known that it is unreasonable for marketers to increase the perceived value of their offerings by lowering prices, namely increasing the benefits of offerings for customers can be more effective. From this perspective, the quality and image of offerings are among the most important resources in which marketers can invest. The majority of perceived value studies are implemented in developed economies, especially the U.S. [54]. In Europe, only few researches have dealt with more complex models, with perceived value of banking services as a central concept [4], [2], [54], [12].

However, thus far, no such studies are yet implemented in former socialist states in Europe. Additionally, although the concepts of reputation, perceived quality and perceived value are among organizational resources that display the characteristics typical for creating competitive sustainable advantage, there remains a lack of studies that incorporate reputation as an antecedent of perceived value. This paper aims to examine perceived quality outcomes and antecedents, including reputation, and to test the model in the new context of a former Central European socialist economy. Service sectors in the former Central European socialist economies are challenging environments in which to examine the perceived quality construct. In the early days, after Slovenia attained independence in 1991 and began the process of transition, banks in Slovenia were preoccupied with reconstruction of core business processes [18]. In most of the former socialist countries that joined the EU, credits for banking sector rehabilitation go to the foreign banks that dominate these markets. In Slovenia has the ownership structure in the banking sector remained characterized by the prevalent domestic ownership. State-owned banks are facing regulatory changes, demand changes, technological changes and non-bank competitors which are all factors that increase the need for more intensive market-orientation of banks. The results of the study by [18] show the high level of homogeneity of banks in Slovenia concerning their product supply and categorization of their target customer groups. All the banks are universal banks and offer a broad array of products. For most of them, the national market is their target market,

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Marketing a obchod although they usually do not cover the entire market but only its segments. Small domestic banks attract more regional and older customers while foreign banks more autonomous, younger and more educated customers. Banks with a majority foreign ownership demonstrate more intense customer orientation. Increasing the market share was selected as a key strategic goal and a focus differentiation dominates among their business strategies. Solely the biggest bank in the country has pursued an internationalization strategy. Only recently have banks in Slovenia begun to focus on customer activities, such as satisfaction and loyalty, mainly due to the intensification of foreign competition. However, up to now banks did not pay any attention to the concept of perceived value, which is the focus of the present study.

1. Theory Background and Hypotheses Development Perceived value is the essential result of marketing activities and is a central element in relationship marketing [41]. Perceived value has proven to be a difficult concept to define and measure [52], [55], [24]. Broadly, it can be defined as the customer’s overall assessment of the utility of a product (or service) [55]. One common element of different authors’ definitions concerning customer-perceived value is that perceived value is a highly subjective and personal concept [39] and cannot be objectively defined by an organization. However, what constitutes value appears to be highly personal, idiosyncratic, and may vary widely from one customer to another [24], [55] as well within one customer in different circumstances (e.g. time, place, competitive offerings). According to the literature review, there are two main research streams of perceived value conceptualization: (a) as a one-dimensional construct, mainly used in older research [32], [55], [23], [6], and; (b) as multi-dimensional construct used in more recent research [24], [45], [49], [44], [43]. While there are several research projects concerning the relationship among perceived quality and customer satisfaction in retail banking, only few projects have dealt with more complex models of the perceived value of banking services. Incorporating perceived value as a mediator

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between perceived quality and customer satisfaction can have an important informative and consequently strategic role for the organization. As [31] state, it is important that in situations when customers may be “satisfied”' with “what”' was delivered (the core) and “how”' it was delivered (the relational), the organization also should have the information if customers got their “money's worth”. The quality of service is the fundamental element in the perception of perceived value, as it is one of the most difficult concepts for competitors to imitate [38] and the base for sustained differentiation and competitive advantage [42]. The majority of authors who explore perceived service quality, define it as the result of customers' subjective judgments of the level of the service offering and its delivery. Perceived service quality is also recognized as a multidimensional and multiatributable concept [28]. The most frequently used scales in the measurement of perceived service quality are SERVQUAL [39] and SERVPERF [11]. Both scales are also used in numerous research projects concerning banking services [4], [3], [29], [22], [53], [54]. Authors researching relationships in the models of perceived service value ascertain that higher perceived service quality leads to higher perceived service value [46], [47]. These studies can be can be divided into: (a) prior authors who explore the direct relationship between perceived quality and customer satisfaction, without taking into account the mediating role of perceived value [16], [26], [54], and; (b) present-day authors who explore the relationship between perceived quality and customer satisfaction while including perceived value as a mediating variable [10]. Models in which authors proposed the direct impact of perceived quality on customer satisfaction (without taking into account the relationship between perceived quality and perceived value) produced only a partial picture [31]. For example, in such a case, customers assess their satisfaction with a certain product or service, but there is no data on their assessment of the benefits compared with their efforts and sacrifices. Consequently it is important that in their models researchers include perceived value as the predecessor of customer satisfaction because perceived quality is an important predecessor of perceived value,

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Marketing a obchod which, in turn, reflects on customer satisfaction and loyalty [20]. Therefore, we propose the following hypotheses: H1: The higher the perceived quality of banking services, the higher their perceived value. H2: The higher the perceived quality of banking services, the higher the customer satisfaction with these services. Reputation is customers’ collective judgments of a corporation based on assessments of the financial, social, and environmental impacts attributed to the corporation over time [5], [21]. According to [7], reputation captures the set of corporate associations that individuals outside the organization believe that are central, enduring and distinctive aspects of the organization. Reputation is typically an intangible resource that is hard to imitate [15] and that has the potential to increase the value of a product [14]. It has an important influence on the norms and expectations [36] and is among the most reliable indicators of a company’s ability to satisfy customer wants [33]. Reputation is also one of the most important indicators of perceived service quality [40]. Literature review reveals that bank reputation plays an important role in determining purchasing behavior where quality of services can not be evaluated prior to purchase [50], [40]. Hence, we propose the following hypotheses: H3: More favorable bank reputation leads to higher perceived banking service quality. H4: More favorable bank reputation indicates higher perceived banking service value. Customer satisfaction is according to [19] who endorses an exit-voice theory, a function of expectations and perceived performance. Some authors [35], [27] argue that it is a cumulative construct that is affected by market expectations and performance perceptions in any given period and is affected by past satisfaction from period to period. However, some authors specify satisfaction as a function of perceived quality and "disconfirmation" – the

extent to which perceived quality fails to match prepurchase expectations [1]. Perceived product value is considered as one of the most influential antecedents of customer satisfaction and loyalty [17], [38], as well as one of the most important indicators of repurchase behaviors [10]. Perceived value, as well as its antecedences and consequences, are also relevant issues in retail banking management. Using these constructs, one can better understand the competitive advantages of retail banks and their ability to attract and retain customers. Although perceived product value recently receives significant research interest, there is still limited research attention focused on perceived service value. Therefore we propose that these relations exist also in retail banking. Therefore, our final hypothesis is: H5: The higher the perceived value of banking services, the higher the customer satisfaction with these services.

2. Methodology The measurement instrument for the empirical study was developed in three phases. First, some of the relevant items for the questionnaire were taken from the literature. This preliminary phase also included a focus group with graduate students at the University of Maribor. Items from the original SERVPERF scale [11] were used to measure perceived quality, items for the measurement of perceived value were adopted from [10], items for the measurement of bank reputation were adopted from [40] and, finally, [50] and [36] scale were adopted for the measurement of customer satisfaction. In the second phase, in-depth interviews with 8 banking managers and 4 experts from the marketing field were conducted to generate an additional pool of items. Then the questionnaire was examined by 6 specialists (4 academics and 2 in the field of marketing research methods) to determine content validity and help avoid redundancy. In the third phase, to test for internal consistency of the scales used in the final study and to further reduce the number of items, a pilot survey was conducted on a sample of 234 retail banking customers, mostly in the Styria region of Slovenia.

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Marketing a obchod In the final study, the items in the questionnaire were measured on a 5-point Likert scale (from 1 = “strongly disagree” to 5 = “strongly agree”). Eleven items measured perceived quality, four items measured perceived value, four items measured reputation, and four items measured satisfaction, for a total of 23 items. Data for the main research was collected from 700 retail banking customers in Slovenia in June 2007 by means of a telephone interview. The stratus sample framework was representative regarding retail banking customers’ structure by the number of inhabitants in each Slovenian region. The final structure of the sample was also in accordance with the market shares of retail banks in Slovenia at the time of research. Exploratory factor analysis, confirmatory factor analysis and structural equation modeling are used in the data analysis.

3. Results First, the dimensionality of researched constructs by performing exploratory factor analysis is assessed. Results, presented in Table 1, show Tab. 1:

that the communalities of all items are relatively high and exceed the value of 0.40. For perceived service quality, a three-factor solution is proposed: core service (SQ1, SQ3 and SQ5), physical evidence (SQ6, SQ7, SQ8 and SQ9) and safety & confidence (SQ24, SQ27, SQ28 and SQ29). Other research constructs are unidimensional. The total variances extracted for all constructs are above 60 %, Cronbach Alphas are above 0.70 and K-M-O measures are appropriate. Second, confirmatory factor analysis (CFA) for perceived service quality is performed. Two measurement models are compared: (a) a onefactor model and (b) a multi-factor model. Summary statistics for both models are shown in Table 2. Concerning perceived quality of retailing banking services, the multi-factor model is found to outperform the one-factor model on absolute measures (χ2, GFI, and RMSEA), incremental fit measure (CFI), and parsimonious fit measures (χ2/df). The majority of the fit indices are within the suggested interval.

Communalities and Factor Loadings for Perceived Quality

Items of perceived quality

Communalities

Factors 1

2

3

SQ1_This bank offers me a complete range of products.

0.783

0.840

SQ2_This bank is innovative.

0.828

0.865

SQ3_This bank matches my specific needs.

0.702

SQ4_Employees in this bank are neat in appearance.

0.614

0.607

SQ5_This bank has up-to-date facilities and equipment.

0.780

0.863

SQ6_The outdoor facilities of my bank are visually appealing.

0.786

0.868

SQ7_Informative materials (website, advertisments, brochures, etc.) are visually appealing.

0.479

0.596

SQ8_The employees in this bank are well educated and professional.

0.512

0.562

SQ9_In this bank my money and savings are safe.

0.559

0.699

SQ10_Using services at outside bank facilities (ATM, telephone banking, e-banking) is safe.

0.547

0.737

SQ11_Recommedations of employees in this bank are trustworthy.

0.650

0.736

0.811

Variance extracted in %

42.60

12.66

10.55

Cronbach Alpha

0.795

0.838

0.712

K-M-O measure: 0.839 Total variance extracted: 65.82 % Source: Authors

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Marketing a obchod Tab. 2:

Summary Statistics for One-Factor and Multi-Factor Models

Perceived Quality

One-Factor Model

Multi-Factor Model*

χ2/df

χ2/df = 125.5 / 41

= 266.61 / 44

RMSEA = 0.099

RMSEA = 0.094

NFI = 0.92

NFI = 0.97

CFI = 0.93

CFI = 0.97

SRMR = 0.184

SRMR = 0.028

GFI = 0.83

GFI = 0.97

* Core service, Safety & Confidence and Physical Evidence

In addition to Cronbach’s Alpha, construct reliability (CR) measures are used to assess the reliabilities of the perceived quality subscales. The reliability coefficient of the three subscales range from 0.84 to 0.89, which meets the standard of 0.6, as suggested by [34]. Next, construct validity of the single subscales is assessed by examining convergent and discriminant validity. Evidence of convergent validity in the single constructs is determined by inspection of the variance extracted (AVE) for each factor. CFA results show that AVE reaches the suggested value of 0.50 [13], and t-test results of all correlations between suggested

Tab. 3:

Source: Authors

dimensions are statistically significant. Also, discriminant validity is assessed for the subscales of perceived quality of retail banking. Several CFAs are run for each possible pair of constructs, first allowing for correlation between the two constructs and then fixing the correlation between the constructs at 1. In every case, the chi-square differences between the fixed and free solutions are significant at p<0.05 or higher. Finally, reliability, convergent validity and discriminant validity for all constructs in the measurement model (perceived quality, reputation, perceived value and satisfaction) are tested, as shown in Table 3.

Items, Construct Reliabilities and Average Variance Extracted

Construct

Dimensions and items

CR

AVE

λ

Reputation

This bank is respected.

0.88

0.66

0.86

23.27

α = 0.82

This bank is trustworthy.

0.86

26.77

This bank is successful.

0.83

22.71

My friends have positive opinions about this bank.

0.69

17.84

Perceived value

This bank offers me a lot of benefits.

0.66

17.17

α = 0.78

In this bank, the ratio between give and get components is very fair.

0.76

20.29

0.74

16.26

0.75

19.02

0.70

17.42 22.82

0.77

0.52

In terms of my relationship with this bank, I perceive more positive than negative things. Perceived quality Core service.

t-value

α = 0.86

Physical evidence. Safety and confidence.

0.79

Satisfaction

The services of this bank meet my expectations.

0.85

22.18

α = 0.87

I have good experiences with this bank.

0.82

19.15

I am satisfied with this bank.

0.78

0.81

0.53

0.59

0.62

13.53 Source: Authors

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Marketing a obchod In the final stage of the research, the proposed conceptual model is tested using

Fig. 1:

structural equation modeling. The overall structural model is shown in Figure 1.

Standardized Path Estimates

Source: Authors

With respect to overall model fit, the chisquare statistic indicates some discrepancies between the data and the proposed model (χ2=277.65/df = 60; p < 0.05). A significant chisquare indicates a non-perfect fit of the model to the data. However, other global fit indices Tab. 4:

suggest an adequate fit of the model. The RMSEA index of the model is 0.072, which is close to the range for a good fit, but still suggests a reasonable fit. Also, the majority of other fit indices suggest that the global model fit is acceptable.

Estimated Effects within the Causal Model

Relationships

Standardized regression coefficient

t-value

Significance

H1: Perceived quality – Perceived value

β = 0.617

4.996

p<0.01

H2: Perceived quality – Customer satisfaction

β = 0.410

8.440

p<0.01

H3: Reputation – Perceived quality

Υ = 0.830

15.338

p<0.01

H4: Reputation – Perceived value

Υ = 0.010

0.059

p>0.1

H5: Perceived value – Customer satisfaction

β = 0.568

8.928

p<0.01 Source: Authors

Table 4 provides an overview of the estimated effects within the causal model regarding the selected hypotheses. As predicted by H1, perceived retail banking service quality is strongly positively related to perceived value (β=0.62; p<0.01). The relationship between

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perceived quality and customer satisfaction with retail banking services is weaker (β=0.41; p<0.01), but significant, as we proposed in H2. Therefore, it can be confirmed that the relationship between perceived quality and customer satisfaction is direct, but also indirect through

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Marketing a obchod perceived value. Further, the effect of bank reputation on perceived service quality is positive, significant and very strong (Υ=0.83; p<0.01). Therefore, H3 is accepted. However, the relationship between bank reputation and perceived service value is non-significant and also very weak (Υ=0.01). Therefore, H4 is rejected. As expected, positive and significant findings are also returned for the path from perceived value to customer satisfaction (β=0.57; p<0.01). According to these findings, our final hypothesis, H5, is confirmed. The indirect effect of perceived quality on customer satisfaction through perceived value is also significant, with a regression coefficient of 0.350 (t-value=5.03). The results indicate that the total effect of perceived quality on customer satisfaction (0.76) is much stronger than the direct relationship (0.41).

Conclusions Today, in circumstances of increased global competition and economic recession, it is of prime importance that companies, especially those operating in service industries that are principally offering intangibles to their customers, focus their activities to increasing the perceived value of their offerings by increasing the benefits instead of lowering their prices. The present paper fills existing research gaps concerning the general lack of: (1) research dealing with the perceived value as a crucial component of overall customers’ perceptions of services; (2) research projects dealing with antecedents and consequences of services perceived value; and (3) using complex perceived value models incorporating also the reputation as an important resource of company competitive sustainable advantage. Despite the importance of perceived value for organizational performance, the concept of perceived value has not received enough attention in the literature, especially regarding its relationships with its antecedents and consequences. While relationships of perceived value with perceived quality, customer satisfaction and loyalty are well researched, the majority of more complex perceived value models are implemented in the U.S., few in the EU, but none in the context of retail banking services in former socialist countries in Europe.

The research demonstrates that the concept of perceived value is valid and reliable for service firms that operate in former socialist countries in Europe. It can be applied to understand customers’ overall assessments of the utility of bank services. In the empirical study of retail banking services, we linked: (1) reputation directly and indirectly to perceived value and (2) perceived quality directly and indirectly to customer satisfaction. First, considering the relationship between reputation and perceived value, results of our research support only an indirect relationship. Second, the perceived value construct is found to be mediating between perceived quality and customer satisfaction, as is often the case in other industries [9], [10], [30]. The results also show that the total effect of perceived quality on customer satisfaction (0.76) is much stronger than the direct relationship (0.41), so it is important for managers to consider the total effects because, otherwise, the relationship can be seen as much weaker. It is also important for managers in retail banks to consider perceived quality as a multidimensional construct, where safety, physical evidence and confidence and employees are important, because focusing only on core service quality is to narrow. Further, results of our research show that reputation has direct, positive and very strong impact on perceived quality. Banks are perceived as respected, trustworthy and successful, consumers have in general positive opinion about their banks. As reputation consists of mainly emotional perceptions that are related to past experiences, and is an important antecedent of perceived quality, which also includes a set of subjective components we can conclude that also in former socialist countries in Europe customers do not perceive only a rational components of offerings but are also more and more sensitive to emotional aspects of services. Reputation, perceived quality, perceived value and customer satisfaction are interlinked, intangible, complex and relatively vague, but strategically important concepts in the retail banking industry of former socialist countries in Europe. Therefore, managers’ decisions regarding their activities with customers should be holistic and systematic, taking into account both direct and indirect effects among the researched concepts.

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Marketing a obchod In assessing the implications of this study, its limitations must be acknowledged. Because the results are directly relevant only to customers of retail banking services, generalizations of the findings beyond the immediate population observed should be made with caution. Also, the model of perceived value could be further expanded to account for more indicators of perceived value and customer satisfaction (e.g. culture, market orientation), as well as more consequences of customer satisfaction, e.g., loyalty in addition to those evaluated as part of this study. Specifically, the concept of consumers’ trust in banks in the context of former socialist economies needs to be examined since it is expected that consumers in these countries have less experience with banks in comparison to consumers from more developed economies. Together with the managerial implications of findings, public policy implications for the banking sector could be established.

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Aleksandra Pisnik Korda, Ph.D, Assistant Professor University of Maribor Faculty of Economics and Business Marketing Department [email protected] Boris Snoj, Ph.D, Professor of Marketing University of Maribor Faculty of Economics and Business Marketing Department [email protected] Vesna Îabkar, Ph.D, Associate Professor University of Ljubljana Faculty of Economics Marketing Department [email protected]


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Abstract ANTECEDENTS AND OUTCOMES OF PERCEIVED SERVICE VALUE: EVIDENCE FROM SLOVENIA Aleksandra Pisnik Korda, Boris Snoj, Vesna Îabkar Perceived value is considered one of the most influential measures of customer satisfaction and is of growing interest to scholars and marketing managers. This paper aims to test a model of perceived value antecedents and consequences in the retail banking industry in the former socialist economy in central Europe. This environment should be of interest to researchers since the majority of perceived value studies have been implemented in developed economies, especially the U.S and even in these countries with an exception of U.S.A. only few researches have dealt with more complex models, with perceived value of banking services as a central concept. However, thus far, no such studies are yet implemented in former socialist economies of Europe. The study also extends extant research by incorporating reputation as an antecedent to perceived value and as an important resource of company competitive sustainable advantage. Content validity of the instrument was assessed by conducting interviews with experts in the field, and face validity was assessed by means of pre-tests with members of the target population (focus groups). In addition, viewpoints related to construct validity, convergent validity, discriminant validity and nomological validity were assessed with exploratory factor analysis (EFA), confirmatory factor analysis (CFA) and structural equation modeling (SEM). Data for empirical research is collected from 700 retail banking customers in Slovenia. A structural model with four reflective constructs is evaluated to test the hypothesized relationships. Results indicate the importance of perceived value as a mediating variable in the quality – satisfaction relationship. However, our results show that reputation influences perceived value, only indirectly, via perceived service quality. Key Words: perceived quality, perceived value, reputation, satisfaction, retail banking services. JEL Classification: M30, M31.

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FACEBOOK ADVERTISING AND ITS EFFICIENCY ON THE SLOVAK MARKET Martin Vejaãka

Introduction At the beginning of the 21st century web has changed into the new form. Web experts identified the trend of user generated content on the web and started to call it web 2.0. Typical examples of web 2.0 terms are blogs, wikis, virtual worlds and currently very popular social networking sites. The web social networks are virtual places where millions of people meet, chat, share their photos, videos, opinions on all possible topics. Web social networks are becoming integrated into mobile devices (smartphones, tablets, etc.), what makes them even more accessible practically anywhere. The most popular web social networks are MySpace, LinkedIn, MySpace, Twitter and the most populated and popular – Facebook.

Tab. 1:

Because of its biggest number of users we decided to deal with it in more details in our research [5]. Facebook mostly speaks to younger people and to have an active Facebook account is almost a social standard of this age categories. In general, the intention to use online social networks is strongly determined by social presence as stated by Cheung, Chiu and Lee (2011). That is the main reason why Facebook has estimated 600 millions of active user accounts. Fifty percent of active users log on to Facebook every day and an average user has about 130 friends in his social network and all of them can catch his presented opinion. The following table shows more statistics on Facebook [1], [10].

Statistics on Facebook and its Average User Characteristics

Facebook Statistics:

Average Facebook user characteristics:

Active users

600 million Friends

Daily active users

300 million Minutes on Facebook per month

Objects (pages, groups, events)

900 million Groups, pages, events

Active applications Mobile users

130 1,400 80

550,000 Pieces of content created per month

90

150 million Pieces of content shared per month

60

Source: Facebook.com, estimated data, [10]

Considering these numbers, it is easy to realize an enormous commercial potential of this virtual place where so many people spend so much time actively. This is the main reason, why Facebook’s market value is currently estimated at 90 billion USD (April 2011), although its shares are not traded publicly yet. Many authors (e.g. Cooke, Buckley, Keller) are very optimistic in case of Facebook’s usage for marketing purposes [4], [14].

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Facebook and web social networks in general are considered a powerful tool for companies to keep in touch with customers and acquire feedback from them. They can also maintain contact with their customers through the fan groups or promote their events. Not only companies, but even products can be promoted or have their fan groups there. On the other hand bad reputation of a company spreads through Facebook even faster. The

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Marketing a obchod same fact is true also for politicians, who discover the power of Facebook campaigns supporting or opposing them [15]. Thanks to the network of its fans a company or a product can get free attention. Also a company can communicate its marketing campaign virtually for free with relatively large number of (current or potential) customers by this approach. Many firms use special groups or events on Facebook to attract customers to special (often time limited) campaigns. Naturally, a Facebook fan page or group should contain campaign specific information excluding pricelists or technical data. This information is located on a company’s web page [1]. It is complicated to measure the accurate efficiency of this type of marketing communication because it is hard to precisely identify its effects. However this communication is for free and it can draw significant attention to the company. The increased awareness leads to potentially higher sales and market share in future, but measuring its particular effect precisely is virtually impossible. Users’ interaction with companies (or brands) through their fan pages or groups provides valuable information and feedback to the company for free. However not all customers’ demands and preferences can be satisfied, because of their often contending character [6]. Facebook groups or fan sites do not replace product or company website, but they can be useful for drawing attention to special events, time restricted campaigns, etc. Many firms often try to attract users to their groups with (often misleading) name or content, not related to company’s activities, (groups like “I will not pay for Facebook by 7th August 2010” and many others). After that they use these groups to reach users by their commercial messages. This is considered unethical and it can frustrate users and have rather negative effect on a company image [20]. On the other hand firms often monitor their reputation on Facebook, mostly by assistance of specialized monitoring firms. These firms acquire statistics, analytical and monitoring data of Facebook groups and provide them to the marketing and PR departments in those firms. The company gains valuable information on its perception by Facebook users due to this monitoring [16]. A very important effect can be achieved in case, when some of potential

customer’s friends likes or recommends a product on his Facebook wall. As a consequence the potential customer can be pushed to a positive buying decision and it basically increases popularity of a product or a company. All the mentioned effects are practically immeasurable precisely. Still companies have to consider every single one of them, while using Facebook for their marketing purposes. Another usage of Facebook’s potential for marketing purposes is direct advertising on it. Although the huge potential of Facebook advertising does not necessarily mean that traditional online banner advertising is over [17]. It should be considered as an additional way of advertising to banners and contextual ads according to some experts’ opinion. Gertz [11] stated that Facebook is fascinating but unpredictable and it is necessary to pay attention, not only to the new trends of web advertising but also to traditional and tested solutions. The older forms of online marketing should be still used and Facebook is the only other and powerful medium, which can be used to advertise and communicate with users and potential customers. Gertz further considers current web banners not “out of fashion” and more mature with good targeting possibilities. Banners have become more interactive, with better quality of graphic or video presentation, what makes them more appealing [4], [23]. Some interactive banners are short games in fact and it is drawing attention to them even more. Generally, quality of presentation increased practically in full-area of banners advertising in comparison with times few years ago [4], [11], [14]. Video advertising on web is also a very common form of online marketing. Users can meet it in various forms, such as Rich media banners (interactive or multimedia banners inc. video), text advertisements or small pop-up banners on video screen showed during playback [24]. Another model of a video advertising is playing a commercial video before demanded video itself or a viral video with commercial content that is spreading around the Web as fast as a virus. Viral video becomes very popular among users because of interesting, entertaining, shocking or surprising content and social networks enabling video sharing are an ideal place for its spreading [1]. A product or a company gets more attention at lower expenses in this case. In addition viral

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Marketing a obchod commercials on the web can affect even those users, who do not watch television and therefore are not reachable by TV commercials. However viral videos do not have commercial content in many cases [1], [4], [19]. Many voices question the ethics of advertising at the place where users have private conversations. This is similar to the criticism of Google selling ads at search results few years ago. Contextual advertising is now a very common form of online advertising and firms often optimize their web pages for search engines to get a higher position in search results [25]. Using Google search, people are searching for some concrete term and top search results (i.e. adverts sold by Google, Google AdWords) related to this term can be still very useful for the user [15]. Opposing to this fact, Facebook is selling commercial space, where people make connections, meet, have private conversations and not necessarily look for some products or services [17]. Probably that is not an ideal situation for addressing them with an advertisement. But the virtual place, where hundreds of million users are present, cannot be unused for advertising or marketing generally. A very interesting question is also the efficiency of online advertising and especially Facebook advertising. Larger companies often have specialized marketing and advertising departments, which continually evaluate their advertising efficiency by various (mostly statistical) methods and therefore have a much better base for the decisions about advertising campaigns. Yet small and medium enterprises (SMEs) would definitely welcome a simple way of comparing various online advertising forms to choose the right one for their next advertising campaign. An ideal measure should reflect the efficiency of advertising in one number to facilitate comparison of advertising forms and campaigns with each other. To propose such a measure and test its expressing power on real data is the main aim of this paper.

1. Advertising on Facebook In the area of a paid advertising on Facebook, measuring of its efficiency is possible and highly important. On Facebook there are commonly used the web advertisements. The significant difference to common web pages is

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that Facebook users manifest their preferences, hobbies and sympathies. All such information is very valuable for marketers to target their marketing campaigns to the specific customer groups or even individuals. Thanks to this information shared on FB, precise advertising targeting is possible, therefore no more tracking cookies are necessary to track browsing habits (i.e. interests) of users, because users are expressing them on the social network publicly [18]. Facebook has been used as an advertising platform soon after its start, through application Facebook Ads. Advertisements on Facebook are subject to an auction, where advertisers compete among themselves for an audition through bidding the price of click on their ad (pay per click) or the price of displaying their ad to selected target group (pay per impression). Advertisers get suggested bid range which currently is winning the auction among similar adverts. The advertiser sets maximum bid (per click or per thousand impressions), but Facebook charges only the amount required for an advert to win an auction. This price may be lower than the maximum bid set by advertiser, making a marketing campaign more efficient. Advertisers have also control of their daily budget – the maximum amount that can be spent on campaign per day. If a daily budget is spent, the advertisement will automatically stop showing until next day of a current campaign [9]. Facebook has wide advertising targeting options, which can improve performance and effectiveness of a company’s online advertising, because ads are displayed to the users who are most likely to be interested in advertised object, thanks to information shared on Facebook users’ profiles. With such a precise targeting company can reach demanded customer group according to its advertising goals. Facebook Ads targeting tool shows the estimated number of users encompassed by firm’s ads, so it is easy to widen or constrain a target group. For an advertisement any number of targeting filters can be set. [9] Targeting filters for Facebook Ads can be divided into the following groups: Location – is based on user’s IP address and profile information about location and target can be specified as country, province, city, or adjustable target radius around specified location.

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Demographics – based on age (specified age range or even birthday), gender, relationship status from user’s profile or preferred language. Education and work – can be based on user’s attendance of a specific school (college and university) or work at particular company, if the information is provided. Interests and likes – targets ads according to information from user profiles, status updates, attended groups and page connections (activities, preferences, favourites – information provided by users themselves). This targeting filter can improve appeal of an advertisement, while ads can be more personalized to a specific customer group. Connections – aimed at users (and/or their friends on Facebook) connected with group, page, application administrated or created by advertiser. This targeting option also gives possibility to exclude advertiser’s connections to address users not connected to an advertised subject yet (to widen range of possible customers) [9]. Precise advertisement targeting to specific users groups is a significant advantage of advertising on Facebook (ads on web social networks respectively) comparable to advantages of contextual advertising on web search portals (like Google Ads). Thanks to information provided by users themselves, it has even more precise targeting options and therefore it can possibly be more efficient. On the other hand Facebook users are not searching for a particular item or a topic on Facebook unlike on search portals. Therefore it is much more complicated to attract their attention to an advertisement, even though it is related with their interests. It still can help increase engagement and product or service awareness, advertisement relevance and can lead to a better efficiency of company’s marketing [3].

2. Online Advertising Efficiency The ability to advertise on Facebook requires the need to measure effectiveness of this online advertising form. For measuring efficiency of online advertising there are used various methods, from simple metrics (like cost per click, cost per impression, click-through rate) to more complex statistical methods (e.g. data envelopment analysis, stochastic frontier modeling) using statistical software.

From complex methods of advertising effectiveness measuring we can mention data envelopment analysis. Data Envelopment Analysis (DEA) is a non-parametric, linear programming based technique designed to measure the relative performance of decision making units (DMUs) where the presence of multiple inputs and outputs poses difficulties for comparisons. DEA uses the ratio of weighted inputs and outputs to produce a single measure of productivity (relative efficiency). Efficient DMUs are those for which no other DMU generates as much or more of each output (with a specific level of inputs) or uses as little or less of each input (with a specific level of outputs). The efficient DMUs have an efficiency score of one (or 100 %), while the inefficient ones have efficiency score less than one but greater than zero in an input oriented DEA model, and more than one (or more than 100 %) in the output oriented model. The efficiency of each unit, therefore, is measured in comparison to all other units. Consequently DEA enables to compare the best performers [22]. Stochastic frontier modeling is a parametric approach of economic modeling which explicitly considers the stochastic properties of the data and distinguishes firm-specific effects and random shocks or statistical noise. But there are some problems with stochastic frontiers, for example the implementation requires the choice of an explicit functional form for the production function, which is not always appropriate, and its user imposes strong distributional assumptions on the error term. Nevertheless, the stochastic frontier production function is a significant contribution to the econometric modeling of production and the estimation of efficiency. It is usable also to express efficiency of advertising and can be used with DEA, because they do not always produce similar results. This happens because DEA is quite flexible but stochastic frontier modeling assumes an inflexible functional form [22]. The characteristics of DEA and stochastic frontier modeling shows, that they are powerful methods of efficiency measurement, but they are also significantly complicated for SMEs to administer them by themselves. For purposes of simple advertising efficiency measuring in conditions of average SME, more simple methods are suitable. Facebook has a variety of simple advertising performance

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Marketing a obchod monitoring tools, which provide the advertiser a basic report in the form of a table, a data chart or a graph about: Standard metrics – impressions (the number of times the ad is shown), clicks (the number of times the advertisement is clicked), efficiency of impressions (average impressions per click – CTR), average cost per click (CPC), sum spent on particular campaign, etc. Profile metrics – interests, favourites, preferences of users clicking on a given advert. Demographic metrics – age, gender, location of users clicking on particular advert. Conversion metrics – allows to track traffic on company’s website resulting from Facebook advertisement (unique Facebook generated tracking tag must be added to company’s website code). Small and medium sized enterprises often do not have capacities and abilities to evaluate efficiency by complex methods, but simple metrics provides only an incomplete overview of online advertising campaign showing only partial efficiency of an advert [7], [22].

2.1 Facebook Advertising Efficiency Measuring Because of above stated reasons, we decided to design a composite index to measure the Facebook ads efficiency in a more complex way and to enable an easy comparison. First, we considered the following three measures to be part of the proposed composite index: Click per impression (CTR – click through rate) expresses the number of clicks per one impression of advertisement. This rate can identify the quality of advert, i.e. the attractiveness of the advert to the target group in desired degree. CTR expresses marketing quality of the ad (clarity of ad statement, text appeal on users, attractiveness of design, placement, etc.). The higher value it has, the advert is more attractive and therefore users click through it more often. Facebook Ads reporting tools monitor this measure as a part of standard metrics, so the advertiser always has information about click per impression at his disposal [9]. Cost per click (CPC) shows average cost of one click on particular advert achieved during campaign. Because of the auction pricing of

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advertising space on Facebook, the cost of each click can differ in time and CPC is calculated as the weighted average of individual costs per click. Lower CPC means more efficient advertisement. This measure can show to the advertiser the costs of increasing traffic on firm’s page (or application, event, group) and also it is showed by Facebook Ads reporting tools directly. An increased traffic means higher awareness of firm and it brings higher chance to sell the firm’s product or service as well. An alternative to CPC is CPM (cost per mille), which measures costs per thousand impressions [9]. Return on Investment (ROI) represents revenue generated by Facebook advert specifically, in comparison with amount spent on given advertisement. A particular ad performs better, if it has higher return on investment. However measuring the ROI of online advertising is a complicated issue. The main problem of ROI usage is to determine the revenue generated by the ad itself, purged of other effects not directly connected to specific advertisement. Obviously there are many more revenueinfluencing factors than advertising campaigns (e.g. attractiveness of product, price of product, product reputation etc.) [9]. Moreover, the Facebook advertising affects in-store sales (not only online sales) by increasing awareness of potential customers. The increase in sales during a single ad Facebook campaign does not necessarily mean that this increase is generated by that ad and vice-versa. To specify the effect of a campaign, it would be necessary to get feedback from every single customer about his buying decision, if it was rooted in the advertisement on Facebook or resulting from other firm’s actions. Only then it is possible to measure effects on firm’s sales precisely, it makes ROI hard to be used for purposes of measuring the efficiency of Facebook advertisement campaigns. Acquiring feedback from all customers is expensive, time consuming and ineffective in most cases. Therefore we decided not to include ROI to proposed composite index of Facebook advertising efficiency. The effect of Facebook advert on sales can be estimated by a firm on certain level of precision. This estimation of the measure of increased sales can be carried out also by comparing status of sales volume before Facebook campaign during and after it.

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2.2 Efficiency Index Proposition To utilize simple efficiency comparison between ads or advertising forms, we decided to form a proposed composite efficiency index (CEI) from click through rate (CTR – clicks per impression) and cost per click rate (CPC). Its proposed composition is as follows: clicks clicks CEI = –––––––––– . ––––––– impression cost

(1)

This can be transformed to: clicks2 CEI = ––––––––––––––––– impression . cost

(2)

If we consider the following equation: clicks –––––––––– = CPC–1 cost

(3)

Then we can transcript the first equation into the simplest form: clicks through rate CTR CEI = ––––––––––––––– = ––––– cost per click CPC

(4)

It follows that the higher number of clicks per impression (CTR) means better quality of advertisement. The lower costs necessary for one click (CPC) also mean a more effective advertisement. The ratio of the number of clicks and costs (reverse value of cost per click) shows how many clicks the advertisement gets per one currency unit. Its higher value means better ad’s performance. We composed these two basic indicators into the one simple coefficient (4), which in a single number reflects how the advertisement is performing (how many click-throughs at particular expensiveness). It gives us a good basis to compare various forms of online marketing or single advertisement campaigns with each other. So the proposed composite efficiency index (CEI) is constituted from a ratio of click-through rate and cost per click. The lowest efficiency represents zerovalue of CEI. This situation is possible only when the ad gets zero clicks (no one will click on ad) and non-zero costs on this ad campaign, and therefore this ad is totally ineffective. The lower number of impressions necessary per a click and lower costs on a campaign (while achieving campaign goals) mean better

efficiency. Although, it is not necessary to restrict number of impressions, if a campaign is paid per a click, not per impressions.

3. Facebook Advertising on the Slovak Market To test an expressing power of our proposed composite index, we decided to test it on Slovak Facebook advertising market. We acquired these data from our own electronic survey. This survey was aimed to acquire numeric data on firms’ campaigns on Facebook, their experience with online marketing in various forms and personal perception of efficiency and usefulness of these campaigns. The survey was realized by the electronic questionnaire. It was addressed to 117 companies operating on the Slovak market and advertising their products or services on Facebook. This sample represents over 90 % of all Slovak companies, whose advertisements were shown at sponsored advertising area on author’s Facebook profile during one month (March 2011). The following companies’ expectations and preferences were revealed during the survey. Approximately two thirds of responding firms were retailers, the rest consisted from firms providing services. 77 percent of responding firms were from segment of small and medium enterprises, the rest of companies were large enterprises. The average number of company’s Facebook advertising campaigns already accomplished was almost 3. This shows that Slovak companies have already some experience with Facebook advertising. The most frequent goals of Facebook advertising campaigns were increasing company’s website visit rate (78 %), sales boosting (68 %), improving client awareness about trademark or company (56 %) or launching a new product or a service (12 %). Over 44 percent of responding companies achieved their campaign goals and 45 percent only partially. Only 11 percent of respondents did not achieve their intended campaign goals at all. These results show firms’ high expectations from Facebook advert campaigns, which are hard to achieve in the praxis. Companies with previous experience with Facebook ads had more realistic estimates and achieved their goals more frequently (in 86 % of campaigns).

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Marketing a obchod We also investigated, if firms use Facebook to keep in touch with their clients and gather feedback from them through Facebook groups, profiles or fan pages. As high as 78 percent of firms stated that they communicate with their clients through the Facebook fan page, 32 percent through user group. Only 12 percent of firms have their own company profile used for these purposes. On the other hand, only 11 % of companies adduced that they do not use Facebook for such a communication. The firms stated their opinion about key aspects of Facebook advertising efficiency and most of them (79 %) adduced the attractiveness of advertisements’ text and picture. Over two thirds (67 %) of respondents consider precise targeting as a key aspect of campaign’s efficiency, 58 percent stated qualities of products or services and over 56 percent good campaign timing. Moreover 11 percent of responding firms consider Facebook ads more efficient than other forms of online marketing and 55 percent adduced comparable efficiency as other forms of online marketing. The rest of respondents perceive Facebook ads as less effective than other online marketing forms. Most of responding firms uses also the other forms of online marketing. Over 65 percent of them use contextual ads, 45 percent classic or interactive banners, 22 percent use commercial online videos. About 34 percent of respondents adduced that they do not use any other online marketing forms than Facebook advertisements. The responding firms also answered the question, if they prefer advertising on Facebook over other online advertising forms. Slightly over one third of respondents prefer it. They adduce as the main reasons following reasons for this preference: easy and precise targeting, simple campaign managing and often a lower price in comparison to the other possibilities. Almost two thirds use advertising on Facebook as a complement to any other web advertising and do not prefer it. Slovak advertising market on Facebook has its specifics because most of active users are younger than average of local population and therefore it is a more valuable communication and marketing channel for companies providing products for this target group (e.g. online games, sporting goods etc.). In comparison with more traditional forms of advertising (like prints, TV and radio commercials, billboards etc.), Facebook advertising is

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perceived even more positively. Over 83 % of responding firms adduced that they prefer Facebook advertising to traditional advertising. This can be caused by the fact that the responding firms were aiming their production mostly at online customers. As the main reasons for their preference they stated perceived lower prices, broader reach of customers, easy campaign management and better possibilities of customers’ feedback. Respondents expect from Facebook advertising (or advertising on web social networks) the following positive effects in general: Increased number of clicks; Higher market share; Increase in sales revenue; Higher customer retention; Positive change in awareness of brand; Desired change in buying intents. In this survey we also acquired numerical data about companies’ advertisements and advertising campaigns.

3.1 Efficiency Measurement and Comparison by Proposed Index The main reason for committing this survey was to acquire numerical data about Facebook advertising campaigns of Slovak companies. The main acquired average data are presented in the following Tab. 2. This table shows the basic summary of our survey numerical output. Very interesting is the high number of impressions of a single ad campaign (3,053,748 impressions), suggesting that Facebook ads can have a very wide reach of potential customers and firms used pay per click payment model and therefore any number of impressions was for free. Also multiple impressions to a single user are counted by companies’ Facebook Ads statistics. On the other hand it also means that the responding firms did not use very accurate targeting of their campaigns in many cases. With average number of clicks at 1,523 it represented average quite low click-through rate at 0.0005. The companies spent around average of 452 EUR on their single campaign in average and recorded estimated 22.63 % increase in sales of an advertised product. This represents quite a huge increase in sales, but it is caused by high number of small and medium enterprises (SMEs) among responding companies. By

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Marketing a obchod Tab. 2:

Average Data About Facebook Advertising Campaigns of Slovak Companies Average data from Slovak market survey Average number of impressions Average number of clicks

3,053,748 1,523

Average campaign costs

452

Average estimated increase in sales

22.63 %

Average cost per click

0.29678

Average click through rate

0.00050

Average composite efficiency index

0.00168

Source: Own survey on Facebook advertisements usage in Slovakia

SMEs relatively small total increase in sales can represent high percentual change, because of small scale of their production. Average from recorded costs per click was at level of 0.29678 EUR, which shows low expensiveness of Facebook advertising. These low costs of ads on Slovak Facebook ads market show that competition between advertisers has not pushed prices very high, yet. Also a relatively small number of responding firms (117) included in our survey implies from low scale of Tab. 3:

a market and its lag behind the expansion on the world’s Facebook advertising market. Finally we expressed our composite efficiency index, which was at average value of 0.00168. To get a better idea of Facebook ads efficiency we shall compare it to banner advertising and contextual advertising as the main types of online advertising. The following table Tab. 3 shows comparison by CPC, CTR and proposed composite efficiency index – CEI.

Basic Comparison of Online Advertising Forms' Efficiency

Efficiency Comparison

Facebook ads

Contextual ads

Banners

Estimated cost per click

0.30

1.05

0.87

Estimated click through rate

0.00050

0.0011

0.00102

Estimated composite efficiency index

0.00168

0.00105

0.00117

Source: Own survey, [8], [10], [12], [13].

Estimated data on contextual ads have been acquired from Google statistics, Hochman consultants and from the company eTarget (Slovak leader in contextual advertising) and they are averaged. Data on banner advertising are from Google’s DoubleClick Benchmarks Research report and they are estimated from EMEA countries’ data, while data on the Slovak market are not stated in this report directly. This comparison shows, that the Facebook ads are (according to our results) significantly more efficient than a contextual advertising and a banner advertising on the Slovak market. Facebook ads have the lowest CTR, but also very low CPC which makes them more efficient.

However this comparison is only roughly accurate, because of small sample of responding companies in our survey (117). Also the characteristics of each single online advertising form can distort this comparison. For example banners’ efficiency is very dependent on banner’s placement, size and if the particular banner is a static picture or Rich media banner, etc. Also banners do not necessarily aim to increase online traffic on company’s page or online sales, but it can still have a positive effect on brick-and-mortar shop sales. Contextual ads have the best overall click-through rate, but their prices increased in the last few years to unprecedented heights. This fact makes contextual advertising the most

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Marketing a obchod expensive online advertising form and also decreases its efficiency. Still we can say that Facebook ads are an efficient form of advertising with excellent targeting options and a wide range of potential addressed clients. It is suitable mostly for companies which have their clients online and using social networking abilities actively. Companies, which have some experience with Facebook ads, consider them efficient and use this advertising form repeatedly. To get a better idea of Facebook advertising efficiency, it is suitable to compare it with other online advertising forms on numerical data (not only if it is perceived efficient by its users). According to Google’s DoubleClick Benchmark Research report click-through rate (CTR) of static and video banner advertisements in EMEA countries was between 0.05 % and 0.18 % with average of 0.102 % (Slovak banner market was not part of this research directly, average was considered for the Slovak market). Data on average costs per click were estimated at 0.87 EUR according to this source. The final value of composite efficiency index for a general banner advertisement on the Slovak online advertising market is 0.00117 [12]. Average click-through rate of contextual advertisement was estimated at value 0.0011 and average costs per click on Slovak market were estimated to 1.05 EUR, both according to data from eTarget and Google, which are top contextual advertising providers on the Slovak market. Resultant composite efficiency index’s value from these data is at value of 0.00105. For a better comparison overview Tab. 3 contents also data on Facebook advertising from Tab. 2 [8], [13]. By a simple comparison of CEIs of all three advertising forms we get a flash view of their estimated efficiency. The best result has Facebook advertising (the highest CEI=0.00168), mostly because of very low costs per click. Low costs per click are currently the biggest advantage of Facebook ads and are caused by lower popularity of Facebook advertising on the Slovak market. With increasing competition within this advertising sector, the increase of advertising costs is expected, as it happened in the area of contextual advertising in recent years. This will lead to decrease in its efficiency, but until that time Facebook advertising is very effective in Slovak conditions. However these

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results would probably differ significantly from more developed online marketing markets. Results also show that Facebook advertisements have high efficiency and moreover Facebook provides higher number of impressions often. It can be caused by more frequent visits of their Facebook wall than any other website by average user (see Tab. 1: Statistics on Facebook and its Average User Characteristics). High number of impressions can have a significant effect on non-online sales of companies, simply by building brand awareness.

Discussion and Conclusion Facebook is considered a good tool for keeping in touch with customers, acquiring feedback from them, reaching online customers and advertising with great possibilities of targeting. In terms of efficiency, we can often encounter the opinion of contextual advertising being (e.g. Google Ads, AdWords) more efficient than Facebook Ads. This is probably caused by the fact that users on Google are more serious about their intentions and are actually looking for purchasing something. Our survey on the Slovak online advertising market proved opposite results, but it does not deny this opinion undoubtedly, because of survey’s low scale and localization only on the Slovak market. Important conclusion is the fact, that Facebook can be a powerful marketing tool for reaching customers, especially young online population. Our conclusions are partly Facebook-specific, but some of them (very good targeting options, easy feedback collection, great reach of young online customers etc.) are applicable on other web social networks in general. We have provided a very simple comparison of Slovak online advertising market by one index thanks to proposed index (CEI). However there is still the possibility of measuring deeper qualities of ads (than CTR, CPC) and comparing them with possibly different outcome. Interesting results could be also obtained by similar surveys on other online advertising markets abroad and their comparison through final CEI values. The topic for further development of composite efficiency index is its enhancement with measures of revenues generation by advertising costs (like ROI). Although it is confronted with the

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Marketing a obchod problem of precise specification of direct advertising effects on total revenues of company [22]. Moreover, it would complicate the usage of CEI, which is in opposite with call for the simplest efficiency measuring and evaluating available for SMEs. For comparison with more sophisticated efficiency measurement methods, more precise and detailed data from advertiser are necessary. Although such a comparison would show true expressing power of proposed composite efficiency index, but it is beyond the scope of this paper. This form of online advertising is increasing but the whole Facebook’s future can be endangered by a few issues. The biggest issue of Facebook is privacy and security especially private information thefts, are threat to Facebook’s future [1]. Many applications created and used on Facebook are malicious and designed to install harmful software to user’s computer. Now developers of applications must have a verified account to provide new applications on Facebook. The Facebook project manager Niket Biswas stated, that this step will help connect those malicious applications with real user’s account and take legal steps against this user. According to the opinion of many security experts this will not stop cybercriminals, because even the verified account can be faked. Some say that Facebook should be inspired by Apple App Store, where all software is validated by commission with strict rules, before its publication to users [21]. This will lead also to great restriction of application numbers but security of Facebook should increase dramatically. Many of users fear that their personal and private information from Facebook profile can be provided to third parties. Another reproach of users is that Facebook does not delete inactive user accounts and users have to find deep in options a command to delete it physically from Facebook’s databases. The social network keeps information about its users often without their knowledge and it is not very keen to delete it on users’ demand [20]. This information can be very valuable for targeted marketing of companies and Facebook is rumored to provide it to those companies at a significant price. Another security issue is user’s information abuse by other users. Therefore the basic rule for Facebook safe usage should be: “Do not input

any private or potentially sensitive information to Facebook.” Facebook shows that it can be an effective advertising medium or platform, but its future can be uncertain as well. Some experts (e.g. Lovink, Geertz and others) say that it could become saturated by users within next few years and then slowly abandoned and forgotten by its users. For example MySpace is recording a downfall in numbers of users in recent years, although thanks to their migration to Facebook [26]. But now the number of Facebook users is still growing and therefore its marketing and advertising potential is increasing, too.

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Marketing a obchod [9] Facebook. Guide to Facebook Ads [online]. Palo Alto, Facebook, 2011 [cit. 2011-04-03]. Available from: . [10] Facebook. Statistics [online]. Palo Alto, Facebook, 2011 [cit. 2011-04-07]. Available from: . [11] GERTZ, O. Facebook opakuje históriu Googlu. TREND Conference: Internet and Advertising. [online]. Bratislava, TREND, 2010 [cit. 2011-04-07]. Available from: . [12] Google. 2009 Year-in-Review Benchmarks: A DoubleClick Report by Google [online]. Mountain View, California, 2010 [cit. 2011-04-21]. Available from: . [13] HOCHMAN, J. The historical cost of pay-perclick (PPC) advertising. [online]. Cheshire, Hochman Consultants, 2010 [cit. 2011-05-15], 20 p. (PDF). Available from: . [14] KELLER, E. Unleashing the power of word of mouth: Creating brand advocacy to drive growth. Journal of Advertising Research. 2007, Vol. 47, Iss. 4, pp. 448-452. ISSN 0021-8499. [15] KNIGHT, K. What’s in a social network? Plenty, say marketers, politicians [online]. BizReport, Hellerup, Denmark, 2011 [cit. 2011-06-15]. Available from: . [16] KOâIâKA, P.; PANCZAKOVÁ, Z. KdyÏ vám nafackují pfiátelé. Ekonom [online]. Praha: Ekonomia, 2010-11-04 [cit. 2011-04-16]. Available from: . ISSN 1213-7693. [17] MADLE≈ÁK, R.; ·VADLENKA, L. Akceptace internetové reklamy uÏivateli v âeské republice. E+M Ekonomie a Management. 2009, Vol. 12, Iss. 1, pp. 98-107. ISSN 1212-3609. [18] MCGEVERAN, W. Disclosure, endorsement, and identity in Social marketing. University of Illinois

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Law Review. 2009, Vol. 2009, Iss. 4, pp. 11051166. ISSN 0276-9948. [19] MEI, T.; HUA, X.-S.; YANG, L.; LI, S. VideoSense: Towards effective online video advertising. Proceedings of the ACM International Multimedia Conference and Exhibition. Augsburg, 2007, pp.1075-1084. ISBN 978-159593702-5. [20] MORGANSTERN, A. Comment: In the spotlight: Social network advertising and the right of publicity. Intellectual Property Law Bulletin. 2008, Vol. 12, Iss. 2, pp. 183-184. ISSN 1554-9607. [21] NOSKA, M. Facebook i pfies nová bezpeãnostní opatfiení zÛstává nebezpeãn˘. Computerworld. 2010, Vol.10/2010. ISSN 1210-9924. [22] PERGELOVA, A.; PRIOR, D.; RIALP, J. Assessing advertising efficiency. Journal of Advertising. 2010, Vol. 39, Iss. 3, pp. 39-54, ISSN 0091-3367. [23] ROBINSON, H.; WYSOCKA, A.; HAND, C. Internet advertising effectiveness – The effect of design on click-through rates for banner ads. International Journal of Advertising. 2007, Vol. 26, Iss. 4, pp. 527-541. ISSN 0265-0487. [24] ROSENKRANS, G. Future online rich media advertising. Proceedings of the 2008 International Conference on Internet Computing, ICOMP 2008, 2008, pp. 349-353. ISBN 1601320736. [25] SUCHÁNEK, P. The Fundamentals of Prosperous E-shop in Connection of Search Engine Optimization. E+M Ekonomie a Management. 2010, Vol. 13, Iss. 2, pp. 92-103. ISSN 1212-3609. [26] VOZÁROVÁ, E. Preão Facebook nepreÏije [online]. eTrend, Bratislava, 2010 [cit. 2011-04-28]. Available from: .

Ing. Martin Vejaãka, PhD. Technická univerzita Ko‰ice Ekonomická fakulta Katedra aplikovanej matematiky a hospodárskej informatiky [email protected]


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Abstract FACEBOOK ADVERTISING AND ITS EFFICIENCY ON THE SLOVAK MARKET Martin Vejaãka The main aim of this paper is to propose a measure of Facebook advertising efficiency. In general marketing role of Facebook is considered with special attention to the advertising. Advertising on Facebook is briefly introduced with possibilities of advertising targeting and its performance monitoring metrics. The current methods of efficiency measuring of online advertisement from the area of econometric modeling (specifically data envelopment analysis and stochastic frontier analysis) are mentioned and their suitability for use by small and medium enterprises is questioned. The composite efficiency index is proposed to measure online advertising efficiency and to give base for a comparison of online advertising campaigns. It is based on simple measures like clickthrough rate and costs per click, to assure its simple usage and easy result comparison in conditions of small and medium enterprises. Proposed composite efficiency index is tested on sample data from the Slovak Facebook advertising market acquired by our own survey. The efficiency of Facebook advertising campaigns of Slovak companies, which supported our research and provided their data about particular advertising campaign, is measured by composite efficiency index and then compared with estimated data on other online forms of advertising in conditions of Slovak online advertising market. Results show higher Facebook advertising efficiency than efficiency of banner and contextual advertising in Slovakia. Also preferences and expectations about Facebook advertising are investigated by the survey. The highly positive attitude towards advertising on Facebook of Slovak companies was detected. Possible threats to the future of Facebook advertising and Facebook itself are indicated. In discussion are included topics for further research in this area. The main conclusion is the fact that Facebook can be powerful and effective marketing tool for reaching online population. Key Words: social network, Facebook, web advertising, advertising efficiency, marketing potential. JEL Classification: M31, M37.

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Informaãní management

IDENTIFICATION OF DATA CONTENT BASED ON MEASUREMENT OF QUALITY OF PERFORMANCE Stanislava ·imonová Introduction Increasing the quality of production belongs to management goals of every single business, whether production means products or services providing. Better quality of production results in, for example, gaining competitive advantage on the market, effective territorial management, returning customer, and citizen satisfied with public administration services and others. Management drives for better quality and resulting profitability in financial and nonfinancial measures. Management of business processes requires supporting business data; business processes and business data form a linked unit, because business processes need relevant data for work and business data should fully serve to business processes. Suitable information environment is, therefore, a necessary condition for successful existence of an organization. Change of business information environment, thus changes in information systems, is difficult but necessary. Difficulty arises from the complexity of system changes development as such and financial costs associated with them. Necessity arises from changes in business processes. These business processes changes are caused by efforts to achieve better organization performance and also changes in business processes result from the reaction of the organization to external and internal influences. Internal effects result from adaptation on given business process which means that workers have fully adopted the process and they are finding ways to, for instance, speed-up activities; reduce delays between consecutive activities or another process improvement; outer effects are caused by changes in requirements from customers’ side, changes in suppliers’

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attitude, changes in legislation and others; all of these changes have to lead to improvement of the process, thus to change in process as a reaction to requirements and effects stated. Questions are - To what extent the information environment of the firm supports performance of business processes? How fast is the information environment of firms able to respond to changes in business processes? Is the information environment able to assist companies to achieve better quality and better performance of business processes?

1. Evaluation of Business / Organization Performance A significant part of process management is the evaluation of business /organization output. Application of methods for monitoring organization output and for production quality assessment is considered a necessity if an organization wants to reach good output in long term. Methods of evaluation of quality and efficiency have found their place in both private and public sector. It is often such time sequence when a certain management method is used first in a production organization and then it is used in a non-production organization as a method proven in practice. Even though there are differences between managements of both sectors, basic principles of management activities are the same in both sectors. That is why the same or very similar models and methods can be used for process management and for evaluation of processes output in both sectors. Models of success and models of exceptionality belong among frequently used methods. They are based on evaluation according to determined criteria and are often connected to

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Informaãní management awarding the best organizations in given branch. Individual criteria of models should represent important areas which lead to prosperity of organizations. One of these models is Excellence Model (EFQM) [6] and model Common Assessment Framework (CAF) for improving an organization through selfassessment [7]. CAF model is derived from model EFQM and is transformed to specific public administration needs. Both models have the same basic principles which include [21]: Process and system approach: process approach is considered to be the base of success, because all organization activities are realized in processes; and in processes the added value is created. Process management is not only about measuring and monitoring of certain measurable parameters; it also includes constant evaluation of processes and their improvement. Decision-making based on facts: in order to make decision efficient it is necessary to have needed information which has to be found, verified for validity, analyzed and then solutions have to be designed with the help of the information. Fundamental roles in this process are played by information technologies and business information systems. The methods and their principles have different application in, e.g. development of software for quality evaluation based on EFQM [10]. Among other methods of managing and improving quality we can name methods Six Sigma and Kaizen. Both these methods are focused mainly on economic organizations and again they follow the principles of EFQM. Kaizen is approach of continuous improvements; it is about continuous flow of little / partial improvements at all organization levels [11], [23]. The Six Sigma method is focused on the improvement of quality with emphasis on elimination of defects. The fundamental idea of this method is the effort to a perfect production [14], [16], [17], i.e. avoid mistakes. Mistake is comprehended as any discrepancy with customer’s wish; or simply any case when the customer is dissatisfied (external or internal customer). Based on customer’s requests on product and organization requests we can define quality criteria Critical to Quality (CTQ).

Six Sigma can be defined as a methodology to manage process variations that cause defects, defined as unacceptable deviation from the mean or target; and to systematically work towards managing variation to eliminate those defects [16]. The method uses the normal distribution curve that describes how probability is distributed; Sigma is determined as standard deviation. Principle of the method is illustrated in figure 1. The base of it are limits which are determined based on customer’s requests – upper specification limit (USL) and lower specification limit (LSL); the area inside limits represents suitable production / output, the area outside limits represents unsuitable (defective) outputs, mean value µ represents ideal production without deviations. In the initial situation A performance of process and compliance of limits proceeds in such a way that USL (upper specification limit) and LSL (lower specification limit) have distance of 3 sigma from mean value µ. If there is ideal improvement in the organization, then the organization transforms production conditions to situation B (output of process and limit compliance proceeds in such way that USL and LSL have a distance of 6 sigma). Model Six Sigma considers also circumstance when mean value is in real situation shifted by +- 1.5 sigma (in the picture it is represented by situation C). Fig. 1:

Principles of Six Sigma Approach

Source: [14], own adaptation

In accordance with the Six Sigma method, defects are monitored and are the base for calculation of DPMO (Defects Per Million

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Informaãní management Opportunities); the value DPMO is the base for determination of sigma level by means of the conversion table. Methods recommend using subjective (soft) and objective (hard, measurable) indicators, of course, with accent on objective indicators and objective evaluation processes. The choice of appropriate indicators for measuring and evaluation of relevant business data is a key factor for measurement of quality and performance of business process and, therefore, it forms the basis for process improvement.

2. Relevance of Business Data Information systems are fully intended to support business processes. In order to gain this functionality, two aspects are monitored, the content and the security of information system [18]. Different approaches or frameworks of business information management aim to contribute to solving this topic. Business Intelligence approach is an umbrella term that combines architectures, tools, databases, analytical tools, applications, and methodologies. Its major objective is to enable interactive access to data, to enable manipulation of data, and to give business managers and analysts the ability to conduct appropriate analysis. By analyzing historical and current data, situations, and performances, decision makers get valuable insights that enable them to make more informed and better decisions. The process of business intelligence is based on the transformation of data to information, then to decisions, and finally to actions [13], [19]. The approach of Competitive intelligence is the art of defining, gathering, analyzing, and distributing intelligence about products, customers, competitors, individuals, concepts, information, ideas or data needed to support executives and managers in making strategic decisions for an organization; includes a broad array from government intelligence to market intelligence to business intelligence; the purpose is to focus not only on business information [2], [3]. The approach of Computer Intelligence expresses the topic of quality computing platforms; this is the application of software tools to support decision making and management, scientific procedures that apply computational intelligence [1].

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Managing of company informatics can be performed with the support of a certain framework or model; Information Technology Infrastructure Library (ITIL) and COBIT belong among standards. ITIL is a framework mostly for company IT management, and provides it with instructions, templates, diagrams and other proven methods for IT services managing [4], [9], [20]. The ITIL framework consists of recommendations, proven sequences, templates and manuals within interest areas as IT strategy, services proposals, services operations and continuous improvement of IT services. COBIT is a framework and supporting tool set that allows managers to bridge the gap with respect to control requirements, technical issues and business risks, and communicate that level of control to stakeholders [8]; COBIT enables the development of clear policies and good practice for IT control throughout enterprises. These approaches and frameworks are particularly concerned with data manipulation (the aim is effective data manipulation in order to business processes) and solve mostly management of IT processes in order to support business processes and meeting business objectives. It is obviously important, because the quality of information system is given by its contribution to performance and effectiveness of company processes, activities and particular users [12], [22]. The quality of information system is, therefore, perceived in wider context because it is important to which extent the business processes and business goals are supported by data and performance of concrete application. Business processes need support of an information system, which means support of IT applications with relevant data. The information systems are most often realised by means of database software. It is above all a case of transaction database systems that are designed for work with organisation’s operative data. Data model is in business practice almost exclusively implemented using the relational data model; the basic construct is a relation. Scheme for the relation titled R can be expressed as [5]: R (A1:D1, A2:D2,…, An:Dn) (1) where Di = dom(Ai), for i ∈<1,n>; A means an attribute; D means domain of attribute.

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Informaãní management Fig. 2:

Procedure for Change of the Relational Data Model in the Context of Process Changes

Source: own adaptation

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Informaãní management A domain describes the set of possible values for a given attribute, and can be considered a constraint on the value of the attribute. Domains and other integrity constrains (data types, correctness conditions, etc.) are an integral part of the relational model. Scheme for the relational model can be expresses as [15]: R = { (R1:I1),…, (RK:IK); I}

(2)

where Ri means the relation, Ii means the integrity constraint for i ∈<1,n>; I means global integrity constraints for all relationships among relations. Development of e relational data model is based on the transformation of the analytical models in the process of data modelling. Changing the relational data model, respectively change in the information system is initiated by two situations (events): Changes in the business process: essential change of process flow or partial change of process activities; Request to change the hardware / software: it is the innovation of technology, use of new ICT opportunities. Procedure for change of the relational data model in the context of process changes is expressed in Figure 2. The model captures major activities in connection with the identification of relevant business data: Identification of activities supported by the information system: selected activities expressed in a process map have to be supported by information system; Identification and characterization of integrity constraints: data integrity, domain integrity, referential integrity etc., these constraints are important and essential input for creating a relational data model. This procedure (see Figure 2), however, is not related to the continuous process improvement, respectively it is not related to the measurement of process performance quality in order to constantly improve the process.

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3. Identification of Data Content Based on Measurement of Quality of Performance To measure the quality of production performance is important to select the measured indicators appropriately and also to monitor and evaluate them appropriately. For monitoring it is necessary to take into account the characteristics of each instance of process. Variability among process instances: most instances within one process proceed in usual way, but some instances have unusual course, which occurs rarely; these seldom instances then distort total results. Various periodicities in repeating of instances: character of the process can be such that its instances are performed regularly and distributed across the year. Measuring of length i.e. of 6 months then fully describes conditions during the whole year. The instances of the other process are performed unevenly during the year, i.e. in some regular periods of time there are plenty of instances and another time period there are only a few instances. Measuring randomly may fall into a period with few instances, which – again – distorts the results.

Other characteristics related to indicators: Correct structure of indicators: it is vital to find as many indicators as possible and such indicators that their evaluation would have predicative ability – so that the evaluation really reflects the quality or defectiveness of production. Objective and subjective indicators: the main question is when and to what extend it is suitable to use subjective indicators, or whether it is more suitable to focus on objective indicators. Precision of recorded values of indicators: The indicators can be monitored systematically (using appropriate technology) or unsystematically (personal recording values). Personal recording represents the risk that the indicators are not consistently recorded and the workers interpret them later with a different meaning.

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Informaãní management Fig. 3:

Context Model of the Functionality – Identification of Data Content


It is a complex task to find suitable indicators, monitor them and evaluate them. Principle basis of Six Sigma methods appears to be suitable for use both - in manufacturing organizations, and in services (e.g. organization of public administration). Monitored characteristics are defect opportunities as well as defects themselves. Defect (mistake) is comprehended as any discrepancy with customer’s wish; or simply any case when the customer is dissatisfied (external or internal customer). Method is based on mathematical statistical apparatus. Calculations according to the Six Sigma method: Defects Per Unit DPU = Total Number of Defects/Total number of Product Units

(3)

Defects Per Opportunity DPO = Total Number of Defects x TO

(4)

where Total Opportunities TO = Total number of Product Units x Opportunities

(5)

Defects Per Million Opportunities DPMO = DPO x 1.000.000

(6)

The DPMO value is, according to conversion table, converted to sigma expression / level. The value of sigma level is the starting point for improving of the process and in this way for obtaining of higher value of six sigma level. Selection of appropriate indicators is emphasized; it is suitable to find maximum

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Informaãní management number of locations which could be monitored as defect opportunities. Monitored and evaluated values of the indicators tell us about the quality of individual performance, given work activities. There may be two situations: The indicators provide information about the quality of performance of work activities within which the defects were measured. The indicators provide information about the quality of the performance of another work activity, so product unit is formed in a given work activity and values for assessing the quality of this product unit are measured in another work activity (activities). As a system solution it appears that the indicators should to be involved into business information systems. Therefore, determination of user requirements (by means of data content identification) has to precede the analysis and development of the relational data model. Context for the idea of identification of data content is shown in Figure 3. It is necessary to take into account the characteristics: Data content must be identified for each business activity (within the process) that is to be supported by information system module. More participants are involved in identifying data content of the functionality: – Actor of the activity characterizes demands on the data for given IS module in terms of performance of his/her profession; – Process manager characterizes demands on the data for that IS module in terms of management of the process (modelled work activity occurs in this process); – Quality manager characterizes demands on the data for that IS module in terms of quality management of this process and others processes (it means other work activities). Output is defined by a set of integrity requirements, which includes data requirements in connection with the performance of the process activities and data requirements in relation to the measurement and evaluation of quality of performance of the process (i.e. the quality of given business activity and quality of other activities); Purpose of data content identification is to identify the requirements that become an

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input for the development of an information system, respectively for the development of the module information system; information system (designed this way) will contain not only the data needed to support given work activity, but also the data necessary for assessing the quality of the performance of given work activities and other work activities. How to identify the data content is expressed in Figure 4. Identification of data content consists of identifying three groups of data functionalities that are associated with analyzed work activity and thus also with analyzed module of information system: Determination of data needs for the actual performance of work activity in accordance with a process map of the process; Determination of data needs for defects measuring and for measuring of defect opportunities (indicators are based on the principles of Six Sigma method) in order to measure the quality of performance of the given work activity; i.e. data arise in connection with that activity and is used to measure quality of performance of the same work activity; Determination of data needs for defects measuring and for measuring of defect opportunities (indicators are based on the principles of Six Sigma method) in order to measure the quality of performance of another work activity; i.e. data arise in connection with that activity and is used to measure quality of performance of another/different work activity. Various modelling techniques can be used for formal expression of data content. The technique determines how to reach the needed results; variation of decision making in different situations and what arises from it, it defines the sphere of force etc. As modelling techniques can be used the 'top-down decomposition’ technique for the decomposition of the problem area to lower – more detailed levels, the ‘downtop composition’ technique for composition of the details to the higher superior units; the ‘functional follow up’ technique for expressing the partial elements which follow functionally to each other, the ‘sequence’ technique for expression the sequence and follow up the

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Informaãní management elements in time, the ‘elements communication’ technique for a closer identification of the parts and their integration in the unit, the ‘global delimitation of the unit’ technique for the delimitation of the all important features which influence the element and are also necessary for realization of the element, the ‘detailed delimitation of the content’ technique for detailed description of the inner parts of given Fig. 4:

element, the ‘delimitation of the functionality’ technique to specify the characteristics of the asked functionalities, the ‘watching the data flow’ technique for watching the data flows within the given problem area, the ‘watching the event influencing the problem area’ technique for identification important events and their influences to the elements of the problem area, and others.

Identification of Data Content


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Informaãní management For identification of data content more modelling techniques are used – models are complementary. E.g. interconnection of the context view and detailed sequence means the interconnection of the two techniques, i.e. the element is characterized by the view of ‘outside’ (contextual delimitation) and then its ‘inner’ side is described in detail (detailed delimitation), where the objects appearing in the contextual view must also have a role in the detailed delimitation. Similarly in delimitation of the functionality the user processes the topic as if the result of the solution was the offered functionality which is needed by the user or another target group; therefore the precise characteristic of ‘what I ask for’, i.e. the precise characteristic of the asked functionalities is necessary and one of the initial steps of the analyses; functionality can be delimited firstly by global and then by detailed delimitation. These techniques are to be applied using graphic and text tools. This may be the diagrams that are part of established modelling procedures, i.e. it is possible to use the graphical data modelling tools and the graphical process modelling tools. However, it is not necessary to use graphical tools belonging to the standards; it is just possible to use your own method of graphic expression.

Conclusion Managed business processes and data aimed at them from information systems significantly participate on fulfilling of business goals. Cohesion of business processes and data consists in the fact that in order for management of processes to be correct, relevant information is necessary and simultaneously business information systems have to contain such data, which serve for execution of business processes and support them. Business processes and their suitable data are closely related and constitute a coherent complex. Changes in the business process cause the need for changes in the relevant information system. Character of businesses processes changes and data changes are participated by process managers and actors. They know precisely and can define which activities are performed in what succession, what kind of data and in what format they are needed etc.

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The information system should also support another important functionality; it should support measurement of the performance quality of the work activity and the business process as a whole. To measure quality of performance, it is appropriate to use the principles of the Six Sigma method. The method evaluates defects in relation to defect opportunities. For Six Sigma utilization it is necessary to identify indicators by which defects are evaluated. An important factor is appropriate measurement of these values, thus determining which indicators should be measured and how they should be recorded. Indicators for measuring defects and opportunities for defects must become a part of the information system. It follows that changes in the structure of indicators or changes in the format of indicators cause the need for changes in the relevant information system. Character of these changes is participated by process managers and quality managers. The data of the given module of information system data performs more functions; it serves not only for the actor (worker) to support their work activities within the business process. Another function of the information system should be to provide data that can be used for assessing the quality of the performance of work activities and for assessing the quality of performance of the business process as a whole. It expands the requirements on developed / altered module of information system. The current typical data architecture is relational, based on the relational data model. The development of the relational data model represents a set of specialized knowledge, application methods of data modelling, including definitions of integrity constraints; this expertise is relevant for members of the technology development team. Direct involvement of actors in the development of a data model is not realistic as it is not possible to delegate to them the expertise associated with the development of relational data model. However, only they know the best what they want and need in order to improve quality and fulfil company goals; only they can, in some way, express their requirements and needs. Therefore, identification of data content must precede the development of the given module of information system. The definition of data content should include these areas: identification of indicators for measuring quality

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Informaãní management of performance of given work activities and the business process, identification of indicators for measuring quality of performance of other business processes, identification of data objects within the desired functionality, identification of links between indicators and links between data objects, etc. Actors, process managers and quality managers use data delimitation to describe accurately their data requirements that they need to perform their business activities. Only in this way, managers will receive tools in form of information systems that provide them with information relevant to business processes.

References [1] ALTER, S. Information Systems. Upper Saddle River: Prentice Hall, 2002. 286 p. ISBN 0-8493-1661-8. [2] BASL, J., et al. Podnikové informaãní systémy: podnik v informaãní spoleãnosti. Praha: Grada, 2008. 283 p. ISBN 978-80-247-2279-5. [3] BRÁZDILOVÁ, M. Konkurenãní zpravodajství a konkurenceschopnost podnikÛ. E+M Ekonomie a Management. 2005, Vol. 10, Iss. 4, pp. 47-53. ISSN 1212-3609. [4] CARTLIDGE, A., et al. Úvodní pfiehled ITILrV3. Prag: Hewlett-Packard s.r.o., 2007. 56 p. ISBN 0-95551245-8-1. [5] DATE, C. J. An Introduction to Database Systems. Boston: Addison-Wesley, 2004. 983 p. ISBN 0-321-19784-4. [6] EFQM Excellence Model [online]. Brussles, c2011 [cit. 2011-09-09]. Available from: . [7] EIPA. CAF – Common Assessment Framework [online]. Maastricht, c2011 [cit. 2011-11-02]. Available from: . [8] IT Governance Institute. COBIT 4.1. USA: IT Governance Institute, 2007. 213 p. ISBN 1-933284-72-2. [9] ITIL ® – IT Infrastructure Library [online]. [cit. 2011-10-10]. Available from: . [10] LEBA, M., et al. Software for Quality Evaluation Based on EFQM Excellence Model. In International Conference on Applied Computer Science. Malta, 2010. pp. 609-614. Available also from: < h t t p : / / w w w. w s e a s . u s / e - l i b r a r y / c o n f e r e n ces/2010/Malta/ACS/ACS-98.pdf>. ISBN 978960-474-225-7. ISSN 1792-4863. [11] MASAAKI, I. KAIZEN. Brno: Computer Press, 2004. 272 p. ISBN 80-251-0461-3.

[12] MOLNAR, Z. Efektivnost informaãních systémÛ. Praha: Grada, 2000. 142 p. ISBN 80-7169-410-X. [13] NOVOTN¯, O. et al. Business Intelligence: Jak vyuÏít bohatství ve va‰ich datech. Praha: Grada, 2005. 254 p. ISBN 80-247-1094-3. [14] PANDE, P. S., et al. Zavádíme metodu Six Sigma. Brno: TwinsCom, 2002. 416 p. ISBN 80238-9289-4. [15] POKORN¯, J., et al. Databázové systémy. Praha: Vydavatelství âVUT, 2003. 148 p. ISBN 80-01-02789-9. [16] SIX SIGMA. About Six Sigma [online]. [cit. 2011-10-25]. Available from: . [17] SKOPEâKOVÁ, H. et al. Subjective and Objective Metrics for Selfevaluation of Public Administration Organization. In International journal of mathematical models and methods in applied sciences. Malta: NAUN-Press, 2011. Iss. 1, Vol. 5, pp. 48-58. ISSN 1998-0140. [18] ·IMONOVÁ, S. et al. Proactive IT / IS Monitoring for Business Continuity Planning. E+M Ekonomie a Management. 2011, Vol. 14, Iss. 3, pp. 57-65. ISSN 1212-3609. [19] TURBAN, E. et al. Business Intelligence: A Managerial Approach. New Jersey: Prentice Hall, 2011. 312 p. ISBN 0-13-610066-X. [20] VRANA, I. et al. Zásady a postupy zavádûní podnikov˘ch informaãních systémÛ. Praha: Grada, 2005. 188 p. ISBN 80-247-1103-6. [21] VEBER, J. et al. Management kvality, environmentu a bezpeãnosti práce. Praha: Management Press, 2006. 358 p. ISBN 80-7261-146-1. [22] ZAVADILOVA, I. et al. Modeling of Process of System Changes under Conditions of IT Applications Outsourcing. In International journal of mathematical models and methods in applied sciences. UNIVERSITY PRESS. 2011. Vol. 5, Iss. 3, pp. 314-323. ISSN 2074-1308. [23] ZÁVODNÁ, L. S. Filozofie Kaizen ve sféfie sluÏeb. In STRI··, J. et al. (eds.) Aktuálne marketingové trendy v teórii a praxi. 1st Iss. Îilina: Edis, 2008, pp. 247-251. ISBN 978-80-8070-964-8. Ing. Stanislava ·imonová, Ph.D. University of Pardubice Faculty of Economics and Administration Institute of System Engineering and Informatics [email protected] Doruãeno redakci: 22. 11. 2011 Recenzováno: 27. 12. 2011, 30. 12. 2011 Schváleno k publikování: 9. 1. 2012

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Informaãní management

Abstract IDENTIFICATION OF DATA CONTENT BASED ON MEASUREMENT OF QUALITY OF PERFORMANCE Stanislava ·imonová Managing of business processes is done based on decision making and performance of workers in management positions who utilize business information systems as a necessary support tool. Business processes and business data form a coherent unit as business processes need relevant data for their execution while business data should fully serve to business processes. A change of the information system as a whole or a modification of a partial module of the information system are initiated exclusively by changes in the flow of instances of the business flow; this means either external changes such as customers’ requirements, change in suppliers’ approach or internal changes such as a change in activity flow or change in work activities as such. Current information systems are developed on the platform of relation database systems with the basic principle of relation data model. Analysis and design of a relation data model requires application of specialized methods leading to definition of relation schemes including integrity constraints. For development of a data model technical expertise is necessary which cannot be delegated on actors of working activities or managers of business processes. However, only the actors in process instances and managers of the process know their needs in relation with execution of the process, therefore, they should characterize their requirements on the module of the information system by identifying the data content. Information system serves as support for carrying out particular business process. Composition of indicators is changeable together with methods of measuring and evaluating and also the process itself changes as a result of its improvements. When controlling the values of indicators, it is necessary to take into account that within one process it is possible to measure indicators and then evaluate the same process or it is possible to measure indicators which provide data about quality or lack of quality of another process. The composition of indicators for measuring and evaluating quality of process performance or any change in the method of controlling should be an impulse for change of information environment. Therefore, development of each data model should be preceded by identification of data content with exact characteristics of requirements of composition and relation between measuring indicators. Key Words: business process, measurement of quality of performance, information system, data content. JEL Classification: L86, M11, M15.

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Recenze knih

MANAGEMENT KOMERâN¯CH BÁNK, BANKOV¯CH OBCHODOV A OPERÁCIÍ Autor: Jaroslav Belás a kolektív Nakladatelství: GEORG Îilina, 2010 Na ãesk˘ a slovensk˘ kniÏní trh se dostala mimofiádná publikace „Management komerãn˘ch bánk, bankov˘ch obchodov a operácií“, kterou zpracoval kolektiv zku‰en˘ch autorÛ z ãesk˘ch a slovensk˘ch vysok˘ch ‰kol pod vedením doc. Ing. Jaroslava Belása, PhD. z Univerzity Tomá‰e Bati ve Zlínû. Byl jsem pfiíjemnû pfiekvapen˘ rozsahem a kvalitou jednotliv˘ch kapitol, ve kter˘ch se promítla specializace jednotliv˘ch autorÛ. Monografie obsahuje celkem 471 stránek textu, rozdûleného logicky do 16 kapitol, které sv˘m obsahem vystihují název publikace a prezentovan˘ zámûr autorÛ. Celá struktura monografie poskytuje anal˘zu nejnovûj‰ích poznatkÛ, trendÛ, legislativních norem a prezentaci aktuálních procesÛ z bankovního sektoru v Slovenské republice a âeské republice. Publikace je zpracovaná na vysoké odborné úrovni s propojením teoretick˘ch poznatkÛ a praktick˘ch zku‰eností. Základním cílem autorÛ bylo pfiedloÏit odborné vefiejnosti a studentÛm vysok˘ch ‰kol ucelen˘ odborn˘ pfiehled uÏiteãn˘ch rad a návodÛ na pfiípadnou komunikaci s bankami, na zkvalitnûní finanãního fiízení firmy, ale i rodinn˘ch rozpoãtÛ. První kapitola charakterizuje ekonomick˘, finanãní a bankovní systém, vãetnû problematiky regulace a nezávislosti bankovního systému. Postavení a úlohám centrálních bank se vûnuje druhá kapitola. Souãástí kapitoly je i mûnová politika ECB po dobu finanãní krize. Tfietí aÏ jedenáctá kapitola je zamûfiená na komerãní bankovnictví. Mimo základních definic autofii s kritick˘m pohledem popisují systém fiízení komerãní banky, vãetnû fiízení v˘konnosti, rentability a rizik. Do ‰esté kapitoly je velmi zajímav˘m zpÛsobem zafiazená i problematika finanãní krize, její dÛsledky a systém záchranné sítû EU. Dvanáctá kapitola se zab˘vá elektronick˘m bankovnictvím a tfiináctá investiãním bankovnictvím. Hypoteãní bankovnictví je souãástí ãtrnácté kapitoly a doplÀkové obchody komerãní banky kapitoly patnácté. Zajímavá je poslední kapitola, ve které autorka popisuje a analyzuje historii bankovnictví Slovenské a âeské republiky od roku 1918. KaÏdá kapitola je ukonãená seznamem literatury, kter˘ umoÏní studentÛm a odborné vefiejnosti konkretizovat zdroje informací. Monografie nabízí nejen souhrn velmi dobfie zpracovan˘ch informací, ale i na základû kritick˘ch pohledÛ autorÛ námûty na polemické diskuze o jednotliv˘ch problémech souãasného bankovnictví. doc. Ing. Jaroslav Slepeck˘, PhD. Vysoká ‰kola bezpeãnostného manaÏérstva v Ko‰iciach

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VYBRANÉ TEORIE EKONOMICKÉHO RÒSTU Autor: Iva Nedomlelová Vydavatelství: Technická univerzita v Liberci, 2011 Na pozadí prebiehajúcej globálnej krízy a jej dlhového rozmeru sa ãoraz markantnej‰ie zdôrazÀuje problematika hospodárskeho rastu, jeho obnovy a udrÏateºnosti. V tomto kontexte moÏno uvítaÈ monografiu Ivy Nedomlelovej Vybrané teórie ekonomického rastu, ktorá prezentuje relatívne ucelenú genézu formovania teórií rastu, komparatívnu charakteristiku aplikovan˘ch metodologick˘ch prístupov, hodnotenie adekvátnosti ich reflexie ekonomickej reality a moÏností prípadného uplatnenia pri rie‰ení problémov vo v˘voji ekonomiky. Monografia je rozãlenená do 6 kapitol, ktoré tvoria vyváÏenú logickú ‰truktúru. Vstupná kapitola vymedzuje kºúãové pojmy a podáva koncentrovanú historickú retrospektívu teórii rasu. V ìal‰ích kapitolách autorka prezentuje fundovan˘ rozbor hlavn˘ch ‰kôl, resp. nosn˘ch smerov teórií rastu poãnúc klasickou politickou ekonómiou, keynesovsk˘mi teóriami ekonomického rastu, cez neoklasické teórie a teórie optimálneho rastu aÏ po modely endogénneho rastu. Z hºadiska v˘kladu treba oceniÈ skutoãnosÈ, Ïe autorka v rozbore hlavn˘ch ‰kôl v rámci kaÏdej vhodne uvádzala aj ìal‰ie vnútorné vetvenie ich rozvoja, ão svedãí o tom, Ïe nielen zvládla a sprístupnila ‰irok˘ reprezentatívny okruh literatúry, ale Ïe jej my‰lienkov˘ obsah aj funkãne zosystematizovala. Ucelenosti v˘kladu prospelo, Ïe priblíÏil aj vznik viacer˘ch pokroãilej‰ích modelov, ktoré sa nezaoberajú len jedn˘m produkãn˘m sektorom a modely ktoré explicitne reflektujú mikroekonomické rozhodovanie spojené s v˘skumn˘mi procesmi. Zaujímavé a z hºadiska väzby na prax sú relevantné tie aspekty v˘voja ekonomick˘ch teórií rastu ktoré sú spojené s in‰titucionálnymi faktormi a sú len veºmi ÈaÏko kvantifikovateºné. Mimoriadne podnetn˘mi pre formovanie modern˘ch teórií rastu sú tzv. ‰tylizované fakty Jonesa a Romera, ktoré kladú na model rastu nároky, aby zohºadÀoval interakciu medzi my‰lienkami, in‰titúciami, obyvateºstvom a ºudsk˘m kapitálom. Recenzovaná monografia predstavuje úspe‰n˘ pokus o spracovanie relatívne ucelen˘ch dejín ekonomického myslenia v oblasti teórií ekonomického rastu. K jej prednostiam nesporne patrí cielené úsilie autorky skúmaÈ charakter jednotliv˘ch modelov z hºadiska adekvátnosti odzrkadlenia ekonomickej reality a moÏnosti ich vyuÏitia v hospodárskej politike, ão umoÏnilo ukázaÈ v˘vojovú konfliktnosÈ ako hybnú silu rozvíjania a zdokonaºovania teórií ekonomického rastu. prof. Ing. Milan ·ikula, DrSc. Ekonomick˘ ústav SAV v Bratislave

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INTERNACIONALIZÁCIA PODNIKATEªSKEJ âINNOSTI MAL¯CH A STREDN¯CH PODNIKOV VO VYBRANOM SAMOSPRÁVNOM KRAJI Autor: Ladislav Mura Vydavateºstvo: Dubnick˘ technologick˘ in‰titút, Dubnica nad Váhom, 2010 Predkladaná monografia identifikuje proces internacionalizácie v podnikateºskej ãinnosti mal˘ch a stredn˘ch potravinárskych podnikov v Nitrianskom samosprávnom kraji, charakterizuje uplatÀované formy prieniku na zahraniãné trhy, uvádza rozhodnutia manaÏmentu skúman˘ch podnikov. Autor venoval tieÏ pozornosÈ teritoriálnej ‰truktúre existujúcej i perspektívne moÏnej expanzie mal˘ch a stredn˘ch podnikov. Syntetizuje skúsenosti a poznatky získané realizovan˘m v˘skumom a poskytuje informácie o súãasnom trende v ich podnikaní. Zámerom publikácie je predstaviÈ v˘voj a súãasné trendy v podnikaní agropotravinárskych subjektov zo sektoru malého a stredného podnikania, analyzovaÈ podniky ako aktérov medzinárodného podnikania a t˘m roz‰íriÈ teoretické poznatky a identifikovaÈ praktické moÏnosti pre podnikateºské subjekty. Tematické zameranie predkladanej monografie vychádza z odvetvovej orientácie autora na ‰pecifick˘ druh priemyslu, ktor˘m je potravinárstvo. Prioritou domácej hospodárskej politiky v sektore potravinárskeho priemyslu je zabezpeãiÈ ponuku v˘Ïivovo hodnotn˘ch, zdravotne bezpeãn˘ch a kvalitn˘ch potravinárskych produktov, podporovaÈ a ìalej rozvíjaÈ v˘konnosÈ agropotravinárskeho komplexu. Zaãlenenie do jednotného európskeho trhu poskytlo slovensk˘m podnikom moÏnosÈ roz‰íriÈ doteraj‰iu podnikateºskú ãinnosÈ. Otvoril sa priestor na úzku spoluprácu so zahraniãn˘mi partnermi mimo ná‰ho územia. Podnikanie v agropotravinárskom komplexe má takmer v˘luãne charakter malého a stredného podnikania. Proces internacionalizácie a vstup podnikov na zahraniãné trhy bol doteraz skúman˘ predov‰etk˘m z pozície veºk˘ch podnikov a multinacionálnych spoloãností. V porovnaní s veºk˘mi podnikmi majú v‰ak malé a stredné podniky svoje ‰pecifiká, ktoré je nutné braÈ do úvahy vo väzbe na ich podnikateºskú ãinnosÈ. Z uskutoãnen˘ch rozhovorov s predstaviteºmi manaÏmentu podnikateºsk˘ch subjektov malého a stredného podnikania autor získal relevantné informácie priamo z podnikateºskej praxe a na základe nich formuloval nasledovné okruhy prekáÏok, s ktor˘mi sa stretávajú najãastej‰ie pri vstupovaní, resp. nevstupovaní na zahraniãné trhy. Ide o deficit kapitálov˘ch zdrojov (predov‰etk˘m finanãn˘ch, ºudsk˘ch), absenciu ovládania cudzích jazykov, resp. nedostatoãná jazyková kompetentnosÈ, nedostatok skúseností s medzinárodn˘m podnikaním a z toho plynúci psychologick˘ strach, neschopnosÈ vytváraÈ alebo úspe‰ne rozvíjaÈ vzájomné vzÈahy so ‰ir‰ím podnikateºsk˘m prostredím, nedostatoãné znalosti s uplatÀovaním marketingového manaÏmentu v ‰pecifickom medzinárodnom prostredí. Odporúãame ju do pozornosti manaÏmentu podnikateºsk˘ch subjektov zo sektoru malého a stredného podnikania, odborníkom z akademickej obce, ale aj ‰tudentom vysok˘ch ‰kôl ekonomického zamerania. Dr.h.c. doc. JUDr. Alena Pauliãková, PhD. Vysoká ‰kola v Sládkoviãove Fakulta práva Janka Jesenského Katedra obchodného, hospodárskeho a finanãného práva

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Pokyny

Upozornûní a pokyny pro pfiispûvatele Pfiíspûvky se pfiijímají pfiednostnû v angliãtinû a dále v ãe‰tinû nebo sloven‰tinû. Za originalitu, odbornou i formální správnost pfiíspûvku zodpovídá autor. V ãasopise nelze publikovat ãlánek, kter˘ byl jiÏ uvefiejnûn v jiném periodiku. Redakãní rada si vyhrazuje právo pfiíspûvek odmítnout. O oti‰tûní pfiíspûvku rozhoduje redakãní rada ãasopisu. AutorÛm pfiíspûvkÛ doporuãujeme, aby definovali tématickou oblast, do které by svÛj pfiíspûvek zafiadili. Koneãné rozhodnutí o zafiazení do rubriky si v‰ak vyhrazuje redakãní rada ãasopisu. Pfiijetí pfiíspûvku od autora, kter˘ nepÛsobí na nûkteré z fakult podílejících se na vydávání ãasopisu, je moÏné pouze za editorsk˘ poplatek 100 EUR (2 500 CZK). Poplatek je nevratn˘. V pfiípadû zájmu, kontaktujte redakci ãasopisu ([email protected]). Prohlá‰ení o pÛvodnosti pfiíspûvku – spoleãnû s pfiíspûvkem odevzdá autor ãlenovi redakãní rady prohlá‰ení o tom, Ïe pfiíspûvek je originální a nebyl dosud nabídnut k publikaci jinému vydavateli. Text prohlá‰ení je k dispozici na webové stránce: www.ekonomie-management.cz/prohlaseni.doc. Pfiíspûvky jsou pfiijímány v˘hradnû v elektronické podobû, ve formátu MS Word. Pfiispûvatelé z fakult, které se podílejí na vydávání ãasopisu, pfiedají pfiíspûvek ãlenu redakãní rady své fakulty. Pfiispûvatelé z ostatních fakult se mohou obrátit na redakci. Nadpis pfiíspûvku je psán velk˘mi tuãn˘mi písmeny (velikost písma 16), zarovnán k levému okraji. Jméno autora (autorÛ) se uvádí bez titulÛ a je psáno tuãn˘m písmem (velikost písma 12). Pod jménem autora je opût vynechán jeden fiádek (o velikosti písma 10). Vlastní text pfiíspûvku je vhodné ãlenit do kapitol. Názvy kapitol se ãíslují (s v˘jimkou úvodu a závûru), pí‰í tuãn˘m písmem a zarovnávají k levému okraji. Je nutno dodrÏet následující nastavení: zarovnání do bloku, druh písma: Arial, velikost písma: 10, odsazení nového odstavce 0,5 cm, fiádkování: jednoduché, stránky neãíslovat. Tabulky a grafy se ãíslují a v textu na nû musí b˘t odkazy. Název tabulky (Tab. 1:) nebo grafu (Obr. 1:) je psán tuãn˘m leÏat˘m písmem, velikosti 10, zarovnává se vlevo a nepodtrhává se. Obrázky i grafy musí b˘t zfietelné i v ãernobílém provedení. Pod kaÏd˘m obrázkem i grafem musí b˘t uveden zdroj, ze kterého autor data ãerpal. Vzorce se oznaãují ãíslem v kulaté závorce. âíselné oznaãení je psáno v Arialu velikosti 10 a zarovnává se k pravému okraji vedle vzorce. Délka pfiíspûvku by nemûla pfiesáhnout 15 stránek A4. Identifikace v˘zkumného projektu. V pfiípadû, Ïe ãlánek publikuje v˘sledky konkrétního v˘zkumného projektu, uveìte na závûr pfiíspûvku kód a název projektu a oznaãení poskytovatele. Napfi. ãlánek byl zpracován s podporou projektu GA âR ã. 999/99/9999 „Název projektu“. Odkazy na literaturu se uvádí sefiazené abecednû dle pfiíjmení autora a upravené dle âSN ISO 690. Seznam musí obsahovat jen v textu vyuÏité zdroje. Na pfiíslu‰ném místû v textu se uvede ãíselné oznaãení v hranaté závorce [ ]. Poznámky pod ãarou nejsou pfiípustné. Pod tímto ãíslem je potom dílo uvedeno na konci pfiíspûvku v seznamu literatury – viz vzor: [1] JÁâ, I., RYDVALOVÁ, P. a ÎIÎKA, M. Inovace v malém a stfiedním podnikání. 1. vyd. Brno: Computer Press, 2005. ISBN 80-251-0853-8. [2] PITTNEROVÁ, R. Revitalizace textilních brownfields [online]. Liberec: Technická univerzita v Liberci, 2005. [cit. 2007-07-04]. Dostupné z: . [3] SIMOVÁ, J. ZpÛsob diferencovaného fiízení vztahÛ se zákazníky podle jejich hodnoty pro podniky v sektoru sluÏeb. E+M Ekonomie a Management. 2007, roã. 10, ã. 2, s. 118-127. ISSN 1212-3609. Adresa autora (autorÛ) je uvedena pod seznamem literatury. Obsahuje jméno a pfiíjmení (vã. titulÛ), název V·, název fakulty, název katedry (ústavu) a e-mailovou adresu. Recenze. Recenzi zaji‰Èuje redakãní rada. Recenzní fiízení vÛãi autorovi pfiíspûvku je anonymní. Anglick˘ název a abstrakt pfiíspûvku. Na konci pfiíspûvku je na samostatné stránce uveden anglick˘ název pfiíspûvku a abstrakt v rozmezí 250-300 slov v angliãtinû. Pod abstraktem jsou uvedena klíãová slova (key words) v angliãtinû a kódy klasifikace JEL (viz http://www.aeaweb.org/journal/jel_class_system.php).

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Instructions

Instructions Preferably, submissions should be in English; Czech or Slovak is acceptable as well. The author is responsible for scientific accuracy, originality, and the formal appropriateness of the article. If the submitted article has been published in another journal, it cannot be accepted. The editorial board has the right to refuse publication of the article. We recommend that the author(s) define the thematic field in which the article fits, but the board of editors makes the final decision regarding its positioning. Accepting a contribution from an author outside the faculties involved in publishing the E+M Economics and Management journal will be charged a submission fee of EUR 100 (CZK 2,500). The fee is nonreturnable. In case you are interested, please, contact the editorial office of the journal ([email protected]). Statement about the originality of the article – the author will submit a statement about the originality of the article and whether the article has been offered to another publisher. Both the statement and the article will be submitted to a member of the editorial board. The statement form can be found on the web site http://www.ekonomie-management.cz/statement.doc. The articles should be submitted electronically using MS Word and in doc format. Contributors from faculties dealing with the publishing of E+M Economics and Management will submit the contribution to a member of their editorial boards. Those from other faculties can submit their papers to the editorial office. Article headline should be written in font size 16 bold capital letters and aligned to the left margin. The author’s name should be written without titles or degrees and in font size 12 bold with a single space, size 10, between it and the text of article. The text of the article should be divided into chapters. Titles of chapters must be numbered (with the exception of the introduction and conclusion), written in bold type, and arranged from the left margin. It is necessary to follow the format described below: Arrangement into blocks Font style: Arial Font size: 10 Indent each new paragraph 5 spaces Spacing: single Do not include page numbers. Charts and graphs are to be numbered and the references must be in the text. The name of a chart (Tab. 1:) or a graph (Fig. 1:) should be written in font size 10 bold italics, aligned from the left margin and without underlining. Pictures and graphs must be visible and clear even in a black and white version. The source from which the author obtained the material should be written under every chart and graph. Formulas are to be numbered. The number should be written in font size 10 Arial in parentheses, aligned to the right margin and next to the formula. Length of article: maximum length should be 15 pages of A4 format. References to literature should be presented according to ISO 690. The list must contain only sources used in the text. References should be presented in the text in its respective place with an indication number in square parentheses. Footnotes are not allowed. At the end of the article in the bibliography, the indicated number should be written. See the following example of how to complete references: [1] HÁJEK, L. Economics: an overview of basic concepts and problems. 1st. ed., Hradec Králové: Gaudeamus, 2000. ISBN 80-7041-004-3. [2] LOW, CH. and LUNGOVÁ, M. The ethical approach to private sector property development: A comparison between the UK and the Czech Republic [online]. Liberec: Technical University of Liberec, 2006. [cit. 2007-07-04], . [3] ZÁMEâNÍK, R. Personnel controlling as a part of the management controlling system in an enterprise. E+M Economics and Management. 2007, Vol. 10, Iss. 2, pp. 29-36. ISSN 1212-3609. Author’s address: The author should present his/her contact information and co-authors‘ as well below the list of references. It must consist of a first name and surname (including titles and degrees), name of university, name of faculty, name of department (institute) and E-mail address. Review. A double-blind peer review is arranged by the editorial board. The title and abstract shall be in English. At the end of the article, on a separate page, there will be an English title of the article and an English abstract ranging between 250 to 300 words. Below the summary there will be given key words in English and JEL Classification codes (see http://www.aeaweb.org/journal/jel_class_system.php).

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TiráÏ

Upozornûní pro ãtenáfie Pfiíspûvky v ãasopise jsou recenzovány. Pfiíspûvky neprocházejí jazykovou redakcí. Contributions in the journal have been reviewed but not edited. Název ãasopisu (Journal Title):

E+M EKONOMIE A MANAGEMENT E&M ECONOMICS AND MANAGEMENT

·éfredaktorka (Editor in chief) doc. Dr. Ing. Olga Hasprová V˘konn˘ redaktor (Executive editor) doc. Ing. Miroslav ÎiÏka, Ph.D. Redakãní rada (Editorial Board) PhDr. Miroslav Barták, Ph.D.

Fakulta sociálnû ekonomická, UJEP Ústí nad Labem tel.: +420 475 283 837, e-mail: [email protected] doc. PhDr. Ing. Ale‰ Gregar, CSc. Fakulta managementu a ekonomiky, UTB v Zlínû tel.: +420 576 032 227, e-mail: [email protected] prof. Ing. Ladislav Hájek, CSc. Fakulta informatiky a managementu, Univerzita Hradec Králové tel.: +420 493 332 350, e-mail: [email protected] prof. Ing. Ivan Jáã, CSc. Ekonomická fakulta, TU v Liberci tel.: +420 485 352 361, e-mail: [email protected] doc. Ing. Emilia Jakubíková, CSc. Ekonomická fakulta, TU v Ko‰iciach tel.: +421 556 330 983, e-mail: [email protected] doc. Ing. et Ing. Renáta My‰ková, Ph.D. Fakulta ekonomicko-správní, Univerzita Pardubice tel.: +420 466 036 510, e-mail: [email protected] doc. Dr. Ing. Miroslav Plevn˘ Fakulta ekonomická, ZâU PlzeÀ tel.: +420 377 633 501, e-mail: [email protected] Mgr. Ing. Michal TvrdoÀ, Ph.D. Obchodnû podnikatelská fakulta v Karviné, Slezská univerzita v Opavû tel.: +420 596 398 460, e-mail: [email protected] prof. Ing. Mária Uramová, PhD. Ekonomická fakulta, UMB Banská Bystrica tel.: +421 484 462 617, e-mail: [email protected] Tajemnice redakce (Assistant of the editorial office) Ing. ·árka Hyblerová, Ph.D. tel.: +420 485 352 481, e-mail: [email protected] Vûdecká rada (Scientific Board) Dr. John R Anchor Dr., Eur. Ing., Eduard Babulak Dr. M. R. Biju prof. Ing. Jan âapek, CSc. prof. Ing. Jifií Fárek, CSc. prof. Andrew Harrison doc. RNDr. Josef Hynek, Ph.D., MBA Dr Frank Lefley prof. Philippe Norel doc. Ing. Marta Orviská, PhD. prof. Ing. Jifií Polách, CSc. prof. RNDr. Jaroslav Ramík, CSc. prof. Edson Luiz Riccio, Ph.D. Assoc. Prof. Manuel J. Sánchez-Franco prof. Dr. István Szintay, PhD. prof. RNDr. Vincent ·oltés, CSc. prof. Ing. Milan Zelen˘, Ph.D., M.S.

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University of Huddersfield, United Kingdom Fairleigh Dickinson University, Vancouver, Canada University of Kerala, India Univerzita Pardubice, Czech Republic Technická univerzita v Liberci, Czech Republic Univesity of Teesside, United Kingdom Univerzita Hradec Králové, Czech Republic Royal Holloway, University of London, United Kingdom Université de Poitiers, France Univerzita Mateja Bela v Banskej Bystrici, Slovakia Univerzita Tomá‰e Bati ve Zlínû, Czech Republic Slezská univerzita v Opavû, Czech Republic University of São Paulo, Brazil University of Sevilla, Spain University of Miskolc, Hungary Technická univerzita v Ko‰iciach, Slovakia Fordham University at Lincoln Center, New York, USA, Univerzita Tomá‰e Bati ve Zlínû, Czech Republic

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