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Decision Support Systems in Strategic Management www.ef.uns.ac.rs/sm ISSN 1821-3448 UDC 005.21 2012, Vol. 17, No. 4

Contents Risk Management of an Investment Project through Monte Carlo SimulationIlija Hristoski, Kosta Sotiroski, Igor Zdravkoski 3-15 Influence Diagrams: Predictive Approach in Decision Support SystemsAmin Hosseinian-Far, Elias Pimenidis, Hamid Jahankhani 16-22 The Internal Determinants of Profitability in the Serbian Banking SectorMilivoje Davidović, Jovana Ivančević, Tamara Antonijević 23-31 Changes in the Human Resource Compensation Systems of European Companies – Based on the CRANET Research Result Analysis Gizela Štangl Šušnjar, Agneš Slavić 32-40 Application of the Process-Based Organisational Model as a Basis for Organisational Structure Improvement in Crisis Stefan Komazec, Ivan Todorović, Miloš Jevtić 41-49 From Customer Satisfaction to CSR in Serbian Conditions: a Review of Literature and Business Practices Dragan Ćoćkalo, Cariša Bešić, Dejan Đorđević, Srđan Bogetić 50-58

STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 003-015 UDC 005.334:519.245 ; 005.8

Received: February 14, 2012

Accepted: October 5, 2012

Risk Management of an Investment Project through Monte Carlo Simulation

Ilija Hristoski, Kosta Sotiroski, Igor Zdravkoski University of St. Clement of Ohrid in Bitola, Faculty of Economics in Prilep, F. Y. Republic of Macedonia

Abstract There are not many certainties when doing business in the contemporary world, especially during periods ofeconomic downturns. Business decisions have always been accompanied with a substantial amount of risk. Inthe area of project investments, safe investing has become a priority for many investors following the globalfinancial crisis of 2007-2008, which caused so much stress. The Republic of Macedonia was not exempt from negative impacts. Due to numerous disastrous effects, in-cluding labour jobs reduction, additional increase of unemployment rate, decrease in salaries etc., and bearingin mind the significant agricultural tradition of the country, many have been forced to turn to start their ownbusiness in production of various agricultural products, including tobacco, vegetables, fruits etc., thus makingtheir first project investments. However, despite the current advances in risk management methodologies, risk assessment has almost always been neglected, and decisions about whether to invest or not have been typi-cally based either upon somebody’s previous experiences, or by applying ad hoc rules of thumb. Managing risk is a crucial part of the decision making process any executive in a company must make. It is asubstantial part of any investment project as well. One of the most advanced and widely utilized methodolo-gies for quantifying risk is the Monte Carlo simulation, a tool that can help project managers to determine thelevel of risk intrinsically involved in complex situations, and before making any decision related to a project.The application areas include, among others, financial risk analysis, valuation, engineering, portfolio alloca-tion, cost estimation, and project management. Within this paper we show how Monte Carlo simulation can be effectively utilized for the valuation of a realinvestment project related to production of strawberries, based on discounted cash flows (DCFs) and simula-tion of net present value (NPV), as well as other relevant economic indicators. Keywords Monte Carlo simulation, risk management, net present value (NPV), discounted cash flow (DCF), investmentproject valuation.

Introduction

The recent financial and economic crisis, the worst since the Great Depression of the 1930s, which started in 2007-2008 and is still present all these years, was a huge systemic shock for almost all na-tional economies. The period of global economic instability has been prolonged since, having profound effects on all segments of human activity. Nonetheless, it is expected that the mid- and long-term effects of such tectonic economic shift will still be felt in the forthcoming years, during the period of recovery. One of the mean reasons for the appearance of the actual worldwide economic downturn was the inabil-ity of the leading financial institutions to effectively evaluate the risk of their capital investments. Since risk can be defined as a potential that a chosen action or activity (including the choice of inactivity) will lead to a loss (an undesirable outcome), it is obvious that the evaluation of risk, also known as risk as-sessment, will play a crucial role in the post-recession era more than ever before. This is especially im-portant in circumstances when the rising economic pressure enforces companies to try increase the level of their overall performance in order to become more efficient and concurrent on the market. Thus, cop-

4 Ilija Hristoski et al. Risk Management of an Investment Project through Monte Carlo Simulation

STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 003-015

ing with risk and uncertainty seems to be an imperative of the contemporary art of doing business, which is evident not only to companies, but also to any individual involved into production and/or sales of goods.

Numerous definitions equalize risk and uncertainty. However, according to Knight (1921) and Hub-bard (2009), there is a clear distinction between these two categories. Knight states that risk applies to situations where the outcome of a given situation is not known, but the chances can be accurately meas-ured, or quantified. On the other hand, uncertainty is the lack of complete certainty, that is, the existence of more than one possibility, and therefore, it applies to situations where the information needed to set accurate probabilities to events is not provided. However, in the real, ever-changing business surround-ing, such distinction between the two terms is quite irrelevant, since all related events, correlations, de-pendencies, variables and interactions are so complex that forecasting is always a matter of struggling with uncertainty, not risk. Moreover, the economic crisis introduces additional uncertainties, making the future relatively unpredictable due to our imperfect knowledge of unknown or uncertain future events.

1. The investment project

For the purpose of this article, we will assume that, due to severe economic crisis that has led to poor living conditions, an individual named N.N. is forced to make additional earnings for living through producing agricultural products and selling them on a local market. N.N., who does not possess much of cash to invest in a large scale project, is not eager to take a loan from a bank due to high interest rates. N.N. is willing to risk the hard-earned money by investing it, looking for the most convenient and most cost effective option, which will return the invested money as soon as possible, bearing in mind the fi-nancial, organizational and spatial constraints. After considering multiple ideas and options, N.N. has finally focused on the possibilities to invest in a production of Elsanta type of strawberries. The advan-tages of the garden strawberry (lat. Fragaria × ananassa cv. Elsanta) are quite obvious: the strawber-ries of this type are fast growing and can be grown in almost any soil; they produce lots of fruits that are long lasting; they have a good shelf life and taste; they can be easily grown and are less prone to dam-age than other varieties. If grown in a protected environment isolated from outer atmospheric impacts, i.e. in plastic polytunnels (greenhouses made of plastic sheeting on metal hoops), the strawberries can be harvested twice a year: first, in May/June and again, in September/October. Additionally, such kind of strawberries is very rewarding, i.e. their retail price on local markets is always significantly higher in comparison to other seasonal fruits. Another great advantage is the possibility to grow a considerable number of strawberry plants on quite a small area. So, N.N. is planning to build a plastic polytunnel with dimensions of 5m x 6m (30m2) in his/her own garden, which would be large enough for growing up to 1000 bedding plants. The total period of observation consists of 8 seasons (2 seasons per year, the first one in May, and the other in October), from May 2012 (t = 1) to October 2015 (t = 8), plus the starting time period, October 2011 (t = 0), when initial investment was made. The choice of producing strawberries seems to be quite an adequate option, but what N.N. does not know at the moment is how to cope with multiple uncertainties that are intrinsic to the production and sales. For example, potential risks include: poor yield per season due to plant diseases; damaged plants due to infestation of insects; poor quality of the fruits due to improper ventilation, fertilization and/or watering; a considerable small market share; poor selling campaign; retail prices lower than expected, many uncertainties related to costs of production and sales etc. By quantifying such uncertainties, rational decisions can be made about which risks are worth taking.

2. Managing risk and uncertainty

Several questions arise at this point: Are all of these risks controllable and assessable? Can they some-how be quantified? Which one makes the greatest impact on the success or failure of the entire invest-ment process? Can one consider various typical “what-if” scenarios, taking into account the impact of the occurrence of several of these risks at the same time? And finally, is the whole idea of investing into production of strawberries well-founded? Should N.N. invest into production of strawberries or not, bearing in mind all possible risks? According to Nemuth (2008), “risk identification at an early stage and an integrated in-house risk management is an indispensable requirement for a monetarily positive result of a project”. Stempowski (2002) gives a schematic view of the risk management cycle, com-

Ilija Hristoski et al. Risk Management of an Investment Project through Monte Carlo Simulation 5


prised of four phases, including: risk identification, analyzing, evaluation and monitoring. We have al-ready identified the possible major specific risks related to the investment project (Phase 1). Potential risks have to be identified at the early stage of the investment project, so a better risk awareness for the project could be achieved as soon as possible. The goal of Phase 2 (Analyzing) is to perform financial analysis of the entire investment project, as well as financial modelling by building up a mathematical model that will correspond to the requirements and specifications of the actual investment project. The basic financial model will be based on discounted cash flows (DCFs), and will calculate, as an output, several economic indicators necessary for evaluating investment projects, including: Net Present Value (NPV), Internal Rate of Return (IRR), Profitability Index (PI) as well as payback time period, i.e. the key parameters required for performing a decision making process. The initial model is going to be a deterministic one, since the methodology for calculating previously mentioned output parameters is based on the usage of closed-form mathematical expressions, while stochastic elements will be built-in by assigning corresponding probabilistic distributions only to critical input variables, previously identi-fied. In this phase, all of the input parameters should be analyzed and categorized as either deterministic or stochastic. The phase of evaluation (Phase 3) will be carried out by means of Monte Carlo simula-tion. We could also use other evaluation methodologies instead, such as “what-if” analysis or scenario analysis, but simulation results are considered to be the most significant, most versatile and most com-prehensive when compared to other risk analysis methods. Risk monitoring (Phase 4) will be done dur-ing the process of actual implementation of the investment project. As the project itself is being divided into seasons throughout the time line, one should monitor (measure and record) all the relevant parame-ters in order to be analyzed and compared to the simulation results, gained through Phase 2 and Phase 3.

3. Building up the financial model

The basic premise to usage of Monte Carlo simulation as a risk management tool is the existence of the basic deterministic mathematical finance model of the investment project. The key steps in building such a model are given in Ross, Westerfield, & Jaffe (2008). The basic structure of the model is pre-sented by Table 1. An exhaustive elaboration on the key elements of the model follows.

Table 1 A schematic representation of the basic structure of the financial model

Time periods (seasons) Oct. 2011 May 2012 … Oct. 2015 t = 0 t = 1 … t = 8

Discount rate 4% 4% 4% Growth rate 3,724% 3,724% … 3,978%

Discount factor 1,00000 0,99735 … 0,75976 Input parameters A, B, C, …

Revenues … Variable costs …

Fixed costs … Amortization …

Profit before tax … Tax (12%) …

Profit after tax … Cash flow …

Initial investment … NPV, IRR, PI, payback period

Source: Ross, Westerfield, & Jaffe, 2008. 3.1. Initial investment

The structure of the initial investment, made in October 2011, is given on Table 2. Besides this, N.N. plans to make additional investment by doubling the plants to the maximum of 1000 in October 2012 (Table 3). Since strawberry plants have to be replaced by new ones every second year, N.N. will have to invest in their renewal first in May 2014 (t = 5), when the first 500 of them will be replaced, and then in October 2014 (t = 6), when the rest of 500 will be replaced (Table 4).



3.2. Analysis of revenues

Figure 1 depicts the analysis of revenues per season. An alternative way to calculate actual revenue per season is based upon our knowledge and estimation of the capacity of the local market per season [kg] and N.N.’s market share per season [%]. Nonetheless, the analysis depicted on Figure 1 is much more detailed, since it is based on much more input variables.

Table 2 Initial investment structure

Season Item No. Item Amount

October 2011

1. Metal construction, paint, concrete 200 € 2. Plastic sheeting (PVC foil), hooks 110 €

3. “Drop-by-drop” watering system (water pump, hosepipes, water filter, plastic connectors)

120 €

4. 500 bedding plants, transportation costs, 125 polystyrene pots, 600 litres of potting compost (humus), additives for protection against pests, fertilizers, nutriments

270 €

TOTAL = 700 €

Source: Authors Table 3 Expenditures necessary for doubling the number of strawberry plants

Season No. Description Amount

October, 2012

1. Additional 500 bedding plants 121,9 € 2. Transportation costs 8,1 € 3. 125 polystyrene pots 50,8 €

TOTAL = 180,8 €

Source: Authors

Table 4 Expenditures needed for replacing the bedding plants

Season No. Description Amount

May 2014; October 2014

1. Additional 500 bedding plants 121,9 € 2. Transportation costs 8,1 €

TOTAL = 130,0 €

Source: Authors

Figure 1 Analysis of revenues per season Source: Authors

Figure 1 shows that actual revenue per season (R) is, in fact, a function of several input variables

(parameters), named B, D, F, G, and H. In addition, it also shows the functional (mathematical) rela-tionships among them. Out of these, only the parameter G (Number of plants per season) is being a de-terministic one, since N.N. knows the exact number of strawberry plants at each moment. All other in-put parameters are uncertain, and can take on different values in different seasons. The uncertainty in the model is going to be modelled by means of probability distributions that are most adequate, i.e. most



comparable to the nature of the observed input variables. In most cases, the following five types of probabilistic distributions are used when modelling uncertainty in investment projects, including: Nor-mal/Gaussian, Lognormal, Uniform, Exponential, and Triangular distribution. Closely related to Figure 1, Table 5 lists the stochastic input variables B, D, F, and H, along with their corresponding probabilis-tic distributions and the specific probabilistic parameters for the expected (most possible) scenario.

Table 5 Overview of the input variables needed for evaluation of revenues by seasons

Input variable Name / Meaning Probabilistic distribution

Parameters (expected scenario)

B Average retail price per season Triangular Min = 1,5; Mode = 2,0; Max = 2,5

D Percentage of quantity sold per season Normal Range: [85% - 95%] = 90%; = 1,66%

F Percentage of loss per season Normal Range: [10% - 40%] = 25%; = 5%

H Yield per plant per season (May) Triangular Min = 0,2; Mode = 0,3; Max = 0,4

Yield per plant per season (October) Normal Range: [5% - 20%] = 12,5%; = 2,5%

Source: Authors It is also worth to mention that the same type of probabilistic distribution need not be used for mod-

elling uncertainty of a single input variable along all the periods of the time line. This is a recommended practice since the mathematical model should represent the reality as good as possible. For instance, it is a fact that the yield per plant per season in May can be up to 20% higher than that one in October each year. Thus, this variable has to be modelled by using two different types of probabilistic distributions, depending on the season, i.e. by Triangular distribution for May, and then, by modelling the decrease of the yield in October relatively to the yield in May by using Normal distribution (Mean = 12,5%; St. dev. = 2,5%).

3.3. Cost analysis

Besides revenues, total costs have to be included in the finance model, as well. The typical categoriza-tion of costs covers two general types, i.e. variable and fixed costs.

3.3.1. Variable Costs

Variable costs are changing along with the product changes, and are equal to zero when there is no pro-duction at all. It is a usual practice to assume that variable costs are constant per product unit, implying that the total variable costs are proportional to the level of production. The costs of direct labour force and the costs for raw materials are usually considered as being variable costs (Ross et al., 2008). In our case study, the costs of direct labour force are zero, since N.N. is not willing to hire additional workers. By default, the costs for raw materials should be expressed as a linear function of the produced quantity of strawberries [€/kg], but in this particular case, the costs for raw materials will be analyzed per season [€/season], because all of the input variables can be properly identified this way. In addition, the analy-sis of variable costs per season is much more precise from the perspective of mathematical modelling, since some of the variable costs appear only in particular seasons.

3.3.2. Fixed Costs

Fixed costs are not dependent on the quantity of goods produced or services committed during the ob-served period of time. They are usually measured as a cost per time unit. For instance, salaries and hir-ing taxes are fixed costs (Ross et al., 2008). In our case study, there will be no costs for salaries, since N.N. is an individual producer. There will also be no hiring taxes for the cultivated ground area, since he/she intends to grow strawberries in his/her own garden. The only fixed costs emerge in the phase of selling strawberries on the local market – it is the cost of local market tax per season [€/season] (Table 6).



Table 6 Fixed costs

Season Item No. Item Amount

*** 1.

Local market tax per season (6 weeks per season x 3 times/week x 3 €)

54 €/season

2. Number of seasons 8 seasons TOTAL = 432 €

Source: Authors The complete analysis of costs, both variable and fixed, is depicted on Figure 2. Similarly as in the

case of the analysis of revenues, costs can also be split down to their basic components – input vari-ables. In this case, variable C (total cost) is a function of the variables E, F, J, L, M, N, O, P, Q, R, S, T, and U, as shown on Figure 2. Out of these, only variables N (Water consumption per plant per day) and R (Actually produced quantity per season) are stochastic, since a considerable amount of uncertainty is inherent to their nature, while all others are deterministic, i.e. known a priori. We have already men-tioned the input variable R (Actually produced quantity per season) as input variable C in the analysis of revenues. From Figure 2, it is obvious that input variable C depends on stochastic variables G, H, and F. The probabilistic definitions of the variables H and F (variable G is deterministic one) are already given in Table 5, so we define only the input variable N in Table 7.

Table 7 Stochastic input variables needed for estimation of the costs per seasons

Input variable

Name / Meaning Probabilistic distribution

Parameters (Expected scenario)

N Water consumption per plant per day (season: May) Normal

Range: [0,3 - 0,7] = 0,5; = 6,66%

Water consumption per plant per day (season: October)

Normal Range: [0,6 - 1,0] = 0,8; = 6,66%

Source: Authors

Figure 2 Analysis of costs per season Source: Authors



3.3.3. Other important issues

In order to calculate NPV and other relevant indicators, several important economic parameters have to be incorporated in the mathematical model: the amortization, the discount rate, the inflation rate, and economic growth rate. The amortization (25€) is fixed per season. A nominal discount rate of 4% per season (1 season = 6 months), constant for all seasons, was used within the model, since the average annual bank interest rate for deposits is 8%. Also, we have to take into consideration the impact of the inflation for the projected period of four years, from 2012 to 2015. The projected annual inflation rate is being projected to 2% for the specified time period. Since the cash flow and discount rate are both nominal, there is no need to include the inflation rate, unless we want to continue working with real amounts. In the later case, the nominal cash flow has to be converted first into real one by dividing it by the factor 1 i before discounting, and then a discounting should be made by using the real discount rate

Rd , which should be calculated taking into account the inflation rate i and nominal discount rate

Nd as follows:

1

11

NR

dd

i

(1)

Regardless of the method applied, NPV will be the same. The key issue is that the consistency dur-

ing the project evaluation should always be kept. For instance, if the cash flow has been projected with-out the inflation component (i.e. it is nominal), and the discount rate already contains the inflation com-ponent (i.e. it is real), the consistency in the project evaluation has to be provided either by embedding the inflation into the cash flow, or by eliminating the inflation component from the discount rate. The impact of the economic growth rate has to be equally considered within the model, too. According to the IMF, the annual projections of the economic growth rate for Republic of Macedonia, for the time period from 2012 to 2015 are 3.724%; 4.178%; 3.976%; and 3.978% respectively. If g is the economic growth rate [%], and d is the discount rate [%], then the discount factor DF t for the season/period t has to be adjusted/calculated using the following expression:

1

1 t

gDF t

d

(2)

The mathematical model of the investment project has been built up using Microsoft Excel

spreadsheets.

4. Monte Carlo simulation

Monte Carlo simulation is in silico problem solving technique (method) of an immense power and ver-satility, based on mathematical modelling of real phenomena being analyzed or evaluated by means of computing. A mathematical model is a simplified description of a given system, made by using mathe-matical concepts and expressions. Such models usually depend on more input parameters. These are processed by the model’s mathematics and, as a result, one or more outputs are gained. Monte Carlo simulation is usually used when the mathematical model is complex, nonlinear, or involves numerous uncertain parameters. On the other hand, computer simulation refers to a computer program, which at-tempts to simulate an abstract mathematical model of a particular system, for which a simple, closed-form analytical solution is not often possible. The common feature of all computer simulations is their attempt to generate a sample of possible representative scenarios for a model in which a complete enu-meration of all possible states of the model would be practically impossible (Wikipedia, 2012). Monte Carlo simulation is just one of many methods for analyzing the principle of stochastic uncertainty propagation, where the goal is to determine how random variation, lack of knowledge, uncertainty or errors affect the sensitivity, performance, or reliability of the system being modelled. It is categorized as a sampling method because it relies on repeated random sampling, which is performed by running mul-



tiple trial runs, using random variables. A random variable is a numerical description of the outcome of an experiment, which is not known in advance. The values of an input random variable usually follow a specific probability distribution, that most closely matches data we already have, or best represents our current state of knowledge. The underlying mathematical model, which is, by the way, deterministic by nature, explicitly incorporates uncertainty (stochastic dimension) in one or more input variables, whose values are randomly generated from probability distributions to simulate the process of sampling from the actual population. During the simulation, random input variables take on various values drawn from the corresponding probability distributions. A simulation can typically involve from 1000, 10000, to over 100000 iterative evaluations of the model in a single run. The obtained output results (the values of the corresponding output variables) are then tracked in order to perform a statistical analysis to see how the outputs vary as a function of the varying inputs (Raychaudhuri, 2008). The output results, i.e. data generated by the simulation can be represented as probability distributions (or histograms), or converted into error bars, reliability predictions, tolerance zones, and confidence intervals. Monte Carlo simulation consists of the following five major steps:

Step 1: Create a parametric deterministic model, 1 2, ,..., py f x x x ;

Step 2: Generate a set of random inputs, 1ix , 2ix , ..., ipx ;

Step 3: Evaluate the model and store the results as iy ;

Step 4: Repeat steps 2 and 3 for 1,...,i N ; Step 5: Analyze the results (i.e. resulting dataset

iy , 1,...,i N ), using various statistical methods. For the purpose of the paper, Monte Carlo simulation was carried out in Microsoft Excel by a

simple program written in VBA (Visual Basic for Applications) programming language, by running the underlying stochastic mathematical (financial) model 10000 times.

5. The results

The deterministic model takes into account single, most probable values, known a priori, for all input variables. As a result, single output values for NPV, IRR, PI and payback period have been calculated. Assuming the same values for the input variables, each time the same values for output variables (eco-nomic indicators) are calculated, no matter how many times the model has been run. The values ob-tained are as follows:

NPV = 367,95 € IRR = 9,64% PI = 1,34 Payback period = 5,5 seasons

Figure 3 is a graphical representation of the relationship between the NPV value and the discount

rate. Since the mathematical model was implemented in Microsoft Excel, the value of IRR (9.64%)

was calculated with the built-in IRR function. However, Figure 3 shows that the real value of IRR actu-ally lies between the values of 14% and 15%, which is considerably higher than the value being calcu-lated. The linear interpolation method estimates the value of IRR at 14.02960%. The diagrams on Fig-ure 4 show various aspects resulting from the deterministic model.



Figure 3 NPV vs. discount rate [%] Source: Authors

The parameters of NPV, IRR, PI and the payback period, calculated with the deterministic model,

indicate that the investment project is going to be a profitable one. But, will it be profitable, too, if input parameters take on different values, unlike the most expected ones, which is more possible situation in reality? How sure can N.N. be about the profitability of the investment project, taking into account many uncertainties intrinsic to the production and selling of strawberries? This question can be an-swered only by running the stochastic finance model, which can be derived from the corresponding de-terministic model, by assigning various probability density functions to certain input variables, as ex-plained previously. This way, uncertainty can be modelled, resulting in probability distributions of out-put parameters, rather than single values. During each run of the simulation, each input variable takes on a random value from the corresponding probabilistic distribution.

Figure 4a Benefit/Cost comparison Figure 4b Discounted Cash Flows (DCFs)



Figure 4c Cumulative Discounted Cash Flow

Figure 4 Graphical representation of the results of the deterministic model Source: Authors

Since there are numerous random input variables in the model, a single simulation run reflects the

instantaneous influence of numerous random combinations of values of the input variables upon the cash flow, thus resulting in a single tuple of values for NPV, IRR, PI and the payback period. However, we are going to focus on the NPV value only. By running Monte Carlo simulation, a probability distri-bution of NPV was gained, rather than a single value (Figure 5a). The histogram depicted on Figure 5a was prepared by applying the Freedman-Diaconis’ rule (Freedman & Diaconis, 1981) for estimating the optimal bin width, since it is based on inter-quartile range (IQR), rather than standard deviation (), and is thus proven to be less sensitive to outliers in data. In our particular case, the Freedman-Diaconis’ rule is better choice both than the Scott’s formula and the Sturges’ rule, which works well for a relatively small number of observations (30 < N < 200), but was found to be inaccurate for large number of obser-vations (N = 10000). Figure 5a shows that the project’s NPV can take values from the interval [-300 € … +900 €]. NPV can be negative, too, meaning that there is a certain amount of risk intrinsic to the ex-pected scenario. Most of NPV values (44.43% of all observations) fall within the interval [120 € … 300 €]. Almost 30% of all observations fall within the interval [220 € … 360 €]. The histogram can also re-veal several interesting facts related to NPV:

The modal, i.e. the most frequent value of 4.84% corresponds to NPV interval of [180 € … 200 €], meaning that in 4,84% of the cases, NPV will belong to the specified interval of values;

The median corresponds to NPV interval of [200 € … 220 €], which means that in 50% of all cases the value of the NPV will be below these values, and in the remaining 50% of the cases it will be above these values;

The average NPV value is estimated to 234 €; The standard deviation of NPV, i.e. the measure of the average decline of the particular values of

NPV from its average value, is estimated to 167,468 €.

Cumulative Discounted Cash Flow (DCF)

-330,79 €

201,10 €

-21,75 €

-700,00 €

-591,99 €

-577,67 €

-135,23 €

-51,22 €

367,95 €

-800 €

-600 €

-400 €

-200 €

0 €

200 €

400 €

600 €

October,2011

May, 2012 October,2012




Seasons

DC

F



Figure 5a Histogram of relative frequencies (probability

mass function) of NPV

Figure 5b Cumulative distribution function (CDF) of NPV

Figure 5 A probabilistic characterization of NPV Source: Authors

By using the Cumulative distribution function (CDF) of the NPV’s pmf function (Figure 5b), one

can estimate various probabilities related to measuring of risk, including the following ones:

P(X x): The probability of negative NPV is 9%, i.e. NPV will be positive in 91% of cases; P(X x): There are 50.26% chances that the NPV will be less than or equal to 220 €; P(a < X b): There is 9.85% chance that the NPV will be greater than 400 € and less than or

equal to 500 €; P(X > x) = 1 P(X x): There is 5.41% chance that the NPV will be greater than 500 €.

We can also make additional highlights by fitting the NPV distribution to any of the well-known probability distributions: Gaussian, Lognormal, and alike. The bell-like shape of the NPV histogram suggests that it could be mathematically modelled as a Normal distribution. In order to prove this as-sumption, a goodness-of-fit test of normality, e.g. either D’Agostino K-squared test, Jarque-Bera test or Lilliefors test, has to be applied. An alternative approach is to generate a normal Q-Q plot and make a visual inspection. Still, we are going to estimate the coefficients of skewness and kurtosis of the NPV distribution: the value of the former is +0.136 (a small asymmetry on the right side), and the value of the latter is +2.865 (somewhat less peakedness than the Normal distribution). Compared to the corre-sponding values of 0 and 3, which are distinctive to Normal distribution, one can conclude that the em-pirical distribution of the NPV random variable can be approximated with a Normal distribution with parameters N( = 234; = 167,468). Knowing this, one can also make additional estimations vis-à-vis 1, 2, and 3 bounds, as follows:

1: The probability that NPV value will fall within the interval [66,5 € … 401,5 €] is 68.26%,

2: The possibility that NPV value will fall within the interval [-100,9 € … 568,9 €] is 95.45%,

3: The chance that NPV value will fall within the interval [-268,4 € … 736,4 €] is 99.73%.

Yet another issue tightly connected to risk management of investment projects based on NPV esti-mation is the sensitivity analysis. It refers to evaluating the level of influence of a single input variable upon the values of NPV, for a given basic scenario. In other words, a single input variable is controlled by varying it over its full range of possible discrete values, and, for each discrete fixed value, a corre-sponding pdf of NPV is derived. Since the input variable “Yield per plant per season” is a critical both to revenues and variable costs, we are going to make a sensitivity analysis of NPV as a function of this variable. For each fixed value of “Yield per plant per season”, ranged over an interval [0,1 … 0,7] with a step of 0,05, a Monte Carlo simulation was run 1000 times and each time a new value of NPV was recorded. This way, a probabilistic distribution of NPV is gained for each fixed value of “Yield per plant per season” variable, drawn from its own domain. Figures 6a and 6b depict the results of the sensi-



tivity analysis. By analyzing these figures, one can conclude that, as values of “Yield per plant per sea-son”, input variable, ascend from 0.10 to 0.70:

the values of NPV output variable also increase from negative to positive values in a linear man-ner; this is expected behaviour since NPV is a linear function of all of its input variables,

the variability (standard deviation) of the probability distributions of NPV variable is getting big-ger i.e. the probabilities of realization of NPV decrease within much wider boundaries; as a re-sult, there is a shift from unimodality towards multimodality of NPV distribution,

the probability distributions of NPV variable are getting more flattened, and its relative frequen-cies decrease in a non-linear manner.

Figure 6a NPV distributions

Figure 6b Box & Whiskers plots

Figure 6 A family of probability distributions of NPV as a function of “Yield per plant per season” variable Source: Authors

Thus, the overall sensitivity of NPV as a function of “Yield per plant per season” input variable gen-

erally decreases with the rise of the values of the independent variable. Similar observations can be made with several other input variables for a given basic scenario, and then the results can be mutually compared to each other in order to determine which one of them is most influential on the values of the dependent variable (NPV).

Besides the expected (the most probable) scenario, which has been previously elaborated, two addi-tional basic scenario analyses can be performed using Monte Carlo simulation, namely, a pessimistic one, as well as an optimistic one. Each of these scenarios can include adjustments of input variable’s parameters in a single or both directions within the mathematical model, i.e. either vertically, through-out the full range of input variables, and/or horizontally, throughout the entire time period of observa-tion.

Conclusion

The described methodology has undoubtedly proved that risks in investment projects are both transpar-ent and analyzable. They can be identified, mathematically modelled and evaluated. Later on, the evalu-ated risks can be monitored and controlled as well. Risk management through Monte Carlo simulation can help management to better understand and assess the investment projects and their specific risks. The actual implementation of the Monte Carlo simulation has definitely demonstrated its full advan-tages over all other known risk and uncertainty management techniques in finance, due to more com-prehensive and wide-ranging analyses it offers. Still, even though Monte Carlo simulation has been pre-sent for more than 40 years, according to Graham & Harvey (2001), merely 15% of the companies use it as a methodology in the projects of capital budgeting, despite its numerous benefits. This is a conse-quence, mainly, of the implementation complexity. Yet another reason is the difficulty of constructing a mathematical model that will accurately reflect the real-world situation, i.e. identification of input vari-ables, their mutual interaction and their probability distributions. Nowadays, numerous excellent soft-ware packages, both dedicated ones (e.g. GoldSim, MCSim, Analytica etc.) and Microsoft Excel



add-ins (e.g. @RISK, Crystal Ball, Risk Solver, DFSS Master, RiskAMP, Risk Analyzer etc.), provide a full support to Monte Carlo simulation. Their usage can considerably facilitate not only the construction of suitable financial models, but can also produce outstanding output results, thus signifi-cantly helping in the process of decision making.

Acknowledgements

The authors would like to cordially thank Mr. Vladimir Kjorveziroski from Prilep, Republic of Mace-donia, a potential producer of Elsanta type of strawberries, for his great contribution and expertise dur-ing the preparation of the paper. SM

References

Freedman, D., & Diaconis, P. (1981). On the histogram as a density estimator: L2 theory. Probability Theory and Related Fields , 57 (4), 453-476.

Graham, J. R., & Harvey, C. R. (2001). The Theory and Practice of Corporate Finance: Evidence from Field. Retrieved January 12, 2012 from Duke University's Fuqua School of Business: http://faculty.fuqua.duke.edu/~jgraham/website/SurveyPaper.pdf

Hubbard, D. W. (2009). The Failure of Risk Management: Why It's Broken and How to Fix It. New Jersey: John Wiley & Sons.

Knight, F. (1921). Risk, Uncertainty, and Profit. Boston: Houghton Mifflin. Nemuth, T. (2008). Practical Use of Monte Carlo Simulation for Risk Management within the International Construction

Industry. Retrieved January 15, 2012 from Palisade Corporation: http://www.palisade.com/downloads/pdf/academic/Abstract_Nemuth.pdf

Raychaudhuri, S. (2008). Introduction to Monte Carlo Simulation. Proceedings of the WSC 2008 Winter Simulation Conference (pp. 91-100). Washington: IEEE Conference Publications.

Ross, S. A., Westerfield, R. W., & Jaffe, J. F. (2008). Corporate Finance (8th ed.). Skopje: Magor. Stempowski, R. (2002). Entwicklung von Bauprojekten. Graz: Nausner und Nausner Unternehmensberatung. Wikipedia. (2012). Computer simulation. Retrieved January 12, 2012 from Wikipedia:

http://en.wikipedia.org/wiki/Computer_simulation

Correspondence

Ilija Hristoski

University "St. Kliment Ohridski - Bitola", Faculty of Economics – Prilep Marksova bb, 7500, Prilep, F. Y. Republic of Macedonia

E-mail: [email protected]

STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 016-022 UDC 005.311.6 ; 004.891

Received: March 3, 2012


Influence Diagrams: Predictive Approach in Decision Support Systems

Amin Hosseinian-Far, Elias Pimenidis, Hamid Jahankhani University of East London, United Kingdom

Abstract Nowadays computerized Decision Support Systems (DSS) are essential for many private and governmentalorganizations. The main clients of such information systems are mainly managers at various levels, althoughother employees might need to make rapid decisions using such a system. There are various definitions and categorizations for DSS. A generalized delineation for Decision Support Systems would be: interactive infor-mation systems which assist the decision makers and planners in their business and decision making activi-ties. Influence Diagrams (ID) can be utilized for the design of DSS. Influence Diagrams are visual representa-tions of probability and uncertainty. Decisions, related objectives, uncertainties and elements can be visual-ised and inferred using ID. The main advantage of an ID is its provision of visual conceptual representation ofthe problem domain. ID can be employed in a DSS in various contexts, e.g. in environmental sustainabilityframework. In such a scenario, DSS designed with probabilistic networks and ID would assist the decisionmakers to precisely predict the effects of climate change policy plans. Hence integration and use of ID would provide a more accurate platform for planning. In addition to that, fewer planning failures and less investmentfiascos together with thorough sustainable growth would assist the current economy recovery. This paper re-flects on the potentiality of using Influence Diagrams and probabilistic inference for knowledge representationof DSS. Furthermore, the predictive nature of probabilistic extrapolation and probability inference in the con-text of environmental sustainability would be critically assessed. There are number of Integrated DevelopmentEnvironments (IDE) that provide built-in Influence Diagrams for the programmer. Moreover, available devel-opment environments providing built-in Influence Diagrams are introduced and compared. Keywords Influence Diagrams (ID), Decision Support Systems (DSS), Decision Trees (DT), Integrated DevelopmentEnvironment (IDE), Direct Acyclic Graph (DAG), Sustainable Development (SD), London Plan (LP).

Introduction

Influence Diagrams are expedient tools in designing Decision Support Systems (DSS) (O'Donnell & Meredith, 2008). With the assistance of the systems analyst, the decision maker would be able to design the DSS using various tools. These tools vary from conventional Entity Relationship Diagrams (ERD) and Universal Modelling Language (UML) to IDs. Historically, Decision Trees (DT) were used for modelling the decision scenario, but there are various advantages in using IDs. Similarly, the influence diagrams hide many details and the decision trees do not only look at the surface of the decision sce-nario but also expands the surface of the problem domain. There are various contexts where ID can be utilized for analysis, design and modelling of the DSS. ID is introduced in the followings. In section three, the difference between the Influence Diagrams and Decision Trees is outlined in more details. Finally, predictive nature of ID is presented by means of a case study.

Amin Hosseinian-Far et al. Influence Diagrams: Predictive Approach in Decision Support Systems 17


1. Influence Diagrams

‘Influence Diagrams’ are extraordinary for their lucid explanation and make a fascinating case for the use of graphical models to seize the logic for outlining the decision problem (Boutilier, 2005). It has had vast effects on Artificial Intelligence since its introduction. They were introduced with many different names and notations, but the fact is that introduction of IDs in Artificial Intelligence field has been very fascinating. One of the earliest contexts where the ID was used was in medical informatics and knowl-edge representation of medical decision problems (Horvitz, Breece, & Henrison, 1988). Since this in-troduction, the use of ID in medical scenarios is still maintained. Though, within the extended perspec-tive of the mentioned medical research, other techniques are also in use in parallel with probabilistic inference, such as: control systems, computer vision, dialog management, user interface design, multi-agent systems, and game theory. In addition to that, Influence Diagrams have had a great impact on probabilistic inference using its graphical representation of the problem domain. Probabilistic graphical models, as a general concept, have had a significant impact on almost all aspects of AI (Boutilier, 2005). These graphical models play a crucial role in various other contexts including but not limited to: inference and reasoning, planning and decision making, machine learning, pattern recognition and com-puter vision, computational linguistics and information retrieval (Boutilier, 2005). According to many scholars including Boutilier (2005), Howard and Matheson’s paper created a convincing discipline which was qualitative models which could confine the rationale behind dependencies which include probability processes. These models can be constructed upon decision scenarios which can be of a com-plex nature. Therefore, the trend in the Artificial Intelligence general field demonstrates that decision theories are now playing a fundamental role in the field, and studies and applications are growing rap-idly (Boutilier, 2005).

According to Howard and Matheson’s paper, advances in electronics and breakthroughs in general technology assist us in easier conceptualisation of real-world decision problems (Howard & Matheson, 2005). Mathematical equipment and formal metaphors are not proper means of communication in the social context as the training on the device or the math is not fairly there. The best means to overcome this challenge would the introduction of a method or description which can be understood by both hu-mans in the social context and computers or systems in the mathematical world. Influence diagram is a great instance for such a tool that can provide both technical and lay descriptions of the problem sce-nario. Understanding people in the social contexts using the Influence Diagrams depends on the degree of familiarity with IDs. Therefore, according to Howard & Matheson, this can act as a reasonable bridge between the qualitative social world and the quantitative mathematical world (Howard & Matheson, 2005). The rationale behind this bridging value of IDs would be their representation of three levels of specification of relation, function, and number in deterministic and probabilistic scenarios.

An influence diagram is a directed graph having no loops. It contains two types of nodes:

decision nodes, chance nodes.

The representation of decision and chance nodes depends on the notation of the IDE used. There are two major types of connecting nodes to each other:

Informational influences, where the decision makers will be able to set values or better to say af-fect other nodes. Within the ID, the nodes that are linked to the decision variables are connected by means of informational influence arcs.

Conditioning influences, as the name concerns, the conditioning arcs are the ones where a chance node (conditioned node) is involved. “The informational influence on a decision node represents influence into a chance node represent, as we have seen, a somewhat arbitrary order of condition-ing that may not correspond to any cause/effect notion and that may be changed by application of the laws of probability (e.g., Bayes’ Rule”) (Howard & Matheson, 2005).

Extended influence diagrams are Influence Diagrams where the graphs should be directed and have a high construct validity (Lagerström, Johnson, & Närman, 2007). The extended influence diagram repre-sents causal relations. Casual relations are the arcs where the status of another node is defined. In other words, one node defines another node using such a relationship. The nodes in an EID would be of three different types: “The utility node is the variable representing the goal of the decision maker e.g. interop-

18 Amin Hosseinian-Far et al. Influence Diagrams: Predictive Approach in Decision Support Systems


erability, The chance nodes are variables causally affecting other nodes, Chance nodes causally affect-ing interoperability might be the level of standardization or the level of semantic correlation” (Lagerström, Johnson, & Närman, 2007). The decision nodes are nodes that are in the hands of the deci-sion makers or the relative system analyst and can be altered according to policy. A simple decision would be a Boolean or a choice between two items or in general form two systems. (Lagerström, Johnson, & Närman, 2007).

1.1. Influence diagrams vs. decision trees

As stated earlier in this paper, Influence Diagrams can construct a decision problem, but the fact is that only the surface of the decision problem is covered. However, decision trees reveal more details of the decision problem. Each route in a decision tree represents probability branches. (Burton, 2007).

According to DecideIT user manual (2011), order of nodes in an Influence Diagram is not important as it is in a decision tree. This flexibility provides a priority for ID over the use of decision trees. Ac-cording to DecisionIT, this suppleness gives the decision maker the opportunity to adjust decision vari-ables at different stages of modelling accordingly.

Influence diagrams and decision trees can be converted to each other. There is slight difference in constructions, as the decision trees cannot be constructed having the chance node in the beginning of the diagram. For instance, chance node ‘Alpha’ should not precede the decision node ‘Beta’. The reason for this is that the decision variable cannot depend on a chance node. Similarly, adding nodes would be-come problematic, as adding the decision nodes which are dependent on a chance is not valid (Preference AB, 2011).

Majority of Decision Trees and Influence diagrams can be converted to each other; therefore consid-eration and selection of a specific tool might not be an issue for all problem domains; as both DT and ID can capture the logic of the decision scenario and develop the decision support system.

2. Financial assessment of the London plan using ID

The London Plan is the London strategic plan outlining the future of London in various contexts. The objective of one of the policy plans is to reduce the carbon footprint of London by 60% in 2050 com-pared to 1990 base.

The nodes in the Influence Diagrams are derived from the system map and the qualitative narrative extraction from the plan policy and discussion reports. The nodes that can be defined in Analytica are:

Decision nodes; theoretically, are the ones that are set by the decision makers. That means that the decision maker(s) can easily amend the values and functions for the decision node. The only decision node in the set in our ID is named ‘30% Reduction in CF reduction by 2030’. The values for this node are set for the percentages and the interim audits.

Table 1 Decision Variable Input

Year CF Reduction (%)

2010 15%

2015 20%

2020 25%

2025 30%

Source: Authors

are the chance variables that hold uncertain values. ‘UK_GDP’, ‘London_GDP’ and ‘CF_Costs’ are the only chance variables used in the model. The values for these variables are uncer-tain, but have a serial connection which will be outlined in the evaluation section. Despite the fact that

Amin Hosseinian-Far et al. Influence Diagrams: Predictive Approach in Decision Support Systems 19


these variables are defined as the uncertain or predictive variables, other node types could be utilized to represent their uncertain values. The values from them are derived from narrative extraction. For in-stance, the value for the UK GDP is as follows:

Table 2 UK GDP values

Year Value (billion £) Year Value (billion £) 2010 1474 2018 1762 2011 1510 2019 1798 2012 1546 2020 1834 2013 1582 2021 1870 2014 1618 2022 1906 2015 1654 2023 1942 2016 1690 2024 1978 2017 1726 2025 2014

Source: (International Monetory Fund, 2010) There is much different statistical information on London GDP. The reason for looking at the Lon-

don GDP in one of the chance variables is that the carbon footprint costs are a portion of the GDP of the area, London. According to Oxford Economic Report (OEF) report (2006), London produces 19% of the overall UK GDP. This is reflected in the UK_GDP and ‘London_GDP’ serial connection. The Lon-don Nominal GDP is derived similarly:

Table 3 London GDP values (International Monetory Fund, 2010)

Year Value (Billion £) Year Value (Billion £) 2010 280.06 2018 334.78 2011 286.9 2019 341.62 2012 293.74 2020 348.46 2013 300.58 2021 355.30 2014 307.42 2022 362.14 2015 314.26 2023 368.98 2016 321.1 2024 375.82 2017 327.94 2025 382.66

Source: (International Monetory Fund, 2010)

CF cost, which simply represents the cost for not investing and implementing the policy, is reflected as 2% the area’s GDP (Stern, 2007). Therefore the cost of climate change due to CF until 2025 would be 106.035 billion pounds.

Table 4 CF costs by 2025

Year Value(Billion £) Year Value(Billion £) 2010 5.6012 2018 6.6956 2011 5.738 2019 6.8324 2012 5.8748 2020 6.9692 2013 6.0116 2021 7.106 2014 6.1484 2022 7.2428 2015 6.2852 2023 7.3796 2016 6.422 2024 7.5164 2017 6.5588 2025 7.6532

Total 106.035

Source: Authors

These are the utility nodes which are known as objectives in the Analytica platform. There are two main utility functions in our influence diagram: economic benefit, which is derived from

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22 Amin Hosseinian-Far et al. Influence Diagrams: Predictive Approach in Decision Support Systems


The platform used for this analysis is Analytica. The reason for it is that Analytica provides intuitive platform for ID development. It is very easy to use and robust. It has been used by some famous com-panies such as British Gas, NASA, etc.

Conclusion

Influence Diagrams have intuitive predictive features which can be used in decision support systems. They can easily model the decision problem using its graphical representation and deterministic prob-abilities would assist in the predictions. These features are added values that conventional modelling tools such as ERDs lack. Although there are many similarities between ID and decision trees, Influence Diagrams illustrate the dependencies in a much clearer view. In addition to that, Influence Diagrams are more compact representations of the problem domains. Deterministic nature of influence diagrams would be appropriate for designing decision support systems. There are various Integrated Development Environments where Influence Diagrams can be built. Analytica is among the most powerful IDEs for modelling using IDs. SM

References

Boutilier, C. (2005). The Influence of Influence Diagrams on Artificial Intelligence. Decision Analysis , 2 (4), 229-231. Burton, D. (2007). Influence Diagrams & Basic Decision Trees. Retrieved February 20, 2012 from Weber University:

faculty.weber.edu/.../Influence%20Diagrams%20&%20Dec%20Trees.ppt Decision Processes Incorporated. (2012). Who We Are. Retrieved February 22, 2012 from Decision Processes

Incorporated: http://www.decisionprocessesinc.com/about.ivnu Horvitz, E., Breece, J. S., & Henrison, M. (1988). Decision Theory in Expert Systems and Artificial Intelligence. Approcimate

Reasoning , 2, 247-302. Howard, R., & Matheson, J. (2005). Influence Diagrams. Decision Analysis , 2 (3), 127–143. Hugin. (2012). Home. From Hugin: http://www.hugin.com/ International Monetory Fund. (2010). Nominal GDP list. Washington: International Monetory Fund. Lagerström, R., Johnson, P., & Närman, P. (2007). Extended Influence Diagram Generation. Proceedings of the 3rd

International Conference on Interoperability for Enterprise Software and Applications (pp. 599-602). London: Springer. Lumina. (2012). Who Uses Analytica? Retrieved February 22, 2012 from Lumina Decision Systems:

http://www.lumina.com/why-analytica/who-uses-analytica/ O'Donnell, P., & Meredith, R. (2008). Influence Diagrams as a Tool for Decision Support Systems. In F. Adam, & P.

Humphreys, Encyclopedia of Decision Making and Decision Support Technologies (pp. 474-481). London: IGI Global. Preference AB. (2011). DecideIT Decision tool User manual. Lidingö: DecideIT. Stern, N. (2007). The Economics of Climate Change: The Stern Review. London: Cabinet Office - HM Treasury. TreeAge. (2012). Home Page. Retrieved February 20, 2012 from Treeage Software, Inc.: http://www.treeage.com/ University of Pittsburgh. (2012). Home Page. Retrieved February 20, 2012 from University of Pittsburgh:

http://genie.sis.pitt.edu/ Correspondence

Amin Hosseinian-Far

University of East London 4-6 University Way, London E16 2RD, United Kingdom


STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 023-031 UDC 330.13:336.71(497.11)"2004/2011"

Received: October 15, 2012

Accepted: November 10, 2012

The Internal Determinants of Profitability in the Serbian Banking Sector

Milivoje Davidović, Jovana Ivančević, Tamara Antonijević University of Novi Sad, Faculty of Economics Subotica, Serbia

Abstract Bank profitability is the ultimate financial performance, which is a function of macroeconomic, bank-specific and market-specific factors. The combination of these factors decisively determines the degree of bank profit-ability, measured by return on equity (ROE) and return on assets (ROA). This article analyzes the causalitybetween internal factors and the profitability indicators of the banking sector of Serbia from 2004 till 2011. In-ternal indicators are selected based on CAMELS rating methodology as the theoretical concept. Internal indi-cators are accounting ratios, calculated based on relating individual items in the balance sheet and incomestatement. The focus of the analysis is on capital adequacy ratio, asset quality (non-performing loans and bad assets to classified assets ratio), management quality (operating expenses to total assets ratio), earning ca-pacity (return on equity and net interest margin ratio), liquidity (loans to deposits ratio and cash and securitiesto total assets ratio), and sensitivity to market risk (securities to total assets ratio). The results of regression analyzes indicated that the most important determinants of bank profitability in Serbia are asset quality andnon-performing loans (negative effect) and capital adequacy, net interest margin and liquidity ratio (Lq1) (posi-tive effect). Operating expenses and investment in securities have no significant effect on profitability. Keywords Profitability of banks, profitability indicators, CAMELS methodology, return on equity (ROE), return on asset (ROA)

Introduction

The banking sector of any country plays the decisive role in the intermediation of financial assets, trans-ferring capital from deposit-sufficient to deposit-deficient economic entities. Furthermore, the banking sector performs the maturity transformation of funds, taking short term loans (on the average) and grant-ing relatively long term ones. Bank performance assessment is extremely important at both micro and macro levels. At the micro level, performance assessment identifies the critical points of the operative function, acting as an early warning mechanism for change in corporate philosophy, change in man-agement, redesigning business process, higher quality of managing the bank’s assets, liabilities, risks and capital. At the macro level, performance assessment of individual banks and the banking sector is extremely important due to the banks’ role in financing economic growth, but also the stability of the financial system as well. If individual major banks’ performance is compromised, this results in esca-lated systemic risk and creation of conditions for banking panic and economic crisis.

As a segment of financial system, the banking sector bears the influences from the environment in its operation, but this operation is also determined by bank-specific factors. In fact, a bank’s efficiency and financial result depend on two groups of factors. The first group includes external factors, divisible into two major sub-groups: macroeconomic factors and market structure factors. Macroeconomic factors are, in fact, the ingredients of the overall economic milieu, systemic in character, affecting the business op-erations of all economic entities, including banks. They include interest rate, exchange rate, inflation, economic growth, financial structure (relationship between the banking market and the banking market. Market structure and competition are extremely important determinants of bank profitability, as the market power of individual market players depends on the degree of competition and concentration on

24 Milivoje Davidović et al. The Internal Determinants of Profitability in the Serbian Banking Sector


the market. The higher the degree of concentration and market power of market players, the higher their earning opportunities. However, viewed across sectors, if a banking sector shifts further from optimal competition (measured in the concentration indicators of assets, deposits and capital, and Herfindahl-Hirschman index), this market will operate increasingly suboptimally, most likely at the expense of ser-vices rendered to clients. In addition, suboptimal is an adverse factor in further development of the banking market. Unlike the external determinants of bank profitability, internal factors, as specific busi-ness factors, significantly determine the structure of banking operations, quality of the management’s decision, etc. These business indicators are derived from relevant quantitative relations between indi-vidual items of the balance sheet and the income statement. In fact, the matching accounting coefficient can determine the performance of banking operations to a great extent, so that they are taken as signifi-cant profitability factors: The most used internal factors are various liquidity indicators, capital ade-quacy ratio, loan portfolio quality indicators (non-performing loan indicator), indicators of sensitivity to risk, etc. Another very important factor is the growth potential of bank assets, which should yield ap-propriate profit.

The analysis in this article is based on establishing causal relations between profitability as the de-pendent variable and internal profitability factors as independent variables. The article consists of four segments. The first segment will present an overview of literature with predominant researchers’ opin-ions regarding the key determinants of bank profitability (both internal and external), and the results of individual studies dealing with this issue the second segment of the article will give a broader descrip-tion of data and applied methodology, both for selecting internal determinants and for the choice of the econometric research technique. The third section of the article is an empirical analysis, which could point to the most significant internal determinants of profitability in the Serbian banking sectors. The fourth section presents the research results, the authors’ relevant conclusions, and finally guidelines for further research into the determinants of profitability in the Serbian banking sector.

1. Literature overview

Given that profit is the driving force of bank operations, banking theory has devoted remarkable atten-tion to studying sources of profitability. Theoretical and empirical research shows that achieving a satis-factory degree of profitability is the key factor of satisfaction of all interest groups within banks. Profit, therefore, plays the key role in the depositors’ decision to offer the bank funds under mutually accept-able conditions. Studying bank profitability includes quantitative and qualitative analysis of financial reports. Quantitative analysis implies juxtaposing matching balance sheet items, resulting in standard profitability indicators: return on assets (ROA) and return on equity (ROE). “Bank profitability meas-ured in return on assets (ROA) is defined as after-tax profit in proportion to total assets; return on equity (ROE) is defined as after-tax return in proportion to total equity. ROA measures a bank’s ability to gen-erate profit based on assets, whereas ROE reflects the yield received by shareholders in proportion to their equity” (Ramadan, Kilani & Kaddumi, 2011, p. 181). “Legislators, regulators and analysts use ROA and ROE to assess the performance of the banking industry and forecast the market structure trends” (Gilbert & Wheelock, 2007, p. 515). Apart from these standard indicators, bank profitability is also measured with risk adjusted profitability indicators: risk adjusted return on capital (RAROC) and shareholder value added (SVA). Both indicators are based on the use of the CAR model as a measure of the economic level of capital exposed to risk. The CAR model is the bank final line of defence from possible insolvency (Vunjak & Birovljev, 2011, p. 247).

The determinants of bank profitability can be twofold: “external (macroeconomic and market spe-cific) and internal (microeconomic and bank specific)” (Ramlall, 2009, p. 161). Bank profitability is therefore a function of co-action of internal and external factors.” The internal determinants are factors decisively influenced by the bank management’s decisions. The quality of these decisions is identified as operative efficiency. Variables receiving the most attention when assessing operative performance are: capital adequacy, credit risk, efficient management and the size of the bank. On the other hand, ex-ternal determinants are the factors reflecting the legal and economic environment where banks operate and affecting their performance. These factors are: inflation, size of the industry, ownership status, competition and concentration” (Ramadan, Kilani & Kaddumi, 2011, p. 180).

Goddard, Molyneux & Wilson (2004) also analysed bank profitability in six European countries, be-tween 1992 and 1998, using the cross-section model and dynamic panel models. “The results of empiri-

Milivoje Davidović et al. The Internal Determinants of Profitability in the Serbian Banking Sector 25


cal analyses indicate that, regardless of the growing competition on the European financial market, there still exists a relatively constant profitability from one year to another. Evidence on the relation between the size of banks and profitability is inconclusive. Correlation between banks’ off-balance-sheet activi-ties and profitability is positive only in the UK, whereas in other countries this correlation is either neu-tral or negative. Correlation between capital to asset ratio and profitability is positive. The overall con-clusion of the study is that, despite the growing integration of European banking markets, national fac-tors still play a significant role in determining bank performance” (Goddard, Molyneux & Wilson, 2004., p. 378).

Ben Naceur & Goaided (2008) analysed the influence of internal and external factor on bank profit-ability in Tunisia, between 1980 and 2000. The aim of this study was to find an answer to the question: Why are some commercial banks more successful than others, and is this discrepancy brought about by internal factors under the management’s control, or are external factors more important for bank per-formance? The study resulted in several conclusions. “High net interest margin and profitability are characteristic of banks with a high level of capital and high operating costs. Other important internal determinants of the interest margin are loans, which have a positive and significant effect. The size of the bank in most cases has a negative and significant correlation coefficient, leading to a conclusion that diseconomy of scale is present” (Ben Naceur & Goaided, 2008, p. 127).

Ahmad & Noor (2009) analysed the profitability of Islamic banks in 25 countries between 1992 and 2009, by using the fixed effects model (FEM). The results of the research indicated that “profitability stands in a positive and significant correlation to the operating costs to assets ratio, capital, nonperform-ing loans to total loans ratio and the degree of the country’s economic development. Economic results for the periods of the Asian financial crisis of 1998 and the global financial crisis of 2008 are negative, showing that the profitability of Islamic banks was not affected by these financial crises” (Ahmad & Noor, 2009, p. 17).

2. Data and research methodology

A database is a dynamic set of accounting indicators, as the internal determinants of operation in Ser-bian banks. These indicators are derived from financial reports, i.e. balance sheets and income state-ments in the Serbian banking sectors at the quarterly level. The quarterly levels were observed as aver-age, and then reduced to the monthly level to get a required time series.

The research methodology means the application of regression analysis where the factors of impact are in fact the banks’ internal operating factors expressed through relevant indicators, whereas the re-sulting profitability of banks was measured with the standard profitability indicators: return on assets (ROA) and return on equity (ROE). As regards methodology for choosing relevant internal indicators, the authors used the CAMELS approach as theoretical basis in selecting internal indicators. This meth-odology was first adopted by the US legislators and regulators, for evaluating financial and managerial performance of American banks. The CAMELS is based on evaluating financial institutions’ capital standards, assets, assets, management, earnings, liquidity and sensitivity to financial risk. Application of this methodology requires prior calculation of relevant ratio, representing individual relations between given positions from the balance sheet, the income statement, and the cash flow analysis.

A detailed analysis of the CAMELS methodology implies a more detailed analysis of its key ele-ments (Davidović & Ivančević, 2012, pp. 3-5):

Capital represents its capability of credit expansion, but also for covering losses arising from dete-riorated asset quality. In the area of capital adequacy, CAMELS focuses on several key facts: (a) calcu-lating the capital adequacy ratio as a ratio between capital and risk-weighted assets; (b) a financial insti-tution’s ability to recapitalise amid the manifestation of operating loss; (c) the policies and procedures of establishing reserves for identified operating risks. Due to the importance of capital adequacy, but also in order to create conditions for equal competition between internationally active banks, the Basel Committee on Banking Supervision has prescribed the minimum capital adequacy ratio, allowing na-tional legislators and supervisor to set stricter (but not more lenient) requirements regarding capital ade-quacy.



Asset quality is exceptionally important for analysis, as the banks’ loan and investment portfolios are the most significant representations of their earning capacity. The evaluation within asset quality focuses on the following key areas: (a) assessment of growth potential and loan portfolio quality; (b) analysis of portfolio risk and volume of written-off loans; (c) establishing and implementing loan poli-cies, procedures and systems for loan classification, data gathering procedures and policies for write-off for accounts receivable. Assets quality is believed by many to be the most important determinant of Camels methodology for assessing performance and ranking banks (Jerome, 2008). According to Sundarajan and Errico (2002), assed quality is assessed based on four classifications: (a) intensity of change, allocation and degree of strictness of asset classification standards; (b) level and composition of non-performing assets; (c) the appropriate level of estimated reserves; (d) ability to identify and manage bad loans. The synthetic expression of asset quality usually used in quantitative analysis is the bad loans to total loan placements ratio. These are referred to as non-performance loans. Bad loans refer to the balance of total outstanding receivables of an individual loan (including the amount of arrears) where the debtor is 90 or more days in arrears from the initial maturity date; where the three-month interest (or more) is added to the debt, capitalised, refinanced, or its payment has been deferred; where the debtor is more than 90 days late, but the bank has assessed that the debtor’s capability of repaying debts is dete-riorated, and full repayment of the debt is compromised (National Bank of Serbia, 2010).

Management is an extremely important determinant of CAMELS supervision methodology. This segment includes the following significant attributes of a financial institution: (a) corporate manage-ment; (b) quality of management of a financial institution; (c) management information system (MIS); (d) internal revision and audit; and (e) strategic planning and budgeting. Technically, the management system is also evaluated by assessing the following characteristics: leadership, administrative capabili-ties, technical competencies, ability to act in volatile situations, adherence to regulations and laws, har-monisation of internal policies, and dedication to meeting the community’s legal requirements (Sunda-rajan & Errico, 2006). These are qualitative managerial performances contributing to the efficiency of a financial institution’s operative functioning. Quantitatively, one of the indicators (or a set of indicators) representing the efficiency of cost management is used to this end. These are cost-effectiveness indica-tors used for establishing relations between individual categories of costs and balance sheet assets. The most used ones are earnings per employee, costs per loan, costs per unit of lent money, and costs per average approved loan (Baral, 2005).

Earning capacity refers to the capacity of a financial institution’s assets to yield maximum returns at a tolerable level of risk, or minimum risk with an acceptable level of returns. Generating an adequate level of profit is a prerequisite for stability and growth of capital, satisfactory earnings for owners and managers, and also for the trust of the general public. The degree of supervision is particularly based on the variations in financial institutions’ profit: with the first signs of decline in profit as the synthetic ex-pression of operative efficiency, supervisors take appropriate actions to prevent possible bankruptcy. The supervisor’s response should be based not only past profit performance, but also on indicators as-sessing the bank’s financial potential and financial performance (Couto & Brasil, 2002). The bank’s current and prospective bank potential are assessed based on two standard profitability indicators: return on total assets – the ratio relating net profit to total assets and showing whether asset management is efficient in profit generation; return on equity – the classical profit rate relating net profit to equity, and indicating the bank’s efficiency in the employment of its own equity for profit generation. Efficient banks find it very simple to generate profit based on their own capital (Christopoulos, Mylonakis, & Diktapanidis, 2011).

Liquidity is another extremely important operative performance, referring to the bank’s capability to satisfy anticipated demand for funds, both on the depositors’ and the borrowers’ side. Every bank main-taining an adequate liquidity has the ability to (b) respond to sudden needs for liquid funds in opera-tions; (b) maintain financial health in the period of financial crises (as financial crises are initially mani-fested as liquidity crises); and (c) use the profit opportunity (if one arises) by investing liquid funds in investments with above-average profit potential. In principle, there are three components assisting a bank in maintaining liquidity: anticipated future inflows and outflows, the bank’s access to the interna-tional money marked and high liquidity assets that can be transformed into cash very quickly (Jerome, 2008). Measuring banks’ liquidity in the standard Camels rating systems is based on (Sundarajan & Errico, 2006):



unpredictability of the bank’s deposits, the bank’s dependence on interest sensitive assets, the bank’s methodological approach in relation to asset structure, volume of liquid assets that can be quickly transformed into cash, the bank’s ability to approach the interbank money market, efficiency of the central bank as the provider and last resort of liquidity.

Numerous liquidity indicators can be used for assessing bank liquidity: liquid assets to total assets ratio, liquid liabilities to total liabilities ratio (Vunjak & Kovačević, 2011); total loans to total deposit ratio; circulating assets to total assets ratio; cash and revocable deposits to total asset ratio; cash and marketable securities to current liabilities ratio; etc. Liquidity assessment is based not only on the cur-rent assessment, but also on the assessment of liquidity over time.

Sensitivity to market risk is a component of the CAMELS rating system focussing on the bank’s ability to identify, monitor, manage and control exposure to market risk. This rating segment refers to the assessment to variations in interest rates and exchange rates. This component is therefore focussed on monitoring and managing interest and exchange risk. The analysis of sensitivity to market risk is a natural extension of liquidity of analysis: to define the position of sensitivity to market risk, it is neces-sary to focus on the security ratio; in other words, management and loan analysts need to undertake a thorough liquidity assessment (Grier, 2007). Assessment of sensitivity to market risk is based on the analysis of the effects of changes in interest rate, exchange rate, and market price of securities on the quality and earning capacity of assets. The most used indicator is the securities to total assets ratio. This indicator establishes correlation between the movements of the position of securities in relation to total assets, where its movement is directly proportional to the degree of exposure to market risk. A larger portfolio suggests a higher degree of risk, whereas a fall in this ratio is appropriate response to the mani-festation of market risk (Christopoulos, Mylonakis, & Diktapanidis, 2011).

The evaluation and ranking of these six basic elements of CAMELS methodology are used for form-ing the composite index as a synthetic measure for assessing a bank’s financial and managerial perform-ance, and the index ranking is of key importance for assigning the rating. This is a qualitative rather quantitative rating, obtained based on the average of all ranked components (Trautmann, 2006). Com-posite rating is assigned on a scale of 1 to 5, where rating 1 denotes the strongest, and rating 5 denotes the weakest bank performance (Table 2). After assigning composite rating to each of the components, the ranking results are forwarded to the top management and the Board of Directors (Comptroller’s Handbook, 2007).

Table 1 Composite rating

Composite rating

Qualitative description of the composite rating

Rating 1

This rating is assigned to banks with superior performance, where most components were rated 1 and 2; the bank management is capable of determining weaknesses in operation, manage risks effectively, and are capable of deciding in complex situations; the bank’s fundamental management practices are exceptionally positive and mini-mum supervision is required.

Rating 2 This rating is assigned to banks with have strong overall financial performance; the lowest rating for individual per-formance is 3; the management and the Board of Directors have the cope with minor problems, management prac-tices are not strong enough, but with an adequate level of supervision, the bank’s performance can be satisfactory.

Rating 3

The bank has certain weaknesses in many area; special dedication is required to avoid illiquidity and bankruptcy; more than two components have been assigned grades below 3; the management is incapable of controlling the situation, and there are major discrepancies in relation to regulatory standards: risk management performance is unsatisfactory, and strong supervision is required; legislators’ or regulators’ guidelines may help the management identify weaknesses, bankruptcy is not likely, but the bank’s financial position requires supervision.

Rating 4

The bank has risky and unstable performance; the unsatisfactory performance results from managerial and finan-cial weaknesses; the management and the Board of Directors are incapable of solving problems, one or two com-ponents have been rated 5; breaches of legal and regulatory standards are manifest; corrective action and appro-priate supervision are required; lack of regulatory action may result in the bank’s insolvency.

Rating 5

The performance is extremely weak, with manifest risky and unstable performance and unsatisfactory risk man-agement practices; the management and the Board of Directors cannot solve the problem and handle the situation; the bank has negative earnings, and many components are rated 4 and 5; efficient regulation and supervisory assistance are essential for the bank to avoid bankruptcy, which is the most likely at this level.

Source: Trautmann, 2006

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monetary policy, the gross retail loans to equity ratio, which was standardised at the level of 250% at the time. It was at the time that the banks reached the limit of the gross loans ratio, as retail loans proved to be an excellent source of interest margin (with a very low percentage of non-performing loans and high interest rate), and in order to create space for further loan expansion, banks increased their equity. As a consequence, the indicator of return on equity was reduced in 2007, but subsequently recorded a return to the previous, upward trend. Both profitability indicators show a drastic decline in 2009, due to the impact of the global financial crisis. As a matter of fact, the majority of loans disbursed by banks in Serbia was indexed with a foreign currency rate clause, so that the sudden depreciation of the dinar, as a consequence of discontinued foreign direct investment, resulted in growing nominal debts and reduced debt servicing. Consequently, the quality of Serbian banks’ loan portfolios derogated, with a fall in the debt collection rate, and, as a reflection of these negative trends, profitability indicators have fallen by about 50%.

Other internal business indicators, such as capital adequacy (CA) ratio, net interest margin to assets (NIM/A) ratio, and non-performing loans (NPL) calculated by gross principle are also instructive for analysis. As a reflection of the marketisation of the Serbian banking sector and entry of foreign institu-tional investors (about 90% of capital is owned by foreign investors) into the banking system, these in-dicators underwent a period of qualitative prosperity. What is especially imposing is the solvency of banks in Serbia, measured by the capital adequacy ratio, which was more than three times as high as the 8% standard prescribed by Basel II in the pre-crisis period, and more than double of the standard set by the national supervisory regulations (according to the NBS, capital adequacy ratio in banks should be minimum 12%). After the expansion of the global financial crisis, the internal business indicators sud-denly deteriorated, which is obvious based on the downward trend in the capital adequacy ratio and the upward trend in the non-performing loans indicator. The net interest margin ratio showed a constant trend, which was to drop drastically in 2010. It is noticed that the deterioration of this parameter lags behind the deterioration of other parameters, which is logical, bearing in mind its intermediary character and the required adjustment period (as interests are charged within a given period corresponding with the loan maturity).

3.2. The results of regression analysis

Variables reflecting the CAMELS methodology were used for conceiving the model. The list of vari-ables, acronyms, calculation methods and expected effect on dependent variables (ROA and ROE) is shown in Table 2.

Table 2 Model variables

Variable Acronym Calculation method Expected effect return on assets ROA net profit / total assets dependent variable 1 return on equity ROE net profit / equity dependent variable 2 capital adequacy CA net profit / risk weighted assets positive non-performing loans NPL non-performing loans / total loans negative asset quality BA/CA bad assets / classified assets negative operative efficiency OE operative costs / total assets negative net interest margin NIM net total revenue / total assets positive

liquidity indicators LQ1 cash and liquid securities / total assets negative LQ2 total loans / total deposits negative / positive

sensitivity to market risk SECA securities / total assets positive / negative Source: The authors’ presentation

The expected effect of individual variables is based on the fundamental postulates of the banking

theory, where some of the variables exert ambivalent influence on profitability indicators, depending on the characteristics and the functioning model of the banking sector. The results of regression analysis are presented in Tables 3 and 4.



Table 3 Results of regression analysis (ROA is the dependent variable)

Variable Coefficient Standard error t-Statistic Prob.

C 0.009554 0.027468 2.347825 0.0072

CA 1.036484 0.065261 -2.559044 0.0057

BA_CA -1.112841 0.04024 -2.804172 0.0064

LQ1 0.626512 0.017248 3.632367 0.0005

LQ2 -0.004022 0.01521 -2.264415 0.7922

NIM 2.023694 0.103865 2.228123 0.0334

NPL -1.11552 0.070383 1.6413 0.0104

OE -0.077059 0.203405 -0.378844 0.7059

SECA 0.026318 0.079765 0.329938 0.7424

R-squared 0.930688 mean dependent variable 0.013121

adjusted R-squared 0.895694 S.D. dependent variable 0.008758

standard error of regression 0.007191 Akaike info criterion -6.930905

residual sum of squares 0.003879 Schwarz criterion -6.67046

log-likelihood 300.098 Hannan-Quinn criterion -6.826208

F-statistic 6.011197 Durbin-Watson statistic 0.395743

probability (F-statistic) 0.000006

Source: The authors’ calculation

The results of the first banking sector profitability model show that liquidity coefficient LQ2, operat-

ing costs and investments in securities are not significant profitability factors over the observation pe-riod. Net interest margin has the most significant influence on return on assets. In addition, a significant negative effect on the profitability of Serbian banks is also exerted by the liquidity coefficient LQ1 and net interest margin ratio. Significant negative effect on profitability showed by asset quality and non-performing loans, whereas operating costs and investment in security do not present a statistically sig-nificant influence on the profitability of Serbian banks.

Conclusion

Bank profitability is determined in the constellation of macroeconomic, microeconomic and market structure factors (competition, concentration and ownership structure). Considering this, it is not possi-ble to establish ex ante which group of factors makes the strongest impact on bank profitability; this process requires a comprehensive analysis of all factors. The focus of this research is the effect of indi-vidual determinants on the profitability of Serbian banks. Regression analysis indicates that exception-ally significant determinants of profitability of the Serbian banking sector include capital adequacy, net interest marking and asset quality, manifested in the bad assets to classified ratio and non-performing loans ratio. Furthermore, operating costs and investments in securities are not statistically significant variables. Such results indicate that it is possible to achieve superior performance of Serbian banks through strengthening capital base, but also appropriate risk management aimed at improving asset quality, i.e. reducing loss on non-performing loans to an acceptable level. In addition, given that the capital market is underdeveloped and inefficient, diversification of investment in securities aimed at boosting the loan portfolio would not be a good strategy in current conditions. If the results of both models are viewed, a comparative analysis indicates that return on total assets is a higher-quality, i.e. more stable indicator of bank profitability in Serbia, as its standard deviation is at a substantially lower level in relation to return on equity.

Further research could take several directions. On the one hand, it is possible to broaden the range of internal determinant in order to get a deeper insight into the impact of all internal factors on profitabil-ity. On the other, the analysis also needs to include as wide spectrum of macroeconomic and market structure factors as possible, thus increasing the comprehensiveness of the analysis, with a more valid



qualitative potential of the results. Moreover, research projects should encompass different methodolo-gies (such as regression analysis, factor analysis, structural equations model, etc.) so that the most influ-ential factor within these global groups of determinants can be established unequivocally. SM

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Milivoje Davidović

Faculty of Economics Subotica Segedinski put 9-11, 24000, Subotica, Serbia

E-mail: milivojed@ ef.uns.ac.rs

STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 032-040 UDC 005.52:005.954.6 ; 005.66

Received: April 12, 2012


Changes in the Human Resource Compensation Systems of European Companies – Based on the CRANET Research Result Analysis

Gizela Štangl Šušnjar, Agneš Slavić University of Novi Sad, Faculty of Economics Subotica, Serbia

Abstract In the period of turbulent change, economic and financial crisis, creative and loyal workforce features as animportant factor of success in any organisation. Establishing a compensation system as a key activity of hu-man resource management affects people’s workplace behaviour. As well as choosing the appropriate bonus schemes and offered benefits, setting the amounts of basic pay and bonuses gains particular importance intimes of crisis. The aim of this article is to present the basic elements of the compensation system in twentyEuropean countries based on Cranet Survey conducted in 2005, and from 2008 till 2010. Cranet is an interna-tional network of business schools enquiring into policies and practices of human resource management. Fa-culty of Economics Subotica has been a member of this organisation since 2008, when the field survey was conducted in Serbia. This article will present the results related to primary responsibility for compensation,data on the operating costs accounted for by labour costs, wage determination methods, and the elements ofbonuses and benefits. Data available for 2005 and the period from 2008 till 2010 will be processed with de-scriptive statistical methods and presented in graphic form. Indicating trends, similarities and differences in thecompensation system elements in the twenty observed European countries will serve as important informationto human resource managers for designing an appropriate compensation, incentive and benefit method. Man-agers of multinational companies can use the obtained results for devising specific strategies in the observed twenty countries. Keywords Compensation, pay, benefits, human resource benefits, Cranet, Europe.

1. Compensation system development

Developing compensation systems as the main human resource management (HRM) activity refers to monetary payments to workforce for the purpose of meeting an organisation’s targets and its employ-ees’ needs. Compensations include basic pay with various bonuses, incentives, formed at the individual, group or organisation level, and benefits, i.e. payments and intangible perks to employees.

Stone (2011, p. 437) argues that money plays a more complex role in individual motivation. Money enables satisfying most needs, and is at the same time a symbol of achievement, recognition and status. Payments, as a motivator most frequently used in organisations, are evidence of how much an em-ployee’s contribution to achieving the organisation’s objectives is valued. Employees often compare their own compensations with others’, and thus form their own satisfaction and performance.

Šušnjar (2005, p. 314) points out that “pay is the weekly wage or monthly salary, as remuneration comprising the major part of compensating employees for their work”. The compensation system usu-ally comprises the following elements: basic pay, bonus, compensation, premium and overtime pay. Most organisations use traditional pay calculation systems, such as seniority, hourly wage and wage

Gizela Štangl Šušnjar et al. Changes in the Human Resource Compensation Systems of European Companies ... 33


brackets. Alternative pay calculation systems include productivity based payments, competence model and wage bracket integration.

Incentive payment system is rewarding employees’ extraordinary performance. The basis of this compensation element is appropriately conducted assessment of the employees’ work performance. En-terprises may use various methods of individual, group and collective incentives. Individual incentives usually include payment per product unit, stimulating time saving, rewarding innovation and individual bonuses. In the case of group stimulation, the entire group’s performance is considered rather than that of individual members. Collective incentives imply rewarding all employees for the organisation’s achieved performance. The most frequently used methods include profit sharing, share options and goal sharing.

Developing a compensation system is an important activity for any organisation. According to Arm-strong (Armstrong, 2007, p. 651), it begins with business strategy analysis. An enterprise’s HRM is formulated based on this. Compensation strategy is developed and key rewarding principles are defined in accordance with the existing HRM strategy, compensation policies and practice, and all company shareholders. Implementation of the developed compensation system, reviewed and modified as re-quired, commences after final communication with all employees.

Milkovich & Newman (1999, pp. 10-14) classify the goals of compensation into three groups: effi-ciency, equality and legality. The efficiency of a compensation system refers to such a system that will improve employee performance, product quality and customer satisfaction, coupled with controlled la-bour costs. Equality is the key assumption of a compensation system, taking into consideration both the employees’ contribution and their needs, offering a compensation system transparent to everyone. Leg-islation should also be taken into consideration when building a system. As legislation in prone to fre-quent changes, so is the compensation system continuously modified and harmonised with regulations currently in force. In the authors’ opinion, four strategic principles are emphasised: internal consistency, external competitiveness, employees’ contribution and compensation system administration. Internal consistency refers to ratios between pays within the organisation, i.e. whether employees with higher qualifications, more experience and performing tasks of higher complexity will get higher wages. Ex-ternal competitiveness refers to forming wages that will be similar to those on the labour market, pro-viding compensation elements similar to those that the employees would receive for similar tasks from the organisation’s competitors. The employees’ contribution indicates the relative relevance of the em-ployees’ performance to their wages. It comprises the application of bonuses, performance-based wages and various incentives. Administration comprises applying compensation elements by trained managers.

Martocchio (2009, p. 42) argues that compensation strategy affects other activities of the HR De-partment. Compensation strategy principles should also include the activities of recruiting, selecting, performance management, training, career development and industrial relations. The author also stresses that the compensation system should take numerous stakeholders into account: employees, line manag-ers, top managers, trade unions and the Government. Martocchio points out that HRM experts should educate employees on the remuneration system elements, and continuously provide advice for designing optimum individual compensation systems. HRM experts should also assist managers in evaluating tasks and results of individual employees and setting objective basis for remuneration. HRM experts assist the top management in designing the basic compensation system in compliance with the regula-tion in force. HRM experts are also responsible for the appropriate implementation of legal regulations in formulating the basic wage, bonuses, incentive pays and benefits (Martocchio, 2009, pp. 48-49).

2. International differences in the compensation system

The formation of a compensation system is influenced by numerous external and internal factors. Stone (2011) lists the numerous factors affecting the remuneration system, including economic, political, le-gal, technological, environmental, cultural, demographic, social and corporate influences, coupled with industrial relations. As for internal factors, the author lists the impacts of strategy, structure, system, people and culture (Stone, 2011, p. 443). Milkovich & Newman (1999, p. 57) present a somewhat dif-ferent classification of organisation factors. In their opinion, the compensation system is influenced by technology in the form of organisation design, nature of tasks, requirements set before employees, HR policies, acceptance by the staff, and cost impact.

34 Gizela Štangl Šušnjar et al. Changes in the Human Resource Compensation Systems of European Companies ...


Many authors point out that, in addition to the existence of basic trends in the wage formation sys-tem worldwide, there are also notable differences among countries in the ways employees are rewarded for loyalty and time spent at work (Brewster, Sparrow, & Vernon, 2007, p. 123).

Literature on comparative HRM is dominated by the approach according to which national culture significantly affects the formation of the compensation system applied in the given country. National culture includes common attitudes, norms and beliefs of individuals within national frontiers. Compen-sation experts should be versed in elements of national cultures and their impact on compensation. The most commonly used classification of national cultures is based on Hofstede’s research, distinguishing the six main distinguishing dimensions of national cultures.

Power Distance (PDI) indicates the extent to which society accepts the fact that power in institutions and organisations is unevenly distributed. Cultures characterised by high power distance values prefer compensation emphasising employees’ status (for instance, seniority-based wages and managerial bo-nuses), whereas in countries with a lower value of this element employees prefer compensation forms emphasising staff equality, such as profit sharing.

Individualism vs. Collectivism (IDV) is a dimension related to the social framework where people are expected to care of themselves and their families. Individual achievement is highly valued in individual-ist countries, whereas individuals in collectivist societies expect society to care of them. Individualist cultures, therefore, prefer compensation rewarding individual performance and ability, whereas collec-tivist countries prefer group or collective incentive.

Uncertainty Avoidance (UAI) indicates the extent to which members of a given culture are willing to tolerate uncertain situations. Using bureaucratic compensation method, emphasising fixed pay and re-duced incentives are recommended in countries where individuals do not accept risk. Incentive wages are recommended in countries where people undertake risks.

Masculinity vs. Femininity (MAS) is a dimension pointing to a society’s dominant values. Men’s val-ues include persistence, affluence, entrepreneurship and innovation, whereas women’s values include quality of life, human relations, harmony and stability. Compensation systems in male-centred cultures often allow gender inequality, but also various payments and benefits to women during maternity leave. The dominant approach to wage formation in female-centred cultures is that based solely on job analy-sis and performance assessment, regardless of the executor’s demographic characteristics.

As the formation of wages and other compensation system elements is influenced by numerous ex-ternal and internal factors, comparing compensation systems is a highly complex task. Indicating trends, similarities and differences in compensation system elements can serve as important information to HR managers for designing appropriate wage, incentive and benefit systems. Managers of multinational companies can use the obtained results for devising specific HR strategies and expatriate incentives.

The aim of this article is to present the basic compensation system elements in twenty European countries based on Cranet surveys conducted in 2005 and from 2008 till 2010 (Cranet, 2005, 2011).

3. Cranet survey overview

Cranet is an England-based international network of business schools involved in comparative research into policies and practices of human resource management, established in 1989. Faculty of Economics Subotica has been a member of this organisation since 2008, when the field survey was conducted in Serbia. Their standard questionnaire comprises five main areas of human resource management: the role of HR Department, staffing practices, employee development, compensations and benefits, and em-ployee relations and communication. The questionnaires, translated into local languages, were filled in by HR managers or officers of companies with more than 50 staff members.

7914 organisations from 32 countries participated in the 2003-05 survey: Europe was represented by the following countries: Austria, Belgium, Bulgaria, Island, Cyprus, the Czech Republic, Denmark, Es-tonia, Finland, France, Germany, Greece, Hungary, Italy, Norway, Slovakia, Spain, Sweden, Switzer-land, the Netherlands, Turkey and the United Kingdom. Participants from other continents were: Aus-tralia, Canada, Israel, Nepal, New Zealand, the Philippines, Tunisia and the USA.

The last round of surveys encompassed a total of 6258 enterprises from 32 countries. European par-ticipants were Austria, Belgium, Bulgaria, Iceland, Cyprus, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Italy, Lithuania, the Netherlands, Russia, Serbia, Slovakia, Slovenia, Sweden, Switzerland and the United Kingdom, whereas the rest of the world was represented



by companies from Australia, Israel, Japan, the Philippines, South Africa, Taiwan, Turkish community on Cyprus, and the USA.

70 % of the sample of respondents in the entire Cranet survey conducted between 2008 and 2010 comprised private companies. About half of the sample (around 50%) accounted for organisations with fewer than 500 employees involved in industrial production (36%) for the national market (31%).

The survey in Serbia was conducted in late 2008, covering 50 enterprises from the entire country, with a total of 17064 employees. Most of the respondent companies (70%) were private, and 26% of them are producers.

As field research was conducted from late 2008 till mid-2010, the impact of the economic crises on obtained data varies, which requires a special and very careful analysis data analysis.

4. Data analysis

Out of the Cranet research into compensation systems across countries, this article will present data re-lated to primary responsibility for compensation, data on primary responsibility for compensations, data on the operating costs accounted for by labour costs, wage determination methods, and incentive ele-ments.

To provide a possibility of comparing data from 2003-05 and 2008-10 research period, the article analyses results from the following European countries: Austria, Belgium, Bulgaria, the Czech Repub-lic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Cyprus, Russia, Serbia, Slovakia, Slovenia, Sweden and Great Britain.

Primary responsibility for compensation system

Primary responsibility for wages indicates the decision maker on compensations in an enterprise. In some countries, the normal practice is for line managers to make these decisions; in some – line manag-ers after consulting the HR Department; in some – HR managers after consulting line managers; whereas in other countries HRM experts have full freedom in formulating staff wages. Figure 1 shows the 2005 data, whereas Figure 2 presents the 2010 data.

Figure 1 Primary responsibility for compensation systems in 2005. Source: Cranet, 2005, pp. 22-23



The dominant European HR management practice was cooperation between HR Departments and line management in determining staff wages. The HR Department does not make independent decisions on wages in any of the country observed. The greatest autonomy in these terms was enjoyed by HRM experts, where the decision was theirs alone in 37% cases. Unlike this, the lowest impact on compensa-tion was exerted by Slovakian HR Departments, where this decision was made by line managers in 57% enterprises. The situation in similar in Bulgaria, Estonia and Slovakia. Most dominantly, this important decision on staff remuneration is made by the HR Department after consulting line managers in France, where 52% respondents reported this. The situation is similar in Belgium, Greece, Italy, the Czech Re-public, Spain and the UK, whereas line managers predominantly make this decision after consulting the HR Department in the Netherland, as reported by 46% respondents. Primary responsibility is similar in Austria, Finland, Hungary, Germany, Slovenia and Sweden.

Figure 2 Primary responsibility for compensation systems in 2010. Source: Cranet, 2011, p. 26

The 2010 data also indicate that decisions on compensation are made in collaboration between the

HR Department and line management. Having consulted line management, HR Department decides on wages in Austria, Belgium, Denmark, Finland, France, Greece and the United Kingdom, i.e. countries with developed markets. Line management decides on wages after consulting the HR Department pri-marily in the Czech Republic, Germany and Sweden. It is interesting that in some countries, such as Bulgaria, Hungary, Slovakia, Slovenia, Russia and Serbia, this decision is made by the line manager. We can infer that, in former socialist countries, line management bears primary responsibility for mak-ing decisions on compensation, and the HRM Department still does not have an appropriate role in this process.

Comparing the 2005 and 2010 data, we can conclude that primary responsibility for determining wage systems is borne by HR Departments in cooperation with line managers. A significant difference, however, is noticeable between the practices of countries with developed markets, where this decision is mostly made after consulting line management on the one hand, and former socialist countries, were line managers have primary responsibility in this issue.

Operating costs accounted for by labour costs

Operating costs accounted for by labour costs is a significant indicator evidencing the impact of the HR Department in an enterprise. If labour costs account for higher share of operating costs, this means that HRM measures essentially influence the company’s performance. Figure 3 shows 2005 and 2010 data. Data for the USA and Australia is given for comparison.



Figure 3 Operating costs accounted for by labour costs (2005 and 2010). Source: Compiled based on Cranet, 2005, pp. 8-9 and Cranet, 2011, pp. 13-14

Operating costs accounted for by labour costs in most observed countries range between 30 and

50%. In 2005, this proportion was between 20 and 25%, mostly due to low wages. Monthly salary in former socialist countries amounted to between 300 and 1000 euros, while reaching up to 7000 euros (e.g. in Belgium). Thus, labour costs account for up to 60% of operating costs in Scandinavian countries with high welfare and social security. 2010 saw a growth in the share of labour costs in most countries, which is explained by a rise in wages and absolute values. A fall in the operating costs accounted for by labour costs only in France, Germany, Switzerland, the United Kingdom and Australia, most probably as the impact of economic and financial crisis.

Different wage determination levels

The diagram below shows ways of determining wages for manual personnel, i.e. whether wages are formed based on bargaining on individual, collective or national level. It must be noted that some re-spondents use several ways of determining wages.

Figure 4 Various wage determination levels for manual personnel in 2005 Source: Cranet, 2005, p. 50



The 2005 data show that bargaining for manual personnel at regional, national or industry level was present in most European countries. Although these data do not show, it must be emphasised that the usual bargaining level for managerial positions was either individual or collective.

Figure 5 Various wage determination levels for manual personnel in 2010 Source: Cranet, 2011, p. 75

Incentives

Having overviewed the 2005 and 2008-10 data, it can be inferred that the practice of performance-related benefits has been on the rise. In 2010, about 60 to 70% of surveyed companies used this form of individual incentive. In some countries, however, such as Cyprus, Great Britain and Norway, perform-ance-based pay is used in only 20 to 30% of companies.

Due to the nature of available data, this article will present a more detailed overview for collective incentives, more precisely shareplan, share options and profit sharing, as shown in Figures 6 and 7.

Figure 6 Collective incentives in 2005. Source: Cranet, 2005, p. 50

Based on the 2005 data, the dominant incentive was profit sharing, i.e. sharing a part of the profit

equally among employees, regardless of their personal performance. This form of collective incentive is primary in Finland, France, Slovakia, Spain, Switzerland and Sweden. The share options method is most widely used in Hungary, and shareplans in Norway.



Figure 7 Collective incentives in 2005. Source: Cranet, 2011, p. 69

The 2010 data indicate development of collective incentive, and a more pronounced domination of

profit sharing. Share options are most used in Belgium and Greece, while shareplan is characteristic of Denmark, Slovakia and the United Kingdom. Collective bargaining at company level is less developed than in other observed countries, and share options are used most.

Conclusion

Compensation system development is a vital human resource management activity, as it influences sig-nificantly and directly the employees’ attitudes and behaviour, as well as the company’s overall per-formance. As remuneration system formation is influenced by numerous external and internal factors, essential differences in compensation systems are noticeable.

Cranet surveys, conducted from 2003 till 2005 and from 2008 till 2010, provide an overview of dominant remuneration practices applied in most European countries. Data analysis indicates that deci-sions on wages are, in most cases, made based on consultations between HR managers and line manag-ers. The prime responsibility in countries with developed markets lies in the hands of HR managers (af-ter consulting the line managers), whereas in transition countries, decisions on personnel’s wages are made by line managers upon consultation with the HR manager. Operating costs accounted for by la-bour costs in most observed countries ranges between 30 and 50%, and wages are formed based on na-tional-level bargaining. A growing role of collective bargaining in this process is noticed in former so-cialist counties. Incentives are gaining importance, and most used incentive at the organisation level is profit sharing. The obtained data confirm Brewster’s scientific analysis (Brewster, Sparrow, & Vernon, 2007, p. 142), which argues that the modern forms of variable wages are gaining importance and con-tributing to companies’ overall performance.

Knowledge of differences between national compensation systems is vital for international human resource management. Knowledge of best practices of compensation systems will assist HR managers in building a nationally adoptable, efficient system of compensations increasing employee satisfaction and corporate overall performance. SM



References

Armstrong, M. (2007). A handbook of human resource management. London: Kogan Page. Brewster, C., Sparrow, P., & Vernon, G. (2007). International human resource management. London: CIPD. Cranet. (2005). Cranet survey on comparative HRM – International executive report. Cranfiled: Cranet. Cranet. (2011). Cranet survey on comparative HRM – International executive report. Cranfiled: Cranet. Martocchio, J. (2009). Strategic compensation: A human resource management approach. Upper Saddle River: Pearson-

Prentice Hall. Milkovich, G. T., & Newman, J. M. (1999). Compensations. Boston: Irwin-McGraw-Hill. Stone, R. J. (2011). Human resource management. Milton: John Wiley & Sons Australia. Šušnjar, Š. G., & Zimanji, V. (2005). Menadžment ljudskih resursa. Subotica: Ekonomski fakultet u Subotici. Correspondence

Gizela Štangl Šušnjar

Faculty of Economics Subotica Segedinski put 9-11, 24000, Subotica, Serbia

E-mail: susnjarg@ ef.uns.ac.rs

STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 041-049 UDC 005.334

Received: May 15, 2012

Accepted: September 21, 2012

Application of the Process-Based Organisational Model as a Basis for Organisational Structure Improvement in Crisis

Stefan Komazec, Ivan Todorović, Miloš Jevtić University of Belgrade, Faculty of Organisational Sciences, Serbia

Abstract All business operations are conducted through business processes, but majority of organisational structures isnot in compliance with business processes. Opportunities for business improvement are therefore very limited.Processes inevitably go through functional or other kinds of organisational units, and their purpose should beto connect those units. In practice, however, points that should feature as links between organisational unitsare the points where the process disruptions are made. Consequently, there can be serious problems in thefunctioning of an organisation. The purpose of this article is to show that applying process-based organisa-tional models for improving business does not necessary mean significant changes in an organisation. Thisarticle provides a practical tool for improving organisational structure, by demonstrating the application ofprocess-based organisational model in an organisation. Limitations of our study can be that the analyzed or-ganisation had a functional organisational structure, and was owned by the government. Keywords Organisational structure, business processes, process model, changes.

Introduction

Professional literature describes two diametrically opposite views on constructing organisational struc-ture methods. The first view is that organisational structure appears spontaneously, through organisa-tional operations. While operating, an organisation faces certain challenges and problems, and forms parts of organisation and relations between them over time, based on experience, for the purpose of finding the best way to meet these challenges. Recognizing this state, the authors define organisational structure as a “dictionary schedule” which has been developed and integrated as a mutual activation entity by individuals (Dulanović & Jaško, 2009). Selznick agreed with this standpoint and defined or-ganisational structure as a set of prepared solutions for emerging business situations (March & Simon, 1958). Indeed, this notion seems very logical, especially when companies are growing from being only small entrepreneurial enterprises that they were in the beginning. As well as the idea and leadership of a single man and informal relationships, entrepreneurial enterprises are characterized by a small number of employees. With the growth of entrepreneurial enterprises, relationships are formalized in order to ensure the most efficient execution of already established activities. As the number of employees grows, people who perform similar jobs are grouped into departments. Thus, department formation and formal-ising relations are the cornerstones of organisational structure development. In literature, this view is referred to as coactivation perspective (Janićijević, 1995). There is, however, an opposite opinion that organisational structure is understood as a functional element of an organisation rather than as its attrib-

42 Stefan Komazec et al. Application of the Process-Based Organisational Model as a Basis for Organisational Structure Improvement in Crisis


ute. As such, organisational structure should lead to better system management (Dulanović & Jaško, 2009). This view stems from the fact that organisations exist to fulfil specific goals and that organisa-tional structure should facilitate goal achievement. Organisational structure should be compatible with the concept of management, where elements of spontaneity cannot be identified. Organisational struc-ture should also possess an adequate stability level, which guides to efficiency. However, spontaneous way of developing organisational structure and inherent changes and adjustments do not guarantee effi-ciency. Mintzberg accepts this view and defines organisational structure as the way how organisations assign their work and tasks and achieve coordination of realization (Mintzberg, 1979). However, the second part of Mintzberg's definition (“achieve coordination of realization”) is very often ignored by practitioners and theorists. In their analysis of the organisational schemes, as graphical representations of organisational structures, experts often consider only vertical, hierarchical relations, as well as supe-rior-subordinate relations, control range and other elements that are directly related to management. However, missing parts of the scheme, horizontal relations among departments, which are necessary for functioning, are the often real cause of problems and carry significant potential for improvement. There-fore, Daft points out three key components of organisational structure definition (Daft, 2004):

1. Organisational structure represents formal reporting relations, including number of hierarchical levels and managers control range.

2. Organisational structure defines grouping of individuals into departments and departments into organisational units of higher hierarchy level.

3. Organisational structure includes creating a system that will ensure effective communication, co-ordination and integration of all the activities across the organisation.

The first two elements provide a structural framework for organisational functioning, while the third element relates to the pattern of interactions among employees. It is clear that managers are responsible for organisational structure design, because of their utmost influence. We have used term “utmost influ-ence” because there are many external factors that affect organisations and cause changes in organisa-tional structure, but management has a key role in analyzing and evaluating those factors and their im-pact. Finally, management sets goals and defines strategy, which directly affects the organisational structure. Management is constantly challenged to design an organisational structure that will lead to efficiency and effectiveness of employees (Robins & Coulter, 2005).

In today's turbulent and unpredictable business environment, organisations must be able to adapt to emerging changes. The necessity of good adaptation to changes is especially apparent in periods of cri-sis, when an economic activity decline in an entire country, region, or even world, as we can see these days. Companies must find ways to maintain, or even improve their market position, minimize costs and increase overall effectiveness and efficiency. One of the possible ways is to change the organisational structure. By implementation of radical organisational changes, such as change of organisational struc-ture, certain organisations have become able to adapt to the environment, others have improved their competitive position, while the third have created chances for better future (Kotter, 1996). However, uncertainty is inherent to any change, so fear of change is always present in organisations. There are many examples of changes which did not lead to expected results, despite large investments. Such ex-amples have just increased fear of changes within the organisations.

Even more dramatic situation regarding the necessity of organisational structure changes is present in Serbian economy. Despite the global economic crisis, which forces companies to adapt and change, many companies have not yet adapted to new market relations in our country. Although the transition from planned economy to market economy has completely changed the whole business environment, many companies in Serbia have not changed their organisational structures. Furthermore, there are many state owned companies where organisational structures are not adapted to the actual number of employees. In fact, many state owned companies have significantly reduced number of employees, be-cause of both decline in business volume and tendency to increase productivity. Organisational struc-tures, however, remained the same. For this reason, we can identify departments consisting of only one employee, managers with only one staff member, organisational units that exist only on paper, not in reality, and other relics of the past. It is clear that majority of local companies needed changes or im-provements in their organisational structure. It is clear that radical changes should be made, including improvements of organisational structure. A possible problem that may occur is the perception of organ-

Stefan Komazec et al. Application of the Process-Based Organisational Model as a Basis for Organisational Structure Improvement in Crisis 43


isational structure improvement as a radical change of the way the organisation is doing its business, usually with large resource requirements. In this article, we will use a specific example to show that organisational structure and, in general, functioning of the company, can be improved without radical changes, by using the process-based organisational model.

Processes permeate the entire organisation and also represent the paths of providing services to cus-tomers and generating new value. A process is a set of interrelated activities that transform inputs into outputs (ISO, 2007). Taking this definition into account, the purpose of each organisation can be pre-sented as usage of certain resources (inputs), and their transformation to desired outputs by using set of interrelated activities, which results in providing services or manufacturing products. With this in mind, it seems very logical and acceptable for the process-based organisational model to serve as a base for analysis of and improvements to functioning. Process-oriented approach considers the application of system of processes in organisation, their identification, mutual interaction and management (Filipović & Đurić, 2009). As mentioned before, organisational structure is usually designed through functional units and hierarchical relationships, which affects the way an organisation is managed. Organisations are controlled through vertical hierarchical relationships, and responsibility for outputs and results is given to functional organisational units, in most of the cases. Application of the process-based organisa-tional models leads to the identification of internal and external users, as well as to the identification of suppliers and other process stakeholders. It also leads to the establishment of an efficient system for managing key activities, with clear duties and responsibilities. One of the key outputs of the process-based organisational model, which can be the base for organisational structure improvement, is the iden-tification of key boundaries of activities within and across the functions.

Applying the process-based organisational model for improving organisational structure is especially important in the periods of crisis. At this time, many companies have financial problems, and there is a small likelihood that they can invest significant resources in organisational transformation projects. In terms of overall national economy insolvency, as present nowadays in Serbia, the process-based organ-isational model has a significant potential for improving organisational structure without investing large amounts of resources.

1. Project preview

The core business of the analyzed company is the transport of goods on middle and lower sections of the river Danube. There are also non-core business operations in company’s portfolio, but with insig-nificant share in its income. The company operates very well in terms of transportation capacity usage, but on the other hand, they have a problem with cost control, which limits their ability to gain higher profits. An appropriate level of profitability is precondition for making investments into fleet possible. This company used to have one of the largest fleets in this part of Europe, and in order to recover its market position, investments into fleet are necessary. Current fleet obsolescence is caused by the overall socioeconomic situation in Serbia. One of the company’s main problems is inadequate organisational structure. In the time of its greatest market power, company had employed over 2000 people, while to-day that number is only 399. Furthermore, there have been no radical changes that would adjust the or-ganisational structure to the reduced number of employees and ensure business efficiency of the com-pany.

2. Method

The project of improving organisational structure in this company required using various quantitative analyses and qualitative research methods. Quantitative analyses were based on financial statements. Indicators of solvency, liquidity and profitability were analyzed, in order to determine direction of or-ganisational structure improvement. The existing organisational structure was analyzed using notable theoretical models. We also conducted an analysis of human resources, which is an element that must be considered during the restructuring process. As a qualitative research method, observation is a very use-ful tool for hypothesis creation and modification, and it was widely used during the project. The con-sultant team had to get a deep insight in the company business model. Job classification, existing organ-isational structure, job descriptions, documentation of quality management system, and other documents of the company were analyzed in detail. This way, a broad picture of the company was created. How-



ever, the team had to become familiar with the way the organisation works and with the realization of business processes, in order to define specific organisational solutions. A whole set of meetings with top management was organised for this purpose. The consultant team also held a lot of individual interviews with sector executives, organisational unit of the highest hierarchical level in this case, in order to gather more information about the functioning of organisation’s main parts. There were also meetings with employees at lower hierarchical levels, department managers, and the purpose was to create solutions at the process level. These employees should be experts for processes in their departments. The described research method provides opportunities for problem identification, and serves as the base for improving organisational structure. For the purpose of this article, only analysis and improvement of organisational structure will be presented, although the result of the project has been a whole set of organisational solu-tions that are changing the way certain parts of the organisation are performing their activities.

Figure 1 Actual organisational structure of company Source: Authors



3. Analysis

At the highest hierarchical level, organisation is divided into four sectors: Commerce and Transporta-tion Sector, Techniques and Maintenance Sector, Finance and Accounting Sector, and Legal and Gen-eral Affairs Sector. Figure 1 shows the current organisational structure of the company. Core activities are performed in the Commerce and Transportation Sector, and support (non-core) processes are taking place in other sectors. The company's strategy is to minimize costs, which is essential for competitive-ness, because it is extremely difficult to differentiate services in the shipping industry. Strategy of the company, as fundamental purpose of operating and starting point in defining the organisation, is associ-ated with a corresponding organisational structure model (Jevtić, Čudanov & Krivokapić, 2012). There-fore, a function-based organisation design model is present in this company.

However, within the Techniques and Maintenance Sector, a divisional model of organisational struc-ture can be found, formed on a territorial basis. Within functional organisational structure, all similar activities (and their executors) are grouped into one organisational unit, which is commonly called sec-tor and managed by one person, who should be an expert in that field of business (Dulanović, & Jaško, 2007). Sectors of the analyzed company mostly consist of departments, although there is no consistency in names of organisational units, so we can find the same types of organisational units at the second and the fourth hierarchical levels. The number of employees in organisation is 399, and just over 50% of them work on core business, in the Commerce and Transportation Sector.

4. Problem identification

Each type of organisational structure has its good and bad aspects, and is applicable in certain situations, while in others it is completely inappropriate. One of functional organisational structure main disadvan-tages is its tendency to generalize efficiency and contributions of different organisational units to total business success (Hansen & Mouritsen, 2006). The organisation operates on the motto “parts are noth-ing, unity is everything” and it is very difficult to determine specific contributions of organisational units in this situation. The Commerce and Transportation Sector generates almost all income, while other sectors mostly generate costs. However, other sectors provide services to the Commerce and Transportation Sector, as well as to other organisational units, which is their contribution to the business results of the organisation. It is extremely difficult to determine success of these sectors, because their services are not sold on the market. Within functional organisational structure, a picture of how well organisational units in charge of support processes are operating is usually based only on cost data.

The largest problem of the company is the performance of the Technical and Maintenance Sector and its undefined relationship with the Commerce and Transportation Sector. The Technical and Main-tenance Sector generates high costs due to large number of employees (119, almost 30% of all employ-ees). There is also a problem with the lack of adequate records about services they had provided, and about their values. The main operating problem and cause of poor cost control in the sector is that, cur-rently, the whole process of vessel maintenance takes place within the sector, although it is not in ac-cordance primarily with the vessels’ “ownership” and exploitation. Namely, we can say that the Com-merce and Transportation sector “owns’’ and exploits vessels, but at the same time has almost no con-trol over the maintenance process. We can conclude that there is no process that will integrate and coor-dinate the activities of the two largest sectors in the organisation. However, maintenance process has potential to become coordination bearer, but the problem is that it currently occurs almost entirely within just one sector. Figure 2 shows the flow of vessel maintenance process and the position of sub-processes in certain departments.

Materials and spare parts procurement is executed in the Commerce and Transportation Sector, more precisely in the Commerce Department. However, this department was only executing procurement process, without analyzing the validity of procurement requests. The shipyard is the place where re-quests for materials and spare parts are specified. Thus, procurement process is presented on the dia-gram within that organisational unit.

Another problem which had been identified in the company is the problem in the procurement proc-ess. As we can see on the organogram, a group of people responsible for procurement of all necessary materials and components, from procurement of fuel and oil for boats to providing office supplies, is located in the Commerce and Transportation Sector, more precisely in the Commerce Department. Pro-



curement requirements from all over organisation are transferred to the Commerce Department. The Department executes procurement without analyzing procurement validity. Later, the Commercial Ser-vice sends all documentation to Finance and Accounting Department, for appropriate record keeping. Besides lack of procurement validity analysis, there is no proper control mechanism to check whether required materials and parts are actually used as it was specified in the procurement requests. There is no clear responsibility for procurement process in organisation. This situation causes an increase in pro-curement costs. The severity of this problem, especially in crisis, is emphasized by the fact that only costs of fuel purchasing in the shipping sector reaches the value of several hundred thousand euros.

Figure 2 Process flow of vessel maintenance Source: Authors

5. Solutions

The consultant team has designed solutions to these problems, using a process-based organisational model. Better control and reduced operating costs were the goals of designed solutions.

Maintenance Planning Department has been formed within Commerce and Transportation Sector, and is proposed to be responsible for certain parts of vessel maintenance process. The Department will consist mainly of current employees, transferred from Technical Department within the Technical and Maintenance Sector, while the others should be hired. The Maintenance Planning Department will per-form the first two phases of maintenance process, maintenance planning and organising vessel inspec-tion. Records of both, maintenance and repair operations performed on a vessel and regular servicing terms, will be kept in the Maintenance Planning Department. This department will then be able to coor-dinate the maintenance process with periods of ships usage. The two phases listed before are under complete control of the Maintenance Planning Department, but it is also planned that this department has an impact on next phases of the maintenance process, in order to improve process control. The Maintenance Planning Department must consist of experts in maintenance process. Before even being started, all work, repairs or overhauls, must be approved as justified by the experts from Maintenance Planning Department. The field work is also included in the job description of the employees from this department. This should prevent a longer stay of vessels in the shipyard, as well as the existence of fic-tional work. When maintenance operations are approved, the shipyard should make specification of ma-terials and parts required for works, which also must be approved by the Maintenance Planning De-partment. After that, specification should be sent to the unit responsible for procurement as a procure-ment request. Finally, the Maintenance Planning Department should perform technical acceptance of work, in order to verify that everything has been done as planned.



This way, opportunities for fraud will be reduced and tight control of maintenance cost will be intro-duced, which is necessary in a crisis atmosphere prevailing on today’s market. On the other hand, it will result in better coordination of the two largest and most important sectors in the organisation, through placing the entire process of maintenance planning in the Commerce and Transportation Sector, which will then be able to make plans in accordance with periods of capacity utilization. Furthermore, the Commerce and Transportation Sector will now be able to focus on its core business, performance of repair and maintenance operations. There is an idea that the two largest sectors could become independ-ent market entities in the future, which will operate separately on the market, offering their services to third parties. In that way, a kind of network organisation would be formed. The significance of process approach and sector coordination would then be emphasized, according to the opinion of certain authors that “the basic problem with management of any network is the necessity for demands and members’ activities adjustment and coordination.” (Jaško, Jaško & Čudanov, 2010)

Figure 3 The new organisational structure of company Source: Jaško, Jaško & Čudanov, 2010



Another change in organisational structure that is based on a process-based organisational model is the relocation of Procurement and Warehousing Jobs organisational unit from the Commerce and Transportation Sector to the Economic Affairs Sector (former Finance and Accounting Sector). For the purpose of emphasizing its importance, organisational units will also be renamed as Procurement and Storage Department. Fuel and oil procurement will continue to be performed within the Commerce and Transportation Sector, more precisely, in the Commercial Department, because of its specificity and close connection to everyday operations of the ships. The main reason for this organisational solution is the assignment of the whole responsibility for procurement process to only one organisational unit, the Procurement and Storage Department, which will then have a direct interest to establish tight control of supply process. In addition, locating this department in the Economic Affairs Sector should enable the analysis of purchased goods and services, corrective actions and improvements by applying accounting tools and techniques. The new organisational structure is presented in Figure 3. Conclusion

Under downturn business conditions, when crisis is spreading through the whole national economy, it is extremely important to adapt organisation to the new situation and find ways to continue profitable op-erations. One of the main goals is cost control, one of constructive elements of profitability. However, the real challenge for organisations affected by the crisis is to find effective and efficient ways to mini-mize the costs, since most of the attempts demand large investments. Consequently, changing organisa-tional structure is often avoided method for cost cutting, and alternative methods, such as reducing in-vestments, downsizing etc. are used instead.

In this article we showed on a specific example that improvement of organisational structure does not necessarily mean a complete organisational transformation, followed by huge investment of re-sources. If an organisation chooses to change its organisational structure, it does not have to change the way it works or its business model. By applying the process-based organisational model, the organisa-tion will be able to make improvements by harmonizing its organisational structure with the existing processes. Thereat, organisation does not have to change the processes, but only to reorganise organisa-tional units, using the processes as a base for coordination and harmonization of the activities within organisation. On the other hand, by defining clear responsibilities for processes and theirs result, organ-isational units will be forced to find appropriate mechanisms for process management and control, which should result in reduced costs.

Regarding the features of the process-based organisational model and benefits that it brings to the organisation, we can conclude that the process-based organisational model is one of the most effective models for improving organisational structure. SM



References

Daft, R. L. (2004). Organisation Theory and Design (8th ed.). Mason: Thomson-Southern Western. Dulanović, Ž., & Jaško, O. (2007). Organizaciona struktura i promena. Beograd: Fakultet organizacionih nauka. Dulanović, Ž., & Jaško, O. (2009). Osnovi organisacije poslovnih sistema. Beograd: Fakultet organizacionih nauka. Filipović, J., & Đurić, M. (2009). Osnove kvaliteta. Beograd: Fakultet organizacionih nauka. Hansen, A., & Mouritsen, J. (2006). Management Accounting and Operations Management, Understanding the Challenges

from Integrated Manufacturing. In C. S. Chapman, A. G. Hopwood, & M. D. Shields, Handbooks of Management Accounting Research 2 (pp. 729-752). Kidlington, Oxford: Elsevier.

ISO. (2007). Sistemi menadžmenta kvalitetom - Osnove i rečnik. Beograd: Institut za standardizaciju Srbije. Janićijević, N. (1995). Korporativna transformacija. Beograd: Timit. Jaško, O., Jaško, A., & Čudanov, M. (2010). Impact of Management Upon Organisational Network Effectiveness.

Management – časopis za teoriju i praksu menadžmenta , 16 (56), 5-13. Jevtić, M., Čudanov, M., & Krivokapić, J. (2012). The Impact of Business Strategy on Organisational Structure. Strategic

Management , 17 (1), 3-12. Kotter, J. P. (1996). Leading change. Boston: Harvard Business School Press. March, J. G., & Simon, H. A. (1958). Organisations. New York: John Wiley. Mintzberg, H. (1979). The Structuring of Organisation. Englewood Cliffs: Prentice Hall. Robins, S. P., & Coulter, M. (2005). Menadžment. Beograd: Data status. Correspondence

Stefan Komazec

Faculty of Organisational Sciences Ul. Jove Ilića 154, 11000, Belgrade, Serbia


STRATEGIC MANAGEMENT, Vol. 17 (2012), No. 4, pp. 050-058 UDC 005.346 ; 005.21:005.35

Received: July 10, 2012

Accepted: November 14, 2012

From Customer Satisfaction to CSR in Serbian Conditions: a Review of Literature and Business Practices

Dragan Ćoćkalo University of Novi Sad, Mihajlo Pupin Technical Faculty, Zrenjanin

Cariša Bešić University of Kragujevac, Technical Faculty Čačak

Dejan Đorđević University of Novi Sad, Technical Faculty “Mihajlo Pupin” in Zrenjanin

Srđan Bogetić Belgrade Business School, Belgrade

Abstract Customer-related and society-related business strategies are in many ways compatible; the most important, infact, key difference is in enhancing the spectrum of stakeholders which the company communicates and inter-acts with. This paper presents integration of customer satisfaction (CS) and corporate social responsibility(CSR) concepts into business models. The review of literature, some pieces of past research and business experiences from this field are presented as well. Current state, business experiences and usability of theseconcepts in a Serbian transitional context are analyzed; the review of acceptable business strategy whichrepresents the concept that satisfy customers’ requirements by integrating QMS, business excellence andrelationship marketing is presented as well. Keywords Customer satisfaction, CSR, strategic business models, Serbia.

Introduction

Customer satisfaction (CS) can be defined in different ways. According to Kotler (Kotler, 1994, p. 40), satisfaction is “the level of a person’s felt state resulting from comparing a product’s perceived per-formance (or outcome) in relation to the person’s expectations.” Satisfaction level is a function of the difference between perceived performance and expectation (Stahl, 1999). Loudon and Bitta (1993, p. 579) stated that satisfaction is “a kind of stepping away from an experience and evaluating it (…) one could have a pleasure. It was not as pleasurable as it was supposed or expected to be. So satisfaction and dissatisfaction are not emotions, they are the evolution of emotions”.

In the contemporary global economy and highly competitive business environment, it may be fatal for a business organization to be non-customer oriented. In fact, only those customer-oriented organiza-tions that can deliver value will survive in the modern business arena. To “make” highly satisfied and loyal customers, organizations throughout the world are striving to produce world class products and services of high quality. For a long time, CS has been considered the key success factor for every profit-oriented organization as it affects companies’ market share and customer retention. In addition, satisfied customers tend to be less influenced by competitors, less price sensitive, and stay loyal longer (Končar & Katai, 2005; Dimitriades, 2006).

Dragan Ćoćkalo et al. From Customer Satisfaction to CSR in Serbian Conditions: a Review of Literature and Business Practices 51


Leading companies make customer focus a key element of the company’s overall strategy to differ-entiate themselves from competitors. Putting the customer first becomes part of the corporate position-ing and differentiation in the market. The lack of management in CS strategies could be one of the many factors leading to an enterprise’s downfall. To effectively draft CS strategies, one must respect customer value and collect customer demands and then compare the importance and performance (satisfaction) between the collected customers’ demands (Naumann, Jackson, & Rosenbaum, 2001; Sudarević, Pupovac, & Zehetner, 2011). Concurrently, customer demands are not stagnant and cannot be manipu-lated by enterprises. Therefore enterprises must periodically diagnose and filter these demands to set reasonable strategies to insure the survival of CS activities.

Quite logically, enhancing customer-related business strategies represents enhancing to society-related business strategies. The main similarity is at the same time the main difference, too – they are both oriented to long-term alliances with stakeholders, to all those who, directly or indirectly, affect the operations of organizations, enterprises and companies. But, in society-related business strategies, satis-faction becomes a primary theme for top management in communications with a broad range of stake-holders. Focusing on satisfying high-value customers can be a powerful part of the company fabric be-cause it is meaningful to a broad set of stakeholders including employees, financial analysts, suppliers, and other allies. Various theories retrieve a different “lists” of stakeholders. According to Freeman (1984), there are six primary groups of stakeholders: owners (shareholders), employees; consumers; managers; suppliers; community. Donaldson and Preston (1995) classified stakeholders into the follow-ing groups: investors; employees; consumers; managers; suppliers; community; government; political groups. Branco and Rodrigues (2006) discuss the concept of comprehensive stakeholders, indicating eleven groups: shareholders (owners, employers); employees and managers (employees and employers) customers – customers; suppliers; creditors; competitors; branches of industry or profession as a whole; local communities; the State; international community and mankind as a whole; environment. It is, as noted, a multitude of interests and needs that they take into account when making business decisions, looking at their reflection and long-term effects.

The theories on corporate social responsibility (CSR) consider companies as determinants of social prosperity. The World Business Council for Sustainable Development defines CSR as “the commitment of business to contribute to sustainable economic development, working with employees, their families and the local communities” (World Business Council for Sustainable Development, 2001). Hence the fundamental idea of CSR is that business corporations have an obligation to work towards meeting the needs of a wider array of stakeholders (Clarkson, 1995; Waddock, Bodwell, & Graves, 2002). More generally, CSR is a set of management practices that ensures that the company maximizes the positive impacts of its operations on society or “operating in a manner that meets and even exceeds the legal, ethical, commercial and public expectations that society has of business” (Business for Social Responsibility, 2001). Organizations are expected to broaden their profit-driven perspectives and con-sider their impact on society and the natural environment (Starik & Marcus, 2000; Windsor, 2001; Sharma, 2002). The CSR literature reads as an argument based on moral considerations and defended by means of negative examples of corporate performance. It has often been argued that companies should contribute to social prosperity, because doing so is morally correct (Boatright, 1996). Another common argument is that the egocentric orientation of managers can lead to business scandals, such as ENRON, WorldCom and Parmalat and many others, that have increased the employment uncertainty of their host communities. Moreover, industrial accidents such as Bhopal, Chernobyl and Exxon Valdez, constitute a great danger for societies and the natural environment and are used as another reason to ad-vocate the social responsibilities of business.

1. Providing CS in Serbian conditions – the business model

1.1. Customer satisfaction and related concepts

Creating world class products and services, as a basic precondition for a company’s growth and devel-opment in a modern economy, are not functions of one organizational unit within the company, but rather the result of synchronized activities across the board, following the precisely defined objectives of the company (Ćoćkalo & Đorđević, 2006). The objective of an organisation should be to achieve and understand the optimum level of customer satisfaction. This field represents the base of at least three

52 Dragan Ćoćkalo et al. From Customer Satisfaction to CSR in Serbian Conditions: a Review of Literature and Business Practices


concepts: quality management, total quality management and business excellence. It also involves rela-tionship marketing, which is, conceptually, the subject of the wider analysis of this paper.

Quality components, such as solving complaints, cooperation of company’s representatives with cus-tomers, availability of products and services, cost and price policy and activities related to making con-tracts, have a great influence on customer satisfaction (Conca, Llopis, & Tarí, 2004; Evans & Burns, 2007). On the other hand, customer satisfaction influences the company’s characteristics, such as spreading positive information about the company and its services and products.

The term “relationship marketing” (RM) was first introduced by Berry (1983) in a services market-ing context. Many entrepreneurs do business by building and managing relationships without using the term relationship marketing. Relationship marketing, defined as marketing activities that attract, de-velop, maintain, and enhance customer relationships (Berry, 1983; Grönroos, 1994), has changed the focus of a marketing orientation from attracting short-term, discrete transactional customers to retaining long-lasting, intimate customer relationships. As the existing literature suggests, a business can build customer relationships by initiating one or several types of “bonds”, including financial, social, and structural (Lin, Weng, & Hsieh, 2003). However, much should be learned about the relationship be-tween the bonds initiated by a company and customer perceptions and behaviour.

Evans and Laskin present a model of effective marketing process. They define RM as “the process whereby a firm builds long-term alliances with both prospective and current customers so that both seller and buyer work toward a common set of specified goals” (Evans & Laskin, 1994, p. 440). The model is cyclic in form, with three sub-processes: (1) inputs (understanding customer expectations, building service partnerships, empowering employees and TQM); (2) positive outputs (customer satis-faction, customer loyalty, quality of products/services and increased profitability); (3) checking phase (customer feedback and integration). Little and Brookes (1997) enhance the explanation of the effective marketing process by saying that this concept is based on database management, interactive market communication and web marketing.

The concept of “total quality” extends well beyond the marketing customer-perceived view of qual-ity (Garvin, 1988) to include all key requirements that contribute to customer-perceived quality and cus-tomer satisfaction. Total quality broadens prior notions of quality in that it includes consideration of business processes for providing complete customer satisfaction on the full range of product and service needs, (Wayhan & Balderson, 2007; Chung, Tien, Hsieh, & Tsai, 2008). Essentially, the total quality concept is a general philosophy of management (Mohr-Jackson, 1998).

Business excellence presents a business strategy which demands complete commitment and accep-tance of concept from management (Terziovski & Samson, 1999; Irani, Baskese, & Love, 2004). The EFQM model of business excellence is based on eight principles. The criteria are: leadership, policy and strategy, people – management of employees, partnership and resources, processes, customer results – customer satisfaction, people results – employees’ satisfaction, society results – the influence on society and key performance results (EFQM, 2002). All of them are the basis for a self-evaluation the purpose of which is to evaluate the “maturity phase” of the organization and to focus on the problems of further business improvement (Dale & Ritchie, 2000; Motwani, 2001; Rusjan, 2005; Tari, 2005; Teo & Dale, 2007).

1.2. The model for providing CS acceptable in Serbian conditions

Companies from transition countries, including Serbia, have problems with the quality of their business practices and production productivity. Inherited inefficient production systems and transitional reces-sion, which are common to all countries in transition, affect these companies and can be blamed for their insufficient competitive capacity. The problem is especially obvious in companies dominated by autochthonous private capital. The reason why only a relatively small number of Serbian companies have implemented a quality system can be found in the difficult financial situation of the domestic economy and the fact that the implementation of QMS calls for considerable effort on the part of man-agement. What is of greatest concern is that, while almost all big companies have already implemented QMS, the majority of companies in Serbia are small to medium sized enterprises (SMEs). Taking all the above into account, it is not surprising that the concept of integrated management systems is the most common on the Serbian market while the elements of business excellence serve more as a theoretic-methodological base. The concept of relationship marketing exists, but only on a basic level and in a



small number of companies (those in foreign ownership). Furthermore, there are no clear indicators concerning this.

Modelling an acceptable concept that would satisfy customers’ requirements by integrating QMS, business excellence and relationship marketing seems a possible solution in Serbian transitional context; to create a model of a system for providing satisfaction of a firm’s customers needs (Ćoćkalo, Đorđević, & Sajfert, 2011). This model assumes a process approach, appropriate marketing research in the begin-ning and corresponding evaluation at the end. The model was created to enable easier management of these processes, with the aim of achieving business excellence and starts from at least two key pre-conditions:

1. Incorporation of the principles and criteria of business excellence – the input and output elements of relationship marketing in defining policy, and objectives and tasks in the organization (the sphere of planning quality in the future) shows the strategic determination of certified Serbian companies to satisfy the requirements of customers and other stakeholders.

2. Wide involvement of organizational and management structure in the processes of identifying, monitoring, measuring and analysing expectations would mean forging stronger bonds between current activities and strategic decisions, where a satisfied customer is the focus.

Here is presented the framework of the business strategy for satisfaction of customers' requirements acceptable for Serbian companies. Figure1 presents a model in the form of a cycle, in order to describe the business strategy which puts the focused modules (sub-processes) in the environment of customers and other stakeholders, whose objective is the progress of the whole organization. Such a business strat-egy is supported by business excellence and relationship marketing. It may be a matter of dispute which “ring” of support is “older” and/or more important. The authors of this paper believe that it is the matter of attitude, but their existence or the need for it has been shown (Ćoćkalo, Đorđević, Sajfert, Bogetić, & Bešić, 2011).

Figure 1 A business strategy for providing customer satisfaction

Source: Authors

2. Integration of corporate social responsibility in business models

Although the necessity of a socially responsible business performance is obvious, the practical rele-vance remains a challenge for CSR theories. In other words, the question is no longer “whether,” but “how” organizations can combine the principles of social responsibility with profit generation (Epstein & Roy, 2001; Smith, 2003). In this context, it is interesting to explore how organizations integrate CSR in their business models. The focus is concerned with the complexity and dynamics with respect to the managerial dependence on various resources (Sanchez & Heene, 2004) when implementing CSR, e.g. how corporate social responsibility is strategically managed. This contribution treats CSR strategy as a deliberate choice of activities that enable the organization to meet its objectives (Porter, 1996).

Management responsibility

Resource management

Product realization

Measurement, analysis and improvement

CCuussttoommeerrss’’ ssaattiissffaaccttiioonn

OOtthheerr ssttaakkeehhoollddeerrss



The strategic approaches to CSR are still in their infancy. In general, CSR theories state that ignoring social rules can lead to the emergence of new laws (Carroll, 1991) or that as a result of delay in respond-ing, social issues “may pile up and ultimately put the company in a position where it cannot function effectively in its traditional role as a producer of goods and services” (Ackerman, 1973). Statements of this kind do not provide a comprehensive explanation but only scratch the surface with respect to the strategic relevance of CSR. The strategic approaches to CSR pay closer attention to the different phases of the management process. Only few researchers have approached CSR theoretically from a strategic perspective in the sense that they look at how responsibility principles can be integrated in business models. Table 1 presents intersection of CSR strategies during the strategic management process, ac-cording to Dentchev (2005).

Even committed and experienced companies face some challenges when integrating CSR principles into their business models and these challenges can be, according to Dentchev (2005), classified into two groups: the (un)certainty of doing the right thing and the careful integration of CSR in business practice. Each group takes a certain number of problems. The first group is burdened with the following difficulties: (1) discovering the right people; (2) a formal agreement is not always possible; (3) difficul-ties to measure the relationship between CSR and corporate competitiveness. Managers need to evaluate if expectations are legitimate, and what are the best responsiveness tactics to legitimate expectations. Knowing this, managers should define very specifically the strategic importance of CSR either enhanc-ing the intangible assets of their firm (e.g. reputation, social capital, trust) or minimizing production costs (e.g. trough better risk management or improved production processes). There might be other con-tributions of CSR to the competitiveness of firms. Managers with specific expectations to the strategic importance of CSR, know how CSR is related to the business model of their company. This is important because of the need of careful implementation of CSR strategies.

Table 1 Strategic approaches to CSR

The strategic management process by Hitt, Ireland, Hoskisson (2003)

CSR strategies by Burke and

Logsdon (1996) by Epstein and Roy

(2001) by de Colle and Gonella (2002)

by Smith (2003)

Legal requirements Environmental/social

benchmarking of competitors

Stakeholder engagement

Life cycle assess-ment (of products, process,activities)

Social audit

Self assessment Resources

Stakeholder engagement

Centrality

Commitment from the top; Consistent

standards and enforcement

Fit between CSR strategy and the organization or

situation

Code of ethics Develop a

personalized strategy

Visibility

Proactivity Voluntarism

Sustainability actions

Sustainability performance

Training Communication

Audit and evaluation

Implement (well) CSR programs Measure social performance

Specificity Customer loyalty, Future purchases,

New products, New markets, and Productivity gains

Stakeholder reaction Long term corporate

performance (revision and reform)

Source: Dentchev, 2005

Strategy formulation

The internal environment

Strategic competitiveness Above-average returns

The external environment

Strategic intent Strategic mission

Strategy implementation



The second group of difficulties refers to the necessity of being careful when integrating CSR into business models. In other words, those managers who engage in CSR only because every other com-pany does so put their firms into a risk. Much time is needed to engage stakeholders and thus to gradu-ally develop a sound CSR strategy, especially if the firm has not used such a strategy before.

3. CSR practice in Serbia

The application and development of CSR concept is one of the major issues related to the development of the countries in transition, especially because legislative and executive authority is ineffective, econ-omy forceless, management generally inexperienced and exclusively profit-oriented, a significant num-ber of citizens live on or below the poverty line. Some peaces of research indicate that the practice of CSR implementation, not only in Serbia but also in countries in the region, is most developed in multi-national companies which do business on its territory, followed by certain Serbian companies which have been privatized recently and whose owners – at least majority owners – are foreign companies and then some autochtonous Serbian companies (considering capital). All of them are big or medium-sized enterprises, while in SMEs there are no significant examples of implementation of CSR concept. The CSR strategies of multinational corporations operating in Serbia are analyzed and evaluate by Nordgren and Đurić-Kuzmanović (2010). They analysed effectiveness and how they are able to mix profit with commitment to society as manifested in the evident increase of the quality of life of the employees and their families, as well as in the social development of local communities and the national economy.

The concept of CRS was formally introduced to the companies in Serbia about ten years ago. The business community, political elites, especially the media, have little knowledge of the essence of this concept, but it is the fact that there have been more and more talk about it lately, and that there are more people who have at least heard of it. In Serbia, the concept of CRS is understood mainly as a tool needed in the activities of marketing in view of building a reputation in the society, the media and gov-ernment, business partners and customers. Improving quality and working conditions, consistent with respect for the rights of employees, professional development, relationship and cooperation with cus-tomers, suppliers and trade unions are still doing so, on the sidelines.

Over the past few years, companies have realized the importance of incorporation of the CRS con-cept into sustainable business. CRS activities in numerous Serbian companies follow the tradition whose essence is the idea of charity and investing in the local community, which should contribute to the acquisition of their social and business prestige. The most favourable (but not the only one) way of Serbian management for investing in social community is in the same time the easiest to be charged – it is done through PR and it is related to different forms of donations.

In 2007, Serbia adopted recommendations of the UN in this field (the United Nations Global Com-pact), and the concept of CSR has become an integral part of the latest National Strategy for Sustainable Development. There were 30 companies and non-profit organizations that were included in the first round. The first members of the UN Global Compact initiative in Serbia are: BFC Lafarge, Holcim, Cisco Sy-stems, EFG Eurobank, Piraeus Bank, Societe Generale Bank, Credit Agricole, Meridian Bank, National Bank of Serbia, and Smart Kolektiv. Since February 2008, the Centre for Democracy – an or-ganization that is running the campaign has joined the UN Global Compact, entitled “Power of Social Responsibility”. In early June 2008, 14 companies, led by “Smart Kolektiv”, with the support of “Busi-ness in the Community”, joined in. They presented the public forum of business leaders in Serbia.

Among the organizations that have developed their own CSR programme or one of the most suc-cessful is B92 - broadcasting corporation that has been applying this concept since 2000. The program aims to improve the social and material conditions of life of vulnerable groups, especially persons with disabilities. Special contribution of this media company, in cooperation with civic organizations and several companies, is establishment of “safe house” for women. Some fields of work of B92 are health, vulnerable groups, youth and education, ecology, as well as media support of CSR events and promo-tion.

In 2010 Serbian Government decided to fulfil the requirements defined at summits in Lisabon and Copenhagen which were related to social inclusion, millennium developing aims and the aims which had been already defined by Strategy for Reducing Poverty and National Strategy of Sustainable Devel-opment for the Period 2008 – 2017. Therefore, the Government issued the Strategy of Development and Promotion of Socially Responsible Business Performance 2010 - 2015 (Serbian Government, 2010).



The framework of this Strategy is, by European Commission, proclaimed importance of social dimen-sion and the need for securing the state in which economic and social policies as well as employment policy act mutually in a positive way. Moreover, social protection must be observed as a productive factor. Parallel with strategic and at least verbal support of the state the implementation of ISO 26000 standard – Social Responsibility, which was translated and published in 2011 in Serbia, was started.

Conclusion

An important step in achieving customer satisfaction is fulfilling customer requirements in order to make informed business decisions. The model of business strategy for providing customer satisfaction, presented here, is harmonized with the requirements of QMS standards, as well as with relevant propos-als and criteria of business excellence, marketing requirements and the specific characteristics and re-quirements of Serbia’s economy.

Also, this paper shows that the integration of CSR into business model can be approached strategi-cally. However, it is a challenge for managers. Practitioners need to consider the logic of CSR for their business carefully and to take their time to construct an acceptable strategy. The complexity and re-source interdependence in realizing CSR strategies require well prepared planning.

Organizations that, in the present business conditions, manage to achieve a balance between profit-ability and ethical principles, including customers (and their satisfaction) and society (and its prosperity) in long-term alliances, can open ways that will enable growth and development. Ideas about corporate responsibility and sustainable development are global and they are growing into a worldwide move-ment. They should show respect and act locally and reflect the success of business organizations, com-panies and businesses but, at the same time, they should contribute to the community to become a better and happier place for living.

Since Serbia is one of the countries on the path to accession to EU, the issues and problems regard-ing the CSR are slowly emerging. Although CSR concept is being accepted very slowly, it is encourag-ing that, in Serbian conditions, this concept achieves the position which it deserves. SM

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Correspondence

Dragan Ćoćkalo

Mihajlo Pupin Technical Faculty Đure Đakovica bb, 33000, Zrenjanin, Serbia


Manuscript Requirements A paper must be written in text processor Microsoft Word. Paper size: A4. Margins: 3.0 cm on top and bot-tom, and 2.5 cm on left and right sides. As a guide, articles should be no more than 5.000 words in length. In case the paper exceeds the normal length, the Editors' consent for its publication is needed. Articles submitted for publication in Journal should include the research aim and tasks, with detailed methodology, presenting literature overview on the research object, substantiation of the achieved results and findings, conclusions and a list of references. Manuscripts should be arranged in the following order of presentation. First page: Title (no more that 10 words), subtitle (if any), autobiographical note (the author's full name, academic affiliation, telephone, fax and e-mail address and full international contact). Respective affiliations and addresses of co-authors should be clearly indicated. Please also include approximately 50 words of bio-graphical information on each author of the submitted paper. Second page: A self-contained abstract/summary/resume of up to 150 words, describing the research objective and

its conclusions

Up to ten keywords, which encapsulate the principal subjects covered by the article; and

A self-contained summary of up to 200 words, describing the article and its conclusions.

Subsequent pages: Main body of the text with headings, footnotes, a list of references, appendices, tables and illustrations. The paragraph parameters are: Font: Times New Roman, 10 pt, regular

Spacing: Before: 0, After: 0

Line Spacing: Single

Alignment: Justified

Indentation: Left: 0, Right: 0, Special: 0. Style: Normal (not Title, Heading1, Heading2,...,Body Text, etc!) Leave an empty line between paragraphs.

Headings: Headings must be short, clearly defined and numbered, except for Introduction and Conclu-sions. Apply at most three levels of headings. Please, leave two empty lines before headings and one empty line after. Font: Times New Roman, bold, 16 pt, centered. Section headings should be in bold with Leading Capitals on Main Words, Times New Roman, 14pt, bold, centered. Sub-section headings should be in italics, with Leading Capitals on Main Words, Times New Roman, 12 pt, bold.

All tables, graphs and diagrams are expected to back your research findings. They should be clearly referred to and numbered consecutively in Arabic numerals. They should be placed in the text at the appropriate paragraph (just after its reference). Tables should be centered. All tables must have captions. The title of your table should follow the table number. Tables should not be wider than the margins of the paper. Skip two lines before and after each table. Figures should be centered. All figures must have captions. The title of figures should appear immediately below the figure. The title of the figure should follow the figure number. Figures should not be wider than the margins of the paper. Skip two lines before and after each figure. Figures will not be redrawn by the publisher. Figures should be high-quality grayscale graphics (please, do not use colors): vector drawings (with text converted to curves) or 300 dpi bitmaps. Please do not supply any graphics copied from a web-site, as the resolution will be too low. In all figures taken or adapted from other sources, a brief note to that effect is obligatory, below the figure. One sentence at least referring to the illustration is obligatory. Mathematical expressions should be numbered on the right side, while all variables and parameters must be defined.

Copyright Articles submitted to the Journal should be authentic and original contributions and should have never been published before in full text, nor be under consideration for any other publication at the same time. Authors submitting articles for publication warrant that the work is not an infringement of any existing copyright and will indemnify the publisher against any breach of such warranty. For use of dissemination and to en-sure proper policing of use, papers and contributions become the legal copyright of the publisher unless otherwise agreed.

Proof Authors are responsible for ensuring that all manuscripts (whether original or revised) are accurately typed before final submission. One set of proof will be sent to authors, if requested, before the final publication, which must be returned promptly.

Referencing Guide The references should specify the source (such as book, journal article or a web page) in sufficient de-tail to enable the readers to identify and consult it. The references are placed at the end of the work, with sources listed alphabetically (a) by authors’ surnames or (b) by the titles of the sources (if the au-thor is unknown). Multiple entries by the same author(s) must be sequenced chronologically, starting from the earliest, e.g.:

Ljubojević, T.K. (1998). Ljubojević, T.K. (2000a). Ljubojević, T.K. (2000b). Ljubojević, T.K., & Dimitrijević, N.N. (1994).

Here is a list of the most common reference types: A. PERIODICALS Authors must be listed by their last names, followed by initials. Publication year must be written in pa-rentheses, followed by a full stop. Title of the article must be in sentences case: only the first word and proper nouns in the title are capitalized. The periodical title must be in title case, followed by the vo-lume number, which is also italicized:

Author, A. A., Author, B. B., & Author, C. C. (Year). Title of article. Title of Periodical, volume number(issue number), pages.

Journal article, one author, paginated by issue Journals paginated by issue begin with page 1 in every issue, so that the issue number is indicated in parentheses after the volume. The parentheses and issue numbers are not italicized, e.g.

Tanasijević, V. (2007). A PHP project test-driven end to end. Management Information Systems, 5 (1), 26-35.

Journal article, one author, paginated by volume Journals paginated by volume begin with page 1 in issue 1, and continue page numbering in issue 2 where issue 1 ended, e.g.

Perić, O. (2006). Bridging the gap: Complex adaptive knowledge management. Strategic Management, 14, 654-668.

Journal article, two authors, paginated by issue

Strakić, F., & Mirković, D. (2006). The role of the user in the software development life cycle. Management Information Systems, 4 (2), 60-72.

Journal article, two authors, paginated by volume

Ljubojević, K., & Dimitrijević, M. (2007). Choosing your CRM strategy. Strategic Management, 15, 333-349.

Journal article, three to six authors, paginated by issue

Jovanov, N., Boškov, T., & Strakić, F. (2007). Data warehouse architecture. Management Information Systems, 5 (2), 41-49.

Journal article, three to six authors, paginated by volume

Boškov, T., Ljubojević, K., & Tanasijević, V. (2005). A new approach to CRM. Strategic Management, 13, 300-310.

Journal article, more than six authors, paginated by issue

Ljubojević, K., Dimitrijević, M., Mirković, D., Tanasijević, V., Perić, O., Jovanov, N., et al. (2005). Putting the user at the center of software testing activity. Management Information Systems, 3 (1), 99-106.

Journal article, more than six authors, paginated by volume

Strakić, F., Mirković, D., Boškov, T., Ljubojević, K., Tanasijević, V., Dimitrijević, M., et al. (2003). Metadata in data warehouse. Strategic Management, 11, 122-132.

Magazine article

Strakić, F. (2005, October 15). Remembering users with cookies. IT Review, 130, 20-21. Newsletter article with author

Dimitrijević, M. (2009, September). MySql server, writing library files. Computing News, 57, 10-12. Newsletter article without author

VBScript with active server pages. (2009, September). Computing News,57, 21-22. B. BOOKS, BROCHURES, BOOK CHAPTERS, ENCYCLOPEDIA ENTRIES, AND BOOK REVIEWS Basic format for books

Author, A. A. (Year of publication). Title of work: Capital letter also for subtitle. Location: Publisher. Note: “Location" always refers to the town/city, but you should also include the state/country if the town/city could be mistaken for one in another country. Book, one author

Ljubojević, K. (2005). Prototyping the interface design. Subotica: Faculty of Economics.

Book, one author, new edition

Dimitrijević, M. (2007). Customer relationship management (6th ed.). Subotica: Faculty of Economics. Book, two authors

Ljubojević, K., Dimitrijević, M. (2007). The enterprise knowledge portal and its architecture. Subotica: Faculty of Economics.

Book, three to six authors

Ljubojević, K., Dimitrijević, M., Mirković, D., Tanasijević, V., & Perić, O. (2006). Importance of software testing. Subotica: Faculty of Economics.

Book, more than six authors

Mirković, D., Tanasijević, V., Perić, O., Jovanov, N., Boškov, T., Strakić, F., et al. (2007). Supply chain management. Subotica: Faculty of Economics.

Book, no author or editor

Web user interface (10th ed.). (2003). Subotica: Faculty of Economics. Group, corporate, or government author

Statistical office of the Republic of Serbia. (1978). Statistical abstract of the Republic of Serbia. Bel-grade: Ministry of community and social services.

Edited book

Dimitrijević, M., & Tanasijević, V. (Eds.). (2004). Data warehouse architecture. Subotica: Faculty of Economics.

Chapter in an edited book

Boškov, T., & Strakić. F. (2008). Bridging the gap: Complex adaptive knowledge management. In T. Boškov & V. Tanasijević (Eds.), The enterprise knowledge portal and its architecture (pp. 55-89). Subotica: Faculty of Economics.

Encyclopedia entry

Mirković, D. (2006). History and the world of mathematicians. In The new mathematics encyclopedia (Vol. 56, pp. 23-45). Subotica: Faculty of Economics.

C. UNPUBLISHED WORKS Paper presented at a meeting or a conference

Ljubojević, K., Tanasijević, V., Dimitrijević, M. (2003). Designing a web form without tables. Paper presented at the annual meeting of the Serbian computer alliance, Beograd.

Paper or manuscript

Boškov, T., Strakić, F., Ljubojević, K., Dimitrijević, M., & Perić, O. (2007. May). First steps in vis-ual basic for applications. Unpublished paper, Faculty of Economics Subotica, Subotica.

Doctoral dissertation

Strakić, F. (2000). Managing network services: Managing DNS servers. Unpublished doctoral disserta-tion, Faculty of Economics Subotica, Subotica.

Master’s thesis

Dimitrijević, M. (2003). Structural modeling: Class and object diagrams. Unpublished master’s thesis, Faculty of Economics Subotica, Subotica.

D. ELECTRONIC MEDIA The same guidelines apply for online articles as for printed articles. All the information that the online host makes available must be listed, including an issue number in parentheses:

Author, A. A., & Author, B. B. (Publication date). Title of article. Title of Online Periodical, volume number(issue number if available). Retrieved from http://www.anyaddress.com/full/url/

Article in an internet-only journal

Tanasijević, V. (2003, March). Putting the user at the center of software testing activity. Strategic Management, 8 (4). Retrieved October 7, 2004, from www.ef.uns.ac.rs/sm2003

Document from an organization

Faculty of Economics. (2008, March 5). A new approach to CRM. Retrieved July 25, 2008, from http://www.ef.uns.ac.rs/papers/acrm.html

Article from an online periodical with DOI assigned

Jovanov, N., & Boškov, T. A PHP project test-driven end to end. Management Information Systems, 2 (2), 45-54. doi: 10.1108/06070565717821898.

Article from an online periodical without DOI assigned

Online journal articles without a DOI require a URL.

Author, A. A., & Author, B. B. (Publication date). Title of article. Title of Journal, volume number. Retrieved from http://www.anyaddress.com/full/url/

Jovanov, N., & Boškov, T. A PHP project test-driven end to end. Management Information Systems,

2 (2), 45-54. Retrieved from http://www.ef.uns.ac.rs/mis/TestDriven.html. REFERENCE QUOTATIONS IN THE TEXT Quotations If a work is directly quoted from, then the author, year of publication and the page reference (preceded by “p.”) must be included. The quotation is introduced with an introductory phrase including the au-thor’s last name followed by publication date in parentheses.

According to Mirković (2001), “The use of data warehouses may be limited, especially if they contain confidential data” (p. 201).

Mirković (2001), found that “the use of data warehouses may be limited” (p. 201). What unex-pected impact does this have on the range of availability?

If the author is not named in the introductory phrase, the author's last name, publication year, and the page number in parentheses must be placed at the end of the quotation, e.g.

He stated, “The use of data warehouses may be limited,” but he did not fully explain the possi-ble impact (Mirković, 2001, p. 201).

Summary or paraphrase

According to Mirković (1991), limitations on the use of databases can be external and software-based, or temporary and even discretion-based. (p.201)

Limitations on the use of databases can be external and software-based, or temporary and even discretion-based (Mirković, 1991, p. 201).

One author

Boškov (2005) compared the access range…

In an early study of access range (Boškov, 2005), it was found... When there are two authors, both names are always cited:

Another study (Mirković & Boškov, 2006) concluded that… If there are three to five authors, all authors must be cited the first time. For subsequent refer-ences, the first author’s name will cited, followed by “et al.”.

(Jovanov, Boškov, Perić, Boškov, & Strakić, 2004).

In subsequent citations, only the first author’s name is used, followed by “et al.” in the introductory phrase or in parentheses:

According to Jovanov et al. (2004), further occurences of the phenomenon tend to receive a much wider media coverage.

Further occurences of the phenomenon tend to receive a much wider media coverage (Jovanov et al., 2004).

In “et al.", “et” is not followed by a full stop. Six or more authors

The first author’s last name followed by "et al." is used in the introductory phrase or in parentheses:

Yossarian et al. (2004) argued that…

… not relevant (Yossarian et al., 2001).

Unknown author

If the work does not have an author, the source is cited by its title in the introductory phrase, or the first 1-2 words are placed in the parentheses. Book and report titles must be italicized or underlined, while titles of articles and chapters are placed in quotation marks:

A similar survey was conducted on a number of organizations employing database managers ("Limiting database access", 2005).

If work (such as a newspaper editorial) has no author, the first few words of the title are cited, fol-lowed by the year:

(“The Objectives of Access Delegation,” 2007)

Note: In the rare cases when the word "Anonymous" is used for the author, it is treated as the au-thor's name (Anonymous, 2008). The name Anonymous must then be used as the author in the refer-ence list.

Organization as an Author

If the author is an organization or a government agency, the organization must be mentioned in the introductory phrase or in the parenthetical citation the first time the source is cited:

According to the Statistical Office of the Republic of Serbia (1978), …

Also, the full name of corporate authors must be listed in the first reference, with an abbreviation in brackets. The abbreviated name will then be used for subsequent references:

The overview is limited to towns with 10,000 inhabitants and up (Statistical Office of the Re-public of Serbia [SORS], 1978). The list does not include schools that were listed as closed down in the previous statistical over-view (SORS, 1978).

When citing more than one reference from the same author:

(Bezjak, 1999, 2002) When several used works by the same author were published in the same year, they must be cited adding a, b, c, and so on, to the publication date:

(Griffith, 2002a, 2002b, 2004) Two or more works in the same parentheses

When two or more works are cited parenthetically, they must be cited in the same order as they appear in the reference list, separated by a semicolon.

(Bezjak, 1999; Griffith, 2004) Two or more works by the same author in the same year

If two or more sources used in the submission were published by the same author in the same year, the entries in the reference list must be ordered using lower-case letters (a, b, c…) with the year. Lower-case letters will also be used with the year in the in-text citation as well:

Survey results published in Theissen (2004a) show that…

To credit an author for discovering a work, when you have not read the original:

Bergson’s research (as cited in Mirković & Boškov, 2006)… Here, Mirković & Boškov (2006) will appear in the reference list, while Bergson will not. When citing more than one author, the authors must be listed alphabetically:

(Britten, 2001; Sturlasson, 2002; Wasserwandt, 1997) When there is no publication date:

(Hessenberg, n.d.) Page numbers must always be given for quotations:

(Mirković & Boškov, 2006, p.12)

Mirković & Boškov (2006, p. 12) propose the approach by which “the initial viewpoint…

Referring to a specific part of a work: (Theissen, 2004a, chap. 3)

(Keaton, 1997, pp. 85-94)

Personal communications, including interviews, letters, memos, e-mails, and telephone conversations, are cited as below. (These are not included in the reference list.)

(K. Ljubojević, personal communication, May 5, 2008).

FOOTNOTES AND ENDNOTES

A few footnotes may be necessary when elaborating on an issue raised in the text, adding something that is in indirect connection, or providing supplementary technical information. Footnotes and end-notes are numbered with superscript Arabic numerals at the end of the sentence, like this.1 Endnotes begin on a separate page, after the end of the text. However, Strategic Management journal does not recommend the use of footnotes or endnotes.

CIP - Каталогизација у публикацији Библиотека Матице српске, Нови Сад 005.21 STRATEGIC managament : international journal of strategic managament and decision support systems in strategic managament / editor-in-chief Jelica Trninić. - Vol. 14, no. 1 (2009) - . - Subotica: University of Novi Sad, Faculty of Economics, 2009-. - 30 cm Tromesečno. - Nastavak publikacije: Strategijski menadžment = ISSN 0354-8414 ISSN 1821-3448 COBISS.SR-ID 244849927 Rešenjem Ministarstva za informisanje Republike Srbije, časopis "Strategijski menadžment" upisan je u regis-tar javnog informisanja pod brojem 2213, od 7. avgusta 1996. Rešenjem Ministarstva za nauku i tehnologiju Republike Srbije br. 413-00-435/1/96-01 časopis je oslobođen opšteg poreza na promet proizvoda kao publi-kacija od posebnog interesa za nauku.

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