Determination of economic indicators in the context of corporate sustainability performance.
Docekalova, Marie Pavlakova ; Kocmanova, Alena ; Kolenak, Jiri 等
Introduction
Corporate economic performance indicators are going to remain one
of the main interests of owners and investors. However, together with
the information about corporate governance, the environmental and social
factors, it creates a complex picture of any company and it has its
significance for other key shareholders, it brings transparency and
sustainability into business (Kocmanova et al. 2011). The goal of this
article is to define the most significant economic indicators of
corporate performance influencing the corporate sustainability.
Corporations are attempting to reach long-term benefits by implementing
sustainability related activities into the very core of corporate
strategy (Chabowski et al. 2011; Cruz et al. 2006). In general it can be
concluded that corporations implement these sustainability techniques
because they either feel obliged to do so, they want to do so or they
are forced to do so (Van Marrewijk 2003).
1. Theoretical approach to corporate sustainability performance
The need for alternative performance measurement systems which
would take the corporate influence on interest groups into account has
increased in the time of crisis. According to Kruse and Lundbergh (2010)
one of the reasons why corporations should consider their environmental
and social performance as well is the fact that investors generally
invest less money into corporations that do not follow this trend
because they consider the level of risk higher. Based on the definition
of sustainability performance published in the Report of the World
Commission on Environment and Development (1987) established by the UN,
corporate sustainability performance (CSP) is defined as corporate
strategy which uses the best business techniques to fulfill and balance
the needs of both the current and the future stakeholders. This presents
a complex task of providing competitive product in a short-term period
and at the same protecting, maintaining and developing human and natural
resources necessary for the future. CSP therefore measures the extent to
which a corporation implements economic, environmental, social and
corporate governance factors into its activities and to what extent it
considers the impact of its activities on its surroundings. (Artiach et
al. 2010; Labuschagne et al. 2005). CSP involves the triple-bottomline
concept which suggests balance of three aspects--environmental, social
and economic--to reach sustainability in organizations (Elkington 1998).
The connection of economic and sustainability performance is a
subject of many theoretical and empirical studies. Kirchhoff (2000),
Feddersen and Gilligan (2001), Fisman et al. (2008) state that economic
benefits are reached by companies with high level of CSP by using brands
and advertisements informing about the sustainability of their products.
This way they support product differentiation. Turban and Greening
(1997) show that high level of CSP allows companies to hire more
innovative and motivated employees which is again reflected in the
economic results. On the other hand there is the neoclassical approach
stating that companies have only one social responsibility and that is
increasing their profit (Milton Friedman). According to this approach
CSP decreases economic performance because activities increasing CSP are
costly (Friedman 1962; Alexander, Buchholz 1978; Becchetti et al. 2005).
Investing into CSP means higher costs of improving conditions for the
employees, donations, costs of supporting the community, introducing
ecological processes and also opportunity costs of giving up socially
irresponsible investments. From this point of view, investing into CSP
goes against the interests of investors because re-allocation of
investors' resources of the particular company onto other
stakeholders takes place (Aupperle et al. 1985; McGuire et al. 1988;
Barnett 2007). Ullmann (1985) states there is no direct connection
between CSP and economic performance. According to Ullmann the reason is
the existence of many variables which influence both types of
performance and that is why this connection should not exist.
It is important for companies to set measurable and relevant goals
of sustainable development and suitable metrics. Moreover, integrated
reporting of financial and non-financial information is needed (Hrebicek
et al. 2011a, 2011b, 2011c; Docekalova 2012). CSP indicators should
reflect the reality of the company and its critical success factors as
well as company values and culture. This is why their development should
not be limited only to borrowing already existing methods and norms.
However, internationally acclaimed norms can be a certain lead when
creating company's own suitable indicators. One of the examples of
these international norms and standards is: Global Reporting Initiative,
ISO 14031 Environmental management--Environmental Performance
Evaluation--Guidelines, United Nations Global Compact, World Business
Council for Sustainable Development Eco-efficiency Metrics, United
Nations Conference on Trade and Development Guidance on Corporate
Responsibility Indicators in Annual Reports, Society of Investment
Professionals in Germany Key Performance Indicators (KPIs) for
Extra-/Non-Financial Reporting etc. (overview of methodologies can be
found in Singh et al. (2009, 2012)).
2. Materials and methods
As the above mentioned suggests, CSP is a multidimensional concept
and the research methods have to take this into account. Defining the
economic indicators was realized in the following subsequent steps:
first step meant creating a set of economic indicators by analyzing the
approach of international organizations Global Reporting Initiative and
International Federation of Accountants. Then, the relevancy of these
indicators was verified by a questionnaire survey. The aim of the third
step was reducing the number of indicators which was realized by
removing duplicity information by correlation analysis and also by
factor analysis so that the loss of original indicators information was
minimal. The fourth step meant assigning weights to the key indicators
because different indicators have different levels of importance in
different companies. The impact on the total performance of each company
also varies and assigning the weights approximates reality. The weights
were set by point method as statistical testing of expert methods has
shown that it does not cause any statistically relevant differences in
the weights value results. As the last step aggregation methods were
applied to combine key indicators into one aggregate indicator measuring
economic performance and the benchmark was set.
2.1. Indicators reduction methods
To reduce dimensionality the correlation and factor analyses were
applied. The purpose of the correlation analysis is disclosing
multicollinearity of the key indicators and removing the redundant key
indicators from the model. High values of pair correlation coefficients,
i.e. [absolute value of r] > 0.8 suggest multicollinearity. To detect
multicollinearity the variance inflation factor was also used (Variance
Inflation Factor, VIF), which is easily detected from an inversion
matrix of the correlation matrix. VIF are diagonal elements of such an
inversion matrix (Clark 2004). The indicator with higher VIF value was
removed from the model. Factor analysis is based on a simple idea to
describe the behavior of a set of variables by using a smaller number of
new variables--factors --and via theses come to conclusions about the
mutual dependence of the original variables.
The factor model analysis is as follows:
[MATHEMATICAL EXPRESSION NOT REPRODUCIBLE IN ASCII], (1)
where:
[x.sub.i] (i = 1, ..., Q) is the original set of variables
(variables are standardized, i.e. zero mean value and unit
distribution),
[[alpha].sub.i1]; [[alpha].sub.i2], ..., [[alpha].sub.im] are
factor loadings (1),
[F.sub.1], [F.sub.2], ..., [F.sub.m] is m non-correlated
standardized factors,
[e.sub.i] is specific (unique, error, residual) part of the
variable [x.sub.i]. (OECD 2008; Skaloudova 2010).
There are various methods of carrying out the factor analysis, e.g.
principal component analysis, principal-axis method, alpha method, image
factoring etc. Before the computation it is useful to decide whether the
factor analysis is worth carrying out, i.e. the correlations of the
variables are possible to explain by factors. Kaiser-Meyer-Olkin
statistics (KMO statistics) is used for this as well as Barlett's
test of sphericity. KMO is based on comparing the values of pair and
partial correlation coefficients and reaches the values between 0 and 1.
KMO statistics values are possible to interpret as follows: 0.90-1.00
using factor analysis is excellent, 0.80-0.89 very good, 0.70-0.79
medium level of usefulness, 0.60-0.69 average, 0.50-0.59 poor and
0.00-0.49 not acceptable. Bartlett's test of sphericity tests zero
hypothesis that the correlation matrix of the variables is unit-based,
i.e. correlation coefficients of the variables equal zero and therefore
the condition of mutual dependence of variables is not met which
prevents applying the factor analysis (Skaloudova 2010).
An important decision has to be made when applying factor analysis
and that is the number of factors. This step significantly influences
the solution and interpretation of factor analysis results. To set the
number of factors the so called Kaiser criterion is used. According to
this rule only the factors that have eigenvalues greater than one are
retained. The number of factors can also be defined from graphic
presentation of eigenvalues of individual factors by using a scree plot.
The borderline marking the suitable number of factors lies where the
numerical drop between two factors is the most significant. The number
of factors can also be set heuristically.
Hendl (2009) states that factor analysis has three aims:
1. to analyze correlations of a number of variables by combining
the variables so that the variables in one cluster strongly correlate
and at the same time the variables in different clusters do not
correlate; this means the set is characteristic for the particular
factor variable,
2. to interpret the factors according to what variables are
included in the particular cluster,
3. to summarize the variability of the variables by only a few
factors.
Inner reliability of suggested indicators was then checked by
applying Cronbach's alpha. Inner reliability means that indicators
measuring the same phenomenon should have positive mutual correlations.
For this purpose the Cronbach's alpha or reliability coefficient or
consistency coefficient is applied. Cronbach's alpha reaches the
values of 0 to 1. Cronbach's alpha is computed by the following
formula:
[alpha] = [[K x C]/V]/[1 - (K - 1)(C/V)], (2)
where:
C is the average inter-item covariance among the variables,
V is the average variance of all the variables.
For standardized Cronbach's alpha the formula is:
[alpha] = [K x R]/[1 - (K - 1)(R)], (3)
where:
R is the average of all the correlation coefficients of the
variables.
2.2. Sample definition
The research is focused on companies of the group 27.1 Manufacture
of electric motors, generators, transformers and electricity
distribution and control apparatus according to CZ-NACE and also on
companies with more than 250 employees. This group of economic
activities is divided into two subgroups 27.11 Manufacture of electric
motors, generators and transformers a 27.12 Manufacture of electricity
distribution and control apparatus. The basic set consists of 32
companies. 65.2% of the addressed companies are majority-owned by
international subjects. Before the questionnaire survey itself it was
known from the basic set that majority of the companies had the form of
Ltd. 82.6% of questionnaires were filled in by Ltd companies and 17.4%
by stock companies, see Table 1. There was one cooperative in the basic
set as well, but this company did not take part in the survey. One of
the definition criterion of the basic set was number of employees. The
survey is only focused on companies with more than 250 employees. The
variable of number of employees has been re-coded into five intervals
and Table 1 shows the value frequency of each interval. Majority of
companies fall between 250-750 employees. Two companies of the interval
above 2251 employees stated these numbers of employees: 3200 and 7500.
3.Results and discussion
3.1. Defining key indicators of corporate economic performance
The basic set of economic performance indicators was defined on the
basis of synthesis of knowledge gained in the pre-research stage
(results published in Kocmanova and Docekalova (2012)) and approaches of
Global Reporting Initiative and International Federation of Accountants.
Resources consumption is described by indicator EN1--Costs.
Indicator EN2--Investments is focused on the investment-effectiveness.
Indicator EN3--Economic results measures how successfully the resources
were transformed and valorized. Effectiveness of using property and
financial resources is described by EN4. Cooperation with suppliers is
an important factor for all companies of manufacturing industry and that
is why indicator EN5--Supplier reliability has been included in the
basic set of indicators. Indicator EN6--Penalties describes the
financial impact of irresponsible behavior of a company. Also indicator
EN7--R&D expenses has been included in the economic indicators. See
Appendix Table A-1 for the list of indicators. These indicators were
then presented through questionnaire survey to top-managers of
corporations described in Table 1.
3.2. Number of indicators reduction
The questionnaire survey was based on evaluating the significance
of the indicators, i.e. how much the factors of these indicators support
the corporate sustainability performance. Rating method has been used.
Experts expressed their opinions on the basis of a predetermined scale
<0; 10>.
The first step of data processing was a quality check carried out
in order to find out whether there were erroneous, missing or distant
values in the data. The following statistic measures were computed in
order to get the basic knowledge of key indicators:
--Measures of central tendency (arithmetic mean and median),
--Measures of variability (range R, standard deviation s and
variation coefficient Vx),
--Measures of shape (skewness skew and kurtosis kurt).
These characteristics of key economic indicators are in Appendix
Table A-2. Evaluating the significance of Economic result shows the
lowest variability (Vx = 83.9% and s = 0.8) of all the indicators which
were presented in the survey. The respondents agreed on the key
significance of this indicator and its impact on success and performance
of a corporation ([bar.x] = 9.4 and [??] = 10.0). The highest variation
coefficient of the economic performance indicators is detected in ROCE
and this indicator is evaluated as the least significant ([bar.x] = 2.0
and [??] = 1.0). ROI has symmetrical distribution. Investments are the
most asymmetrical indicator (skew = -1.6), this indicator also has the
highest coefficient of kurtosis (kurt = 3.4).
The basic set of key economic indicators included twenty five
indicators. Key indicators that correlate the most (r > 0.79) are
listed in Table 2. Besides that Economic result and Revenue correlate
highly positively (r = 0.789) as well as Economic result with Total
costs (r = 0.774). After evaluating the values of pair correlation
coefficients and VIF values it was decided that Total costs, Revenue,
ROE, Receivables turnover ratio and Stock turnover would not enter into
further stages of constructing the aggregated indicator.
The next step of reducing the number of indicators was factor
analysis. To extract factors the principal component analysis was
selected. This method organizes the non-correlated factors (components)
according to their variance so that the first factor has the highest
variance and the last one the lowest one. The principal component
analysis exists individually as well; factor analysis can be considered
as its extension.
Number of factors is defined by Kaiser criterion.
Kaiser-Meyer-Olkin statistics of anti-image matrix for individual key
indicators has not sufficiently high value in these indicators:
Operational costs (KMO = 0.468), Investments (KMO = 0.337), Economic
result (KMO = 0.394), Economic value added (KMO = 0.344), Added value
(KMO = 0.450), Asset turnover (KMO = 0.384), Liability turnover (KMO =
0.461), Debt (KMO = 0.424), Supplier reliability (KMO = 0.471) and
Research and development expenses (KMO = 0.400). After removing these
economic indicators KMO statistics increased from 0.502 to 0.699 and at
this value it is meaningful to apply factor analysis. This is confirmed
by Bartlett's test, because based on its result the zero hypothesis
that variables do not depend on each other can be rejected, see Table 3.
Table 4 which shows the communalities makes it obvious that factors
explain the variability of Personal costs the best (87.0%) and only by
49.6% in case of Turnover.
Ten extracted components explain the total variance of the original
variables and the first three with eigenvalue higher than 1 explain
71.77% of total variance, see Table 5.
Scree plot shows all the extracted components with their
eigenvalues. The graph clearly shows the turn between the first and the
second component. It would therefore be sufficient to consider only one
component. According to the Kaiser criterion three components should be
selected--this approach is also more convenient considering the total
explained variance, see Appendix Figure A-1.
In Table 6 showing the factor matrix the factor weights lower than
0.3 are neglected. The key indicators selection is based on their
component correlation and only the ones with the factor weight higher
than 0.7 which can be considered sufficiently high, are included in the
aggregate indicator.
Two key indicators Cash Flow and ROA (Return on assets) are
included in the aggregate indicator and are combined into one component.
Reliability of this solution was verified by Cronbach's alpha
([alpha]), which approximates the recommended limit of 0.7, see Table 7.
3.3. Key indicators aggregation
Weights ([v.sub.i]) are computed by point method according to this
formula:
[v.sub.i] = [b.sub.i]/[[summation].sup.k.sub.i=1][b.sub.i], (4)
where: [b.sub.i] is an average number of points assigned by the
respondents to i KPI. For each aggregate indicator it is necessary to
compute normalized weights so that:
[k.summation over (i=1)][v.sub.i] = 1, for i = 1, 2, ..., k. (5)
The computed weights suggest that Cash Flow (v = 70.8%) is much
more significant than ROA (v = 29.2%).
There are three methods of aggregating indicators. Additive method
of aggregation is a linear method based on the summary of weighted and
normalized sub-indicators. An important condition when using the linear
additive aggregation is preference independence of individual
sub-indicators. (OECD 2008) The problem of indicators compensation can
be solved by the multi-criteria aggregation method which does not allow
sub-indicators compensation at all (Munda, Nardo 2005, 2009). A
compromise between fully compensational and non-compensational approach
to the aggregate indicator construction is offered by the geometry
aggregation method which defines the aggregate indicator as a product of
individual sub-indicators raised to a higher power by the particular
weight value. To create the aggregate indicator measuring economic
performance the geometrical aggregation method was selected. The key
indicators have to be transformed to the same units--%. Cash flow is
related to added value.
[FIGURE 1 OMITTED]
Aggregate indicator measuring economic performance is described as
follows:
EI = [x.sup.0,708.sub.1] x [x.sup.0,292.sub.2], (6)
where:
[x.sub.1]--Cash flow/added value;
[x.sub.2]--EBIT/assets.
Benchmark value is established by two ways:
1. From the best values in the group of companies 27.1 NACE;
2. From the average values in the group of companies 27.1 NACE.
In 2012 the values (2) are as follows:
--Cash flow/[Added value.sub.average] = 27.73%; Cash flow/ [Added
value.sub.best] = 39.04%.
--[ROA.sub.average] = 11.46%; [ROA.sub.best] = 43.84%.
--[benchmark.sub.2012]:[EI.sub.average] = 21.42%; [EI.sub.best] =
40.39%. Graphic depiction of the computed aggregate indicator is
presented using the example of four companies, see Figure 1.
Conclusions
The aim of this article was to define economic indicators which
influence corporate sustainability performance the most. To meet this
aim an expert evaluation with a subsequent statistic evaluation was
used. By applying statistic methods (correlation and factor analyses) it
was found out that the original set of 25 key indicators can be
substituted by only two--Cash flow a ROA. These indicators are therefore
the top key ones for corporate sustainability from the point of view of
the top-management. It is important to emphasize that it cannot be said
about any of the indicator sets that it is optimal as the development
and application of indicators should be a dynamic process which supports
decision making and company management more than the goal itself.
Searching for the balanced set of indicators is a complex process.
Provided it is allowed for the indicator set development process to take
long time, it can reduce its dynamics and reliability. Once a small,
good and balanced set of simple indicators is created, the real effort
should be made creating the evaluation process, providing the indicators
are a base of a constructive dialogue among the organizational levels
and mainly coming up with a way to improve the values of these
indicators.
Caption: Fig. 1. Graphic depiction of economic aggregate indicator
and benchmarks (source: own processing)
doi: 10.3846/btp.2015.450
APPENDIX
[FIGURE A-1 OMITTED]
Caption: Fig A-1. Scree plot (source: own processing)
Table A-1. Basic set of economic indicators
(source: own calculation)
Indicator Key indicator
EN1--Costs Total costs
Personal costs
Operational costs
EN2--Investments Investments
ROI
EN3--Economic results Economic result
Revenue
ROE
Economic value added
Added value
Turnover
Cash flow
Market share
EN4--Asset & ROS
financial resources
utilization ROA
ROCE
Liquidity
Assets turnover ratio
Stock turnover ratio
Liability turnover ratio
Receivables turnover ratio
Debt
EN5--Suppliers Supplier reliability
reliability
EN6--Penalties Monetary penalty value
EN7--Research and Research and development expenses
development expenses
Table A-2. Descriptive characteristics of indicators
(source: own calculation)
Indicator R Min. Max.
Economic result 3.0 7.0 10.0
Revenue 3.0 7.0 10.0
Expenses 6.0 4.0 10.0
Turnover 5.0 5.0 10.0
EVA 9.0 0.0 9.0
ROE 9.0 0.0 9.0
ROA 10.0 0.0 10.0
ROCE 9.0 0.0 9.0
ROI 9.0 1.0 10.0
ROS 10.0 0.0 10.0
Liquidity 10.0 0.0 10.0
Investments 6.0 4.0 10.0
Assets turnover 10.0 0.0 10.0
Stock turnover 10.0 0.0 10.0
Claim turnover 9.0 1.0 10.0
Liability turnover 8.0 1.0 9.0
Debt 10.0 0.0 10.0
Added value 10.0 0.0 10.0
Personal expenses 6.0 4.0 10.0
Operation expenses 6.0 4.0 10.0
Cash Flow 6.0 4.0 10.0
Research and development expenses 10.0 0.0 10.0
Monetary penalty value 8.0 0.0 8.0
Market share 10.0 0.0 10.0
Supplier reliability 7.0 3.0 10.0
Indicator [bar.x] [??] s
Economic result 9.4 10.0 0.8
Revenue 9.1 10.0 1.1
Expenses 8.8 10.0 1.7
Turnover 8.3 9.0 1.5
EVA 3.0 3.0 2.9
ROE 3.4 3.0 3.0
ROA 3.1 3.0 3.0
ROCE 2.0 1.0 2.5
ROI 5.8 5.0 2.8
ROS 3.7 3.0 3.3
Liquidity 6.3 7.0 2.2
Investments 8.6 9.0 1.5
Assets turnover 3.8 4.0 2.8
Stock turnover 6.1 6.0 3.0
Claim turnover 6.2 6.0 2.2
Liability turnover 5.9 6.0 2.1
Debt 5.3 6.0 3.7
Added value 7.2 8.0 2.4
Personal expenses 8.4 9.0 1.5
Operation expenses 8.5 9.0 1.7
Cash Flow 7.5 8.0 1.9
Research and development expenses 7.2 8.0 2.7
Monetary penalty value 3.1 2.0 3.0
Market share 7.0 8.0 2.9
Supplier reliability 6.3 6.0 2.2
Indicator Vx (%) skew kurt
Economic result 8.9 -1.5 1.9
Revenue 12.5 -0.9 -0.8
Expenses 19.5 -1.6 2.0
Turnover 18.3 -0.9 0.1
EVA 99.6 0.7 -0.3
ROE 87.9 0.6 -0.7
ROA 95.5 0.7 -0.3
ROCE 121.2 1.2 1.2
ROI 47.9 0.0 -1.0
ROS 89.1 0.7 -0.8
Liquidity 35.6 -0.9 1.4
Investments 17.1 -1.6 3.4
Assets turnover 72.1 0.4 -0.2
Stock turnover 50.0 -0.3 -0.5
Claim turnover 35.8 -0.3 -0.3
Liability turnover 36.1 -0.2 -0.4
Debt 69.0 -0.3 -1.4
Added value 32.9 -1.5 2.9
Personal expenses 17.8 -1.3 2.1
Operation expenses 20.1 -1.3 1.1
Cash Flow 25.5 -0.6 -0.6
Research and development expenses 38.2 -1.0 0.4
Monetary penalty value 95.7 0.3 -1.4
Market share 41.8 -1.0 0.1
Supplier reliability 35.3 -0.1 -1.4
Acknowledgements
This paper is supported by The Czech Science Foundation. Name of
the Project: Measuring Corporate Sustainability in Selected Sectors.
Reg. No. 14-23079S.
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(1) Factor loadings are between -1 and +1 and can be interpreted as
correlation coefficients between the variables and factors.
(2) According to Database Amadeus, Bureau van Dijk
Marie PAVLAKOVA DOCEKALOVA [1], Alena KOCMANOVA [2], Jiri KOLENAK
[3]
[1,2] Department of Economics, Faculty of Business and Management,
Brno University of Technology Kolejnt 2906/4, 612 00, Brno, Czech
Republic [3] Newton College, Politickych vezhu 10, 110 00, Praha, Czech
Republic
E-mails: [1] docekalova@bm.vutbr.cz (corresponding author); [2]
kocmanova@bm.vutbr.cz; [3] jiri.kolenak@newtoncollege.cz
Received 13 March 2014; accepted 14 January 2015
Marie PAVLAKOVA DOCEKALOVA. Doctoral candidate at Brno University
of Technology, Faculty of Business and Management, Department of
Economics. Research interests: environmental, social and economic
corporate performance, sustainable corporate performance, Corporate
Governance.
Alena KOCMANOVA. Associate professor at Brno University of
Technology, Faculty of Business and Management, Department of Economics.
Research interests: corporate sustainability, environmental, social and
economic corporate performance.
Jiri KOLENAK. Vice-rector of Newton College in Brno. Research
interests: personal growth of managers with connection to sustainable
corporate performance.
Table 1. Basic information on companies taking
part in questionnaire survey (source: own
calculation)
Criteria N %
Majority Owner
Domestic subject 8 34.8
International subject 15 65.2
Legal form
Stock company 4 17.4
Ltd 19 82.6
Number of employees in 2012
250-750 13 56.5
751-1250 4 17.4
1251-1750 2 8.7
1751-2250 2 8.7
More than 2251 2 8.7
Table 2. Correlation analysis
(source: own calculation)
KPI r
Expenses 0.845
Revenue
ROA 0.897
ROE
Liability turnover 0.948
Claim turnover
Claim turnover 0.796
Stock turnover
Liability turnover 0.792
Stock turnover
Table 3. KMO statistics and Bartlett's
sphericity test (source: own calculation)
Kaiser-Meyer-Olkin statistics 0.699
Bartlett's Approx chi-sq. 106.734
sphericity df 45
test Sig. 0.000
Table 4. Communalities of economic KPIs (source: own
calculation)
KPI Initial After extraction
Personal costs 1.000 0.870
ROI 1.000 0.834
ROE 1.000 0.808
Turnover 1.000 0.496
Cash Flow 1.000 0.785
Market share 1.000 0.803
ROA 1.000 0.806
ROCE 1.000 0.504
Liquidity 1.000 0.502
Monetary penalty value 1.000 0.770
Table 5. Numbers and percentage of explained distribution
(source: own calculation)
Component Number % of explained Cumulated %
variance
1 4.472 44.720 44.720
2 1.541 15.408 60.128
3 1.165 11.646 71.773
4 0.989 9.889 81.663
5 0.578 5.779 87.442
6 0.458 4.585 92.027
7 0.308 3.078 95.104
8 0.217 2.166 97.270
9 0.163 1.625 98.896
10 0.110 1.104 100.000
Table 6. Factor solutions matrix (source: own calculation)
KPI Component
1 2 3
Personal costs 0.689 0.601
ROI 0.675 -0.599
ROE 0.693 -0.568
Turnover 0.656
Cash Flow 0.821 -0.323
Market share 0.553 -0.701
ROA 0.711 0.504
ROCE 0.689
Liquidity 0.676
Monetary penalty value 0.462 0.627 0.405
Table 7. Reduced set of economic KPIs
(source: own calculation)
Economic Performance
Cash Flow
ROA
A = 0.651
