ࡱ> iy %bjbj A{{ ...8f r.nhz(`v fffffff$PjmTf}>"`f(h616161 f61f6161N _}b0,0*l`0qf>h0nh`Vm/ Vm`}b}b8Vmb61ff61nhVm : ANALYSIS OF OVERALL AND PURE TECHNICAL EFFICIENCY OF TOURISMS IN EUROPE Vesna Prorok, PhD University of East Sarajevo/Faculty of Economics Alekse `antia 3, Pale, Bosnia and Herzegovina Phone: ++ 387 57 226651; Fax: ++ 387 57 226188 E-mail: vesna.prorok@ekofis.ues.rs.ba Neven `eri, PhD University of Split/Faculty of Economics, Business and Tourism Cvite Fiskovia 5, Split, Croatia Phone: ++385 21 430659; Fax: ++385 21 430701 E-mail:  HYPERLINK "mailto:nevenseric@efst.hr" nevenseric@efst.hr Ivan Peronja, PhD University of Split/Faculty of Maritime Studies Ruera Boakovia 37, Split, Croatia Phone: ++385 21 619-399; Fax:++385 21 619-499 E-mail: iperonja@pfst.hr Key words: tourism, technical efficiency, DEA, methodology JEL codes: C67, Z32 ABSTRACT The aim of this paper was to evaluate the overall and pure technical efficiency of the tourism of European countries for 2017 using the output-oriented Charnes, Cooper and Rhodes, and Banker, Charnes and Cooper data envelopment analysis methodology. The countries are divided into two groups: the European Union countries and non-European counties. For both groups of countries, we identified two input and two output variables. Input variables are identified using the principal component analysis method, starting from fourteen pillars that measure the performance of countries in the field of travel and tourism, and whose ratings are published by the World Economic Forum in their regular reports. Then, by defining two output variables (the total (direct and indirect) contribution of travel and tourism to gross domestic product, and a direct contribution of tourism to the rate of employment growth), we determined the relative efficiency of decision-making units within the formed homogeneous groups of countries, among which the group of Western Balkans countries was singled out. This analysis enabled us, based on relatively scarce potential, to propose guidelines for the tourism development strategy that will encompass the whole region of the Western Balkans. INTRODUCTION From a global point of view, the tourism is today one of the most dynamic and fastest growing economic activities, which, unlike other economic activities is characterized by a constant trend of growth and development, especially dominant in the last two decades. Tourism is recognized primarily as an opportunity for accelerated economic development of a large number of countries, but at the same time it requires the economic policy holder to face a range of challenges and willingness to undertake complex tasks in terms of creating the conditions for establishing cooperation between a large number of business entities, both nationally, as well as internationally. In addition to numerous economic and social opportunities, the development of tourism leads to greater international openness and geographic connectivity of the country, reflected in the prism of increased income of the population and the development of cheap air transport as an accompanying and necessary element for the successful development. The fact is that strong development of tourism increases employment opportunities and leads to an increase in income and living standards of the population as well as the elimination of a series of financial and institutional barriers. For this reason, it becomes clear that interest in its development exists and is particularly pronounced in low- and the middle income level countries. Tourism meets the requirements of the most realistic development concept, since the inputs necessary for competitive positioning at international level are not insurmountable, as is the case with technologically intensive branches. Tourism, moreover, is a labor intensive branch of the economy with high added value, and its development undoubtedly provides new jobs with the growth of employees' income, confirming that it is a branch that does not build its competitiveness on cheap labor. If we analyze the tourism offer of a country or region, it should be emphasized that it is most commonly based on natural and cultural-historical components that are most often presented in underdeveloped and passive areas. Today, the competitiveness of such resources is largely determined by innovative ideas and marketing strategies whose core task is to attract more tourists in order to ensure a balanced and unified regional development (Jakai-Stojanovi and `eri, 2018). From the point of view of demand, prospects and the potential of expanding tourism are relatively unlimited, which opens the possibility even for poorly developed economies to find their potentials for the market with already existing inputs and to achieve positive effects from the tourism with minimal investment. By its definition, tourism industry efficiency represents the tourism resources utilization level of the region (Luo and Qian, 2017). Starting from the definition of tourism efficiency, the purpose of this research was to measure and analyze the relevance of the investment efficiency in the tourism of European countries, with a particular focus on the Western Balkan countries. Our aim is to point out the problems and sources of inefficiency of the tourism of the Western Balkan countries and, accordingly, to propose guidelines for defining a development strategy that will contribute to increasing the impact of the tourism in these countries. Proposal guidelines will be based on an empirical study of best practices of various forms of a year-round tourist offer at the global level. In line with the innovative trends in the tourism, the proposal of measures for efficiency improvement would be based on the development of certain specialized tourism products. LITERATURE REVIEW Previous studies in this area are mainly focused on the assessment of tourist efficiency of provinces, regions, countries or even groups of countries, and were carried out in order to define tourism development strategies which would provide guidelines for easier and more efficient placement of existing or new tourism services to potential tourists. Botti, Peypoch, Robinot and Solonandrasana (2009) examined the tourism efficiency of 22 regions in France using the output-oriented data envelopment analysis (DEA) methodology. The analysis was based on the number of tourists as output variable and on 6 input variables: number of hotels, camps, parks, monuments, museums and miles of available beaches. Technical efficiency was achieved in 10 regions whose examples of good practice can serve as a benchmark for increasing efficiency in the rest of the regions. Similar analysis was performed by Barros, Botti, Peypoch, Pobinot, Solonandrasan and Assaf (2011). The analysis also included 22 French regions in the period from 2003 to 2007 and was based on the application of the two-stage DEA method. In the first stage, efficiency coefficients for each region were estimated based on two input variables (accommodation capacities and number of tourist arrivals) and one output variable (number of overnight stays).In the second phase, using regression analysis with the inclusion of variables representing tourist attractions (monuments, museums, parks, beaches, ski resorts and natural parks), the authors came to the conclusion that the efficiency is most dependent on sea exit and coast tidiness. For other regions that do not meet these conditions, a development strategy is proposed to increase the number of thematic parks, monuments, ski resorts and natural parks. The authors believe that the expansion of the tourist offer and the number of tourist attractions contribute to increasing the efficiency of the least developed tourist regions. Encouraged by the importance of tourism for the economy of a country and the growth of tourism market competitiveness due to the transition from mass tourism to the specific needs of tourists, Cracolici, Nijkamp and Rietveld (2008) analyzed the technical efficiency of destinations from 103 regions in Italy in 2001.Competitiveness in terms of technical efficiency was examined by using the parametric stochastic frontier analysis (SFA) and the nonparametric DEA method. The SFA method showed variability in terms of effectiveness across the region, indicating that regions with artistic and cultural content were better rated than mountain or regions positioned on the coast. Some lower efficiency scores were obtained by using the DEA method, which was characterized as a result of insufficient homogeneity of observed regions. Gucci and Rizzo (2013) applied two-stage DEA method in order to examine the extent to which UNESCO nominations determine the efficiency of tourist destinations and the flow of tourist travels in Italian regions for the period 1995-2010. The results showed that short-term UNESCO nomination had a negative impact on the efficiency of tourist destinations, while in the long run their impact was not statistically significant. This is because tourists value cultural content and natural attractions when choosing a destination, which UNESCO-nominated destinations mostly missing. In order for UNESCO nominations to have a positive impact on efficiency, it is essential that such sites are accessible to tourists, secured with material and immaterial infrastructure and enriched with cultural events. Encouraged by such analyzes, many authors have tried to evaluate the tourism efficiency of the less developed European countries in order to develop a strategy for increasing competitiveness, and thus the exploitation of both natural and cultural-historical components that are often located in underdeveloped areas. One such study was conducted by Tom (2014), who examined the efficiency of 8 regions in Romania in 2012 using the input-oriented DEA method with 4 input and 5 output variables. The analysis pointed to the efficiency of 5 regions. One region was technically inefficient due to the large number of tourist capacity in relation to demand, while 2 were inefficient because the tourist demand for accommodation facilities grew faster than supply. In the market of Asia similar analysis was performed by Bi, Lou and Liang (2014). The analysis evaluated the efficiency of 31 provinces in China through two stages defined as the capacity building stage and the benefit creating stage. In the first stage there were 19, and in the second 22 efficient provinces, but according to the overall estimate, only 6 provinces had efficiency scores equal to one (Beijing, Inner Mongolia, Shanghai, Henan, Qinghai and Ningxai), while the worst-rated province was Hebei with an efficiency score of 0.3890. Of the studies that included a group of countries from one or more regions, we will mention only those that were related to the analysis of the efficiency of European countries, given that such analyzes are the closest to the research that will be carried out in this paper. Cvetkoska and Bariai (2014) measured the efficiency of 15 European countries (Austria, Bosnia and Herzegovina, Bulgaria, Croatia, Cyprus, Czech Republic, France, Greece, Italy, Macedonia, Montenegro, Portugal, Serbia, Slovenia and Spain) for the period from 2004 to 2013 using Window-DEA analysis method. By selecting two input and two output variables, with the formation of 6 windows covering 5-year periods, the highest efficiency scores were achieved in 2004, while the lowest were achieved in 2011.According to the results, no country achieved full efficiency in all years and in all windows, but 10 out of 15 countries had an efficiency coefficient of over 0.95.Montenegro has been identified as a country with the lowest efficiency, while the highest scores recorded four countries: Italy, Cyprus, France and Spain. Kosmaczewska (2014) analyzed 27 members of the European Union in the period 2007-2009. The results have shown that richer countries have achieved a higher level of technical efficiency, while developing countries have reached a higher level of scale efficiency. This can be explained, inter alia, by the fact that tourism development is largely determined by investment in which richer countries abound. However, opportunities to improve efficiency in richer countries are less and less, given that the tourist services that these countries offer are already on an extremely high level of development. At the moment, this does not leave much room for investors, so they, in search of increasing their own capital, are increasingly turning to developing countries which they should consider as their chance. Developing countries or groups of developing countries which constitute one region, first of all, should recognize its competitive advantages over other countries and accordingly develop strategies that will attract investors and potential tourists. In a comprehensive list of research results, it is interesting to note the study of the authors Martn, Mendoza and Romn (2015), who have created a unique competitiveness index at global level and ranked 139 countries using the DEA method, by analyzing at the same their geographic position and national income. Describing differences in the characteristics of the best and worst-rated countries as well as geographic areas, this paper has made a significant contribution to the mapping of profiles that in the near future can be used by economic policy-makers to form strategies that will maximize their use and increase their tourism potential. All efforts in the literature so far enable future authors to use the DEA method in their research to a greater extent, which will, in combination with other parametric and nonparametric methods, provide relevant assessments of tourism efficiency (`eri and Ljubica, 2018). This study, as well, will be focused on evaluation of the efficiency of the tourism of European countries on the basis of which the Western Balkan countries' positioning in terms of efficiency would be conducted, with the aim of proposing guidelines for a strategy of tourism that would be oriented towards the expansion of tourist offer through innovative forms of tourism and the development of specialized tourist products (Jakai-Stojanovi and `eri, 2018). METHODOLOGY Charnes, Cooper and Rhodes (CCR) DEA Model The non-parametric DEA method was proposed by Charnes, Cooper and Rhodes (1978), with the intent to define a relative measure of the efficiency of the decision-making units in situations when a large number of output and input variables are available. The relative measure of efficiency using the DEA method is determined by the ratio of weighted output values and weighted input values for each observation unit individually. The units of observation are compared with each other by the formation of a linear programming model. The efficiency frontier is composed of observation units with best business practice, while the efficiency of all other units is determined on the basis of the distance from the defined efficiency frontier. Units at the frontier are considered relatively efficient, while those out of the frontier are relatively inefficient. A set of efficient units is viewed as a reference point for proposing improvements to relatively inefficient units (Prorok and Bonjak, 2018) Suppose we have  EMBED Equation.3 decision-making units (DMUs) and that each of the units EMBED Equation.3 ,  EMBED Equation.3  produces  EMBED Equation.3  outputs of the same type and of different values,  EMBED Equation.3  EMBED Equation.3 , using different  EMBED Equation.3  input values of the same type,  EMBED Equation.3  EMBED Equation.3 . The CCR model is designed to solve for each  EMBED Equation.3 -the decision-making unit  EMBED Equation.3  EMBED Equation.3  the optimization task of the relationship between the virtual output and the virtual input, in order to determine the weight coefficients for the output and input variables to which the value of the relationship will be maximized:  EMBED Equation.3  (3.1) with constraints:  EMBED Equation.3  (3.2)  EMBED Equation.3  (3.3)  EMBED Equation.3  (3.4) where:  EMBED Equation.3  - the relative efficiency of the  EMBED Equation.3 -th decision-making unit;  EMBED Equation.3  - the number of decision-making units;  EMBED Equation.3  - the number of inputs;  EMBED Equation.3  - the number of outputs;  EMBED Equation.3  - weight coefficients for input  EMBED Equation.3 ;  EMBED Equation.3  - weight coefficient for output  EMBED Equation.3 ;  EMBED Equation.3  - the amount of input  EMBED Equation.3  for the  EMBED Equation.3 -th decision-making unit,  EMBED Equation.3 ;  EMBED Equation.3  - the amount of output  EMBED Equation.3  for the  EMBED Equation.3 -th decision-making unit,  EMBED Equation.3 . Banker, Charnes and Cooper (BCC) DEA Model The presented CCR model assumes that the observation units achieve constant returns to scale, i.e. the increase in the value of the engaged inputs for a certain percentage results in the same or approximately the same percentage increase in the output. For this reason, the efficiency frontier formed on the basis of the CCR model has the shape of a convex cone. All observation units that are positioned at the frontier of efficiency are considered to have full overall technical efficiency that includes both pure technical efficiency and efficiency of scale. For the purpose of measurement of pure efficiency, Baker, Charnes and Cooper (BCC) proposed the extension of basic CCR DEA model in 1984. BCC model provides an assessment of pure efficiency, excluding the effect of the business scale. This is achieved in a way that the observed unit is compared only with other units of similar size. The mathematical formulation of the BCC model, unlike the CCR model represented by the expressions (3.1)-(3.4), includes an additional variable. The decision on whether the additional variable will be included in the numerator or the denominator depends on whether the general form of the BCC model is transformed into a linear programming model with output or input orientation. The purpose of the additional variable introduction is to set up a constraint on returns to scale and to provide that referent set is formed on the basis of a convex combination of decision-making units (Prorok and Bonjak, 2018). The general formulation of the BCC model is given by:  (3.5) With following constraints:  (3.6)  (3.7)  (3.8) Window DEA Analysis Window DEA analysis is a specific form of the DEA method that allows measuring of changes in the efficiency of the observation units over a given period of time. The method involved defining windows covering multiple time instances, where each observation unit is treated as a separate observation unit at different times. This allows the comparison of the efficiency of not only one unit over time, but also a comparison with other units that are covered by the defined window. The application of the Window DEA analysis allows to increase the number of observed decision-making units and to include the time dimension of the data by analysis. However, the main disadvantage of this method is that, by moving windows, certain time units are tested several times, while time instances corresponding to the first and last periods are tested only once, because they are only covered by the first and last window, respectively. In the continuation, we will use the Window DEA analysis to test the overall and pure technical efficiency of insurance companies in the Bosnia and Herzegovina and rank the most efficient decision-making units based on average efficiency estimates, both through windows and time periods. (Prorok and Bonjak, 2018) IDENTIFICATION OF INPUT AND OUTPUT VARIABLES FOR THE EVALUATION OF THE EUROPEAN COUNTRIES OF EFFICIENCY OF TOURISM Identification of input variables Although the DEA method represents a very good optimization technique for assessing the efficiency of the tourism, certain limitations still exist. Limitations occur in situations where a large number of input and output variables are available, which is relatively high compared to the number of observation units. As one of the ways to overcome this limitation, it is proposed to introduce in the model only those variables (inputs and outputs) that represent the basic components of the production process. In this way, the outcomes of the DEA method are not affected, but its power is increased. In evaluating the efficiency of the tourism of European countries, we tried to remove the DEA deficiencies by allocating countries to relatively homogeneous groups according to the resemblance of available tourist resources, and adjusting the number of defined input and output variables to the number of observation units. Key input variables were identified using the principal component analysis method, starting with the 14 pillars that measure the performance of countries in the field of travel and tourism, and whose assessments are published by the World Economic Forum (WEF) in its regular reports. In the 2013 report, the pillars were divided into three categories: Travel & Tourism (T&T) regulatory framework, T&T business environment and infrastructure, and T&T human cultural, and natural resources. However, since 2015, the pillars are divided into four categories: Enabling Environment, T&T Policy and Enabling Conditions, Infrastructure, and Natural and Cultural Resources. The index of travel and tourism competitiveness is formed on the basis of the aforementioned categories and it measures the performance of countries in the field of travel and tourism. Given that, above all, the countries, and then the tourist regions, differ according to the degree of tourism competitiveness achieved, it can be assumed that not all factors will have the same impact on the tourist performance of these countries. Therefore, the aim was to identify the main components that contribute to the tourism competitiveness of European countries,viewed in 2017. We divided the countries into two groups: the member countries of the European Union (28 countries) and countries outside the European Union (14 countries). Within both groups of countries we identified two principal components. For EU countries, we noted that all variables with the highest factor load, which make up the first component, have positive signs, namely: : 1) Business Environment, 2) Human resources and labor market, 3) Information and communication technology (ICT) Readiness, 4) International openness, 5) Environmental Sustainability, 6) Ground and port infrastructure; while the second component consists of variables: 1) Prioritization travel and tourism, 2) Price competitiveness, 4) Air transport infrastructure, 5) Tourist service infrastructure, 6) Natural resources, 7) Cultural resources and business travel, of which only the Price competitiveness variables has negative, while the other variables have a positive factor load. This would mean that if a given country as a tourist destination is rated positively for one attribute within the component that it determines, it will probably be highly rated by other attributes with the same sign within that component. On the other hand, those countries that are highly rated on any of the attributes with a positive sign are likely to be badly rated for some of the attributes with a negative sign. Specifically, in our case, countries from the EU 28, belonging to a group whose tourism is determined by the second component, are likely to have poor ratings in terms of price competitiveness, if they are highly rated by other variables that are mainly related to air transport, tourist infrastructure, and natural and cultural resources. Similar structure of the components is set out in non-EU countries, so we have the first component, whose structure is made up of the following group of variables1) Business Environment, 2) Safety and security, 3) Human resources and labor market, 4) ICT Readiness, 5) Prioritization travel and tourism, 6) International openness, 7) Price competitiveness, 8) Environmental Sustainability, 9) Ground and port infrastructure; 10) Tourist service infrastructure;; while in the structure of the other component there are three variables, namely: 1) Air transport infrastructure, 2) Natural resources, 3) Cultural resources and business travel. Identified main components were used as input variables in assessing the efficiency of the tourisms. After identifying the main components, we conducted a cluster analysis to group the countries into appropriate clusters according to the similarity of the tourism resources available to them. Hierarchical cluster analyzes and k-means cluster analysis was used to determine the number of clusters and to map the countries into the corresponding cluster. The analysis identified 4 groups of countries within the EU member states, as well as three groups of countries for non-EU countries. Based on the ANOVA analysis, we confirmed that the clusters thus formed, within both observed groups of countries, are statistically significant. The following table presents the results of the cluster analysis, with the definition of positively and negatively profiled components for each cluster individually. Table1. Countries grouped by clusters for EU and non-EU countries Clusters for EU countriesClusters for non-EU countriesC1C2C3C4C1C2C3Austria, Germany, United Kingdom, Ireland France, Italy, Greece, Portugal, Spain, Cyprus, Malta, CroatiaLuxembourg, Netherlands, Denmark, Finland, Sweden Belgium, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Slovakia, Slovenia, Bulgaria, RomaniaSwitzerland, Norway, Iceland Turkey, Ukraine, Russia Serbia, Bosnia and Herzegovina, Macedonia, Albania, Montenegro, Moldova, Georgia, ArmeniaPC1PC2 i PC1PC2 Countries where both defined components are negatively profiled and do not currently contribute to the tourism competitiveness of the countries. Similar to the profile defined by: PC1 i PC2PC2Countries where both defined components are negatively profiled and do not currently contribute to the tourism competitiveness of the countries.PC2PC1 Unlike the profile defined by:PC1Source: Prorok et al. (2017) Identification of output variables Development of tourism as an economic branch has a strong influence on both economic and non-economic aspects of development. Economic functions of tourism are reflected through direct influence on: social product and national income, development of underdeveloped areas, balance of payments and employment; as well as through indirect influence in a way that it stimulates the development of material production (industry, construction, agriculture) which have the role of supplier in the tourism industry. The non-economic or social aspects of tourism development relate primarily to the following functions: health, entertainment, cultural, social and political. The non-economic or social aspects of tourism development relate primarily to the following functions: health, entertainment, cultural, social and political. These functions stand out as crucial for the development of a country's tourism, and their neglect would lead to the question of the practicality of treating tourism as a development option. Given that this paper deals with the efficiency of tourism from an economic point of view, our analysis focuses on economically measurable indicators related to the total (direct and indirect) contribution of travel and tourism to GDP and to the contribution of travel and tourism to employment. The two variables mentioned above will be used as output variables when evaluating tourism efficiency using the DEA method. The World Travel and Tourism Council continually publishes data on total tourism contributions to GDP, which is methodologically consistent with the UNWTO (TSA RMF 2008) recommendation, and which is in line with GDP calculation in national accounts (uranovi and Radunovi, 2011).This aggregate is also an indicator of the shifting of social products from economically developed countries to those with a lower level of economic development. In this way, a more uniform development of the world's power is achieved. In addition to the contribution to GDP, it is important to look at the impact of tourism on employment, i.e. generating new jobs. The largest number of jobs is created, both within the hotel and restaurant sector, as well as within other supporting activities. It should also be emphasized that the tourism for years has enabled recruiting staff of different degrees of expertise and education. This trend continues, with growing demands for high flexibility staff, which have adequate competencies and capabilities to meet future tourism needs. Also, the World Economic Forum regularly publishes data on tourism's contribution to total employment, reflecting the real state of affairs, but not the real needs of the number of employees in the tourism, given that year-on-year needs become more and more. EVALUATION OF EFFICIECY OF TOURISM OF EUROPEAN COUNTRIES The overall and pure technical efficiency of the tourism of European countries was assessed by using the output-oriented CCR and BCC model. In the analysis we observed two groups of countries. The first group is made up of 28 member states of the European Union, while the other group consists of 14 countries outside the European Union, including the Western Balkans. For both groups of countries, we determined two input and two output variables when evaluating efficiency. Input variables for both groups of countries are obtained on the basis of rating of 14 pillars defined in the annual report of the tourist competitiveness of countries for 2017, and released by the World Economic Forum (WEF). We reduced the number of input variables by applying the principal component analysis (PCA) method. Thus, for both groups of countries, two input components are formed, based on a linear combination of 14 defined pillars. Given that the linear combinations for the observed groups of countries were different, it was necessary to observe separately evaluation of their efficiency. The structure of the principal components was presented in the previous chapter. For output variables, we used data on the total contribution of travel and tourism to GDP, and data on direct contribution of travel and tourism to the rate of employment growth, where both variables were expressed in percentages. Output variables data referred to 2017 and were downloaded from the World Data Atlas site (HYPERLINK "https://knoema.com/atlas"https://knoema.com/atlas). Tables 2a and 2b show the results of the overall and pure technical efficiency of EU countries and non-EU countries using the output-oriented CCR and BCC DEA model. Countries are ranked according to the results of tourism efficiency. For analysis purposes, we used the DEA-Solver-LV software package. Table 2. a) Evaluation of overall and pure technical efficiency of EU countries in year 2017, using output-oriented CCR and BCC DEA model Country (DMU)CCR efficiency scoreRankBCC efficiency scoreRankCyprus1111Malta1111Bulgaria1111Portugal0.9911411Croatia0.9225511Greece0.7269611Estonia0.6906711Ireland0.6901811Poland0.6761911Sweden0.61161011Spain0.58041111Romania0.54931211Slovenia0.53361311Netherlands0.5071411Italy0.47971511Latvia0.41261611Czech Republic0.3891711Finland0.37221811Hungary0.35871911Denmark0.34532011Slovakia0.34212111Luxembourg0.27562211Belgium0.25112311Lithuania0.21972411Austria0250.000125France025026Germany025026UK025026Source: Authors' calculations According to the results presented for EU member states, it is noted that, from the point of view of overall technical efficiency, the best scores were obtained by the following countries: Cyprus, Malta and Bulgaria. The efficiency coefficient value for the three mentioned countries is 1, indicating that these countries have reached total technical efficiency within the observed set of countries and that they represent a reference set on the basis of which the relative efficiency of other countries is assessed. Slightly lower efficiency scores are achieved by Portugal (0.9911) and Croatia (0.9225), followed by Greece (0.7269), Estonia (0.6906), Ireland (0.6901), etc. It should be noted that, at defined output and input variables, countries such as Austria, France, Germany and the UK have proven to be completely inefficient compared to the reference units. Table 2. b) Evaluation of overall and pure technical efficiency of non-EU countries in year 2017, using output-oriented CCR and BCC DEA model Country (DMU)CCR efficiency scoreRankBCC efficiency scoreRankTurkey1111Albania1111Montenegro 1111Georgia 1111Ukraine1111Armenia1111Moldavia0.8913711Macedonia0.7826811Bosnia and Herzegovina0.6115911Serbia0.42681011Russia0.30571111Iceland0.004120.999912Switzerland0130.000113Norway013014Source: Authors' calculations When it comes to the non-EU countries, the most efficient units, with the efficiency coefficient equal to 1, were the following countries: Turkey, Albania, Montenegro, Georgia, Ukraine and Armenia; while relatively inefficient countries were: Moldova (0.8913), Macedonia (0.7826), Bosnia and Herzegovina (0.6115), Serbia (0.4268), etc. From the Western Balkan countries, which according to defined input variables belong to cluster 3, Albania and Montenegro achieved the best efficiency scores, while Macedonia, Bosnia and Herzegovina and Serbia achieved relatively poor scores of overall technical efficiency. It should be noted that the implementation of the input-oriented CCR model would give the same results of the efficiency scores, with the orientation being different. The results of the assessment of pure technical efficiency for EU member states show that most countries achieved full efficiency. Countries that did not achieve pure technical efficiency are France, Austria, Germany and Great Britain. The pure technical efficiency coefficient for these four countries is equal to zero. However, if we take into account that the coefficient of their overall technical efficiency is equal to zero, and that, according to a large number of tourism indicators (revenues from tourism, number of employees in tourism, etc.) they occupy relatively good positions; we can conclude that these countries achieve their tourism development through the efficiency of scale. Also, from the non-EU countries, almost all countries achieved pure technical efficiency, while only Switzerland and Norway had a coefficient of efficiency equal to zero. It should also be noted that all countries of the Western Balkans had a pure technical efficiency coefficient equal to 1. Albania and Montenegro, in addition to achieving pure technical efficiency, also achieved the efficiency of scale, because their coefficient of overall technical efficiency is equal to 1. Countries that had a lower coefficient of overall technical efficiency, with a high value of coefficient of pure technical efficiency, did not achieve the efficiency in terms of scale. This is the case with countries such as Macedonia, Bosnia and Herzegovina, and Serbia. In other words, a lower CCR coefficient value, with a high BCC value, would mean that these countries are locally but not globally efficient, which is again a consequence of inefficiency of scale. Inefficiency of scale may be the result of inefficient operational activities and / or unfavorable conditions for the development of tourism. For this research, it is particularly interesting to note the position of the Western Balkan countries in relation to other countries, primarily those that are not part of the EU. Using the principal component analysis method and then the cluster method, we grouped the countries of the Western Balkans into one cluster (C3) and showed that none of the two defined components currently contributes positively to the overall tourist competitiveness of these countries. This resulted in lower scores of total technical efficiency, and consequently, of efficiency of scale. For economic policy makers, this should be an indication of the necessity of designing strategies and operational measures in the field of tourism. In order to achieve their global efficiency, it is necessary to devise a tourism development strategy that would encompass the entire region. The strategy should be based on innovative trends in tourism, for which there are enormous potentials in the Balkans. CONCLUSION AND DISCUSSION In this paper we evaluated the overall and pure technical efficiency of the tourism of European countries using CCR and BCC DEA methods. We divided the countries into two groups: the EU countries and non-EU countries. For both groups of countries we defined two input and two output variables. Input variables are presented by linear combinations of 14 pillars on the basis of which The Travel and Tourism Competitiveness Index (TTCI) is formed. On the basis of defined input variables, we carried out a clustering of countries using a cluster method in order to allocate relatively homogeneous decision-making units that have similar or identical tourism potentials, from the point of view of natural resources, cultural and historical heritage, geographical location, infrastructure, etc. In that sense, cluster C3, where Western Balkan countries belong, was distinguished as a special cluster within non-EU countries. The aforementioned cluster is characterized by the negative profile of both principal components. In addition, by defining output variables, we estimated the relative efficiency of all observed units. The result of the analysis has enabled us to identify the advantages and disadvantages of post-transition countries in terms of their tourism competitiveness. From the Western Balkans countries, Albania and Montenegro have been most effective in achieving full and pure technical efficiency compared to other observation units from the group of non-EU countries. Bosnia and Herzegovina, Serbia and Macedonia have achieved lower coefficient of total technical efficiency, while their coefficient of pure technical efficiency was equal to one. These results indicate that the three mentioned post-transition countries are ineffective in terms of scale, which may be the result of non-existence or inefficient implementation of operational activities in the field of tourism, as well as of unfavorable conditions for its development. For this reason, in order to improve tourism competitiveness, these countries should seek their chance in a tourist offer based on relatively inexpensive and already existing resources that do not require significant investments. This can be achieved through the development of specialized tourism products based on innovative trends in tourism, for which the countries of the Western Balkans have enormous potentials. Such forms of tourist offer could include various subtypes of health tourism, dark tourism, cultural tourism, educational tourism, etc. Such a tourism development strategy, which would encompass the whole region of the Western Balkans, would represent the opportunity for these countries to be recognizable on a global level, thus achieving a significantly higher level of tourism competitiveness. Considering the presented findings of the conducted research, it is evident that identifying the causes of modest tourism contribution to the GDP of most non-EU countries of the Western Balkans requires more detailed individual analyzes. LITERATURE Andersen, A., and N. Petersen (1993). A Procedure for Ranking Efficient Units in Data Envelopment Analysis, Management Science, vol. 39(10), pp. 1261-1264. Assaf, A., and Kne~evi, C. L. (2010). The performance of the Slovenian hotel industry: evaluation post-privatization. International Journal of Tourism Research, 12, 462 471. Baker, M. and M. Riley (1994). New perspectives on productivity in hotels: Some advances and new directions. International Journal of Hospitality Management, 13(4), 297311. Banker, R. D. and Morey, R. C. (1986). Efficiency analysis for exogenously fixed inputs and outputs. Operations Research, 34(4), 513-521. Barros, C. P. and Alves, F. P. (2004). Productivity in tourism industry. International Advances in Economic Research, 10(3), 215-225. Barros, C. P. and Mascarenhas, M. J. (2005). Technical and allocative efficiency in a chain of small hotels. Hospitality Management, 24(3),415-436. Barros, C.P., Botti, L., Peypoch, N., Robinot, E., Solonandrasana, B. and Assaf, A.G. (2011). Performance of Frech destinations: Tourism attraction perspectives. Tourism Management, 32(1), 141-146. Bi, G., Luo, Y., Liang, L. (2011). Efficiency evaluation of tourism industry with data envelopment analysis (DEA): a case study in China. Journal of China Tourism Reasearch, 7(1), 104-116. Botti, L., Peypoch, N., Robinot, E., Solonandrasana, B. (2009). Tourism destination competitiveness: the French regions case. European Journal of Tourims Reasearch, 21, 5-24 Cracolici, M.F., Nijkamp, P. and Rietveld, P. (2008). Assessment of tourism competitiveness by analyzing destination efficiency. Tourism Economics, 14(2), 325-342. Cuccia, T., Guccio, C., Rizzo, I. (2013). Does UNESCO inscription affect the performance of tourism destinations? A regional perspective, ACEI working papers series, AWP-04-2013. Cvetkoska, V.and Bariai, P. (2014) Measuring the efficiency of certain European Countries in tourism: DEA window analysis. Book of Proceedings of the International May Conference on strategic management  IMKS14. Bor, University of Belgrade. Davutyan, N. (2007). Measuring the quality of hospitality at Antalya. International Journal of Tourism Research, 9, 51 57. uranovi, M,. and Radunovi, M. (2011). Analiza efekata turizma na BDP, zaposlenost i platni bilans Crne Gore. Sektor za istra~ivanje i statistiku. Centralna banka Crne Gore. Hadad, S., Hadad, Y., Malul, M. and Rosenboim, M. (2012). The economic efficiency of the tourism industry: a global comparison. Tourism Economics, 18(5). 931-940. Hwang, S. and Chang, S. (2003). Using Data Envelopment Analysis to Measure Hotel Managerial Efficiency Change in Taiwan. Tourism Management, 24, 357-369. Jakai-Stojanovi, A., `eri, N. (2018). The Montenegrin Lighhouses as Destination Icons, University Mediterranean Podgorica, Montenegro Johns, N., Howcroft, B. and Drake, L. (1997). The use of data envelopment analysis to monitor hotel productivity. ProgrjxF  v x  N P ŷ{g\QCCh)DwhlO5\mH sH hlO5\mH sH h 75\mH sH 'hlOhlO0J5B*\mH phsH hlOhlO5>*\mH sH &jhlOhlO5>*U\mH sH h)Dw5\mH sH h)Dwh)Dw5\mH sH hlOhlO5\mH sH h5\mH sH "hhm5CJ\aJmH sH h]5CJ\aJmH sH hm5CJ\aJmH sH  v " $ F d  x  P R $a$gdcj$a$gd$a$gd)Dw$a$gdlO$a$$a$gdmP R j ]d/=ABX˶|qg`Y`R`R`R`KR h#0 h|j h#0 h h#0 h] h#0 hh CJmH sH h5\mHsHh 5\mHsHhT'0h8`5\hT'0hcj6]hG6\]hT'0hcj6\]hT'06\]hT'0hcj5CJh 6]mH sH hh6]mH sH h6]mH sH hh5\mH sH hhlO5\mH sH    {|b"c"u"w"$ & F a$gd $ a$gd $^a$gd $ & Fa$gd $ a$gd8$a$gd$a$gd $a$$^`a$gdX18r   d=>vw<z{|'46[\lԯɝԝԊԊԯԃԃ| h?h hHL-h h4hh hhqh hmh hHthh}(h8q hDhD h0hhhu[h] h]3hhh8h5h#0 hmH sH  h#0 h h#0 h h#0 h0ln  !w~s  ] d ! ! !"!!!!!!! " ""a"c"u"w"##$$V$[$u$۹׹汹۩ۢۛۛېh1h/B*ph hkh hhh8h5hu[hG`` h& 7h h`5hhh9^h% h8qh]h hoNhhhq h?hh1h~uB*phh~u4w"#55AAJFLFdFfFFFDKEKLL;N$  a$gd $`a$gd8q $`a$gd$ & F>d^`>a$gd $da$gd$ & Fda$gd$a$gdu${$$$$$9%%%%&&'(())))**#*%***T++++++++,,,,,,U-V-\--....c/q///s0t0׾徺峨ӡӚ嚖ӏӈ h/Jh hxhhu[ h5h hAA!hh1hB*ph hH2hh  hSSh hdh h{hh/ hfth hkhhh1h/B*phh1h B*ph4t01111a2f2v222333j3k334.4N4U4W4c4h4444444z5555555<7>788888888R9u9999::::::;;I<i<j<k<긱ߪꪣ hri h hbTqh h"h hh hlh hhh8q h9h hOhh  h$qhh hhh/ hFh>k<===>>C?W?x?y????@@1@AAABBB"C$C*CCD Dd^`>a$gd $ a$gd$  a$gdw$  a$gd$  a$gd NNNNNOOOOOOO/OKOYO]O^OpOqOrOsOwOOOʹnaMh|jhEHUjͿ` h|jhEHUVj<h|jhEHUj̿` h|jhEHUVh|jhEHjh|jhEHUh|jhmHnHuj:h|jhEHUj˿` h|jhEHUVh|jhEHjh|jhEHU h|jhjh|jhEHUj8h|jhEHUjʿ` h|jhEHUVR R2R3R4R5R>R?RQRRRSRTRURWRnRoRRRRRRRRŸņyp`SyLHh h|jh8qjDh|jhEHUjп` h|jhEHUVh|jhEHjh|jhEHUh|jhmHnHujBh|jhEHUjϿ` h|jhEHUVh|jhEHjh|jhEHU h|jhj@h|jhEHUjο` h|jhEHUVh|jhEHjh|jhEHURRRRRRRRRRRRRRSSSSSST T!T%TiTTTU"U)U*U-U:UqUsUUU8V:V^V|k|dd hG&h h1h1B*mHnHphu h1hB*mHnHphuh1hB*phh{ hTmhh8q hE=hh]hu[ hk!hh h8q5hJ5 h5CJOJQJaJ(h1h/5B*CJOJQJaJph(h1h5B*CJOJQJaJph'!TrUsU9V:VXXXY1YgYYYYYY[ $ a$gd $ a$gd{$ `a$gd$  `a$gd$  `a$gd $ a$gd8q^VaViVoVtVwV~VVVVVVVWWVX\XbXXXXXXXXXXXXYYY1Y2YPYbYcYdYgYƿvcvv$jLhmhmEHUmHnHuh$hmHsHhEmHsHhmHsH$jIhmhmEHUmHnHuh8q hu[h8qh hu[hmHnHu hu[hhG&hmHsH$jFhmhmEHUmHnHuhh hG&hhG&hmHnHu&gYhYjY{YYYYYYYYYYY[[[[]]^^___ __ _6_7_8_ҿݻr^M^Mr!h 5CJOJQJaJmHsH'h8h 5CJOJQJaJmHsH!h]5CJOJQJaJmHsH'h8h5CJOJQJaJmHsH hu[hrhu[hmHnHu hu[h h{5 h5h$jRhmhmEHUmHnHuh$hmHsHhmHsHhEmHsH$jDPhmhmEHUmHnHu[[]]^^7_8_Z_[_aaddLfMfnhohn $ a$gd$ & F ^`a$gd$ 8^8a$gd$ & F da$gd$a$gd $ a$gd8_Z___aaddYdkdndodddLfMfgmhnhohHi\iaiuiziiiiiiiiiɸ~vvnvaTvh1h6B*phh1h6B*phhu[h>OL6hu[h6hrhmHnHuhF,ghmHnHuhu[hrmHnHuhmHnHuhu[hmHnHu h1hB*mHnHphu h1hB*mHnHphuh1hB*ph hu[hrh] hu[hhu[h5 iijjUjxj{jjjjjjjjjjjk)k>knn-oAoFoYo^orowo~oooooooooooo pp/p5pTppppppq q q qfqmqpqqqcrorsstt纳h>OLCJaJ hVh hVh gh] h>OLhrhh>OLh>OL6h>OLh6 h>OLhhr h>OL6 hu[hhu[h6@nn q qpqqqssttttttu $$Ifa$gdK@ $$Ifa$gdK@ $`a$gd $`a$gd $ a$gdttttttttuuu'u(uQuWuuuuuuuu%v6v7v8vUvVvnvovvvvvvvͻ»³§~od³³³»³hW{hCJaJhu[hCJaJmHsH"jh^bCJUaJmHnHuhCJaJh5HhCJaJmHsHhCJaJmHsHhCJaJ h,<hh,<hCJaJh#vh5CJaJh5CJaJh#vhCJaJh>OLCJaJh>OLhCJaJ#uuuuuuu!u$u'uoccccWccc $$Ifa$gdK@ $$Ifa$gdK@kdU$$Ifl4F#` t06    44 lalyt8 'u(ukd=V$$Ifl4ִ tz#` }` t06    44 lalyt8(u1u:uJuRuSuTuUuVuWu_ufuouyuuuuuuuuuuuuuuuu $$Ifa$gdK@u vvv%v/v7v8vEvMvUvVv^vgvnvovwvvvvvvvv $IfgdK@ $$Ifa$gdK@vvkd!W$$Ifl4ִ tz#` }` t06    44 lalyt8vvvvvnwowwwww2x $IfgdK@ $$Ifa$gdK@ $$Ifa$gdK@ vDwLwmwnwwwwwwwxx1x2x3x=x\x^xaxcxdxxxxxxxxx#y$yyziznzqzz?{@{{{||q}}}}@~B~ hZh h-Hhhr h@Shh h1hh h4hhx3h9 h>OLhrh>OLh5 h>OLh h,<hhjhCJaJh,<hCJaJhCJaJhMhCJaJ12x3xkd X$$Ifl4qִ tz#``}`` t06    44 lalyt83x7x8xx]x^xbxcx $IfgdK@ $$Ifa$gdK@ cxdxkdX$$Ifl4qִ tz# `} ` t06    44 lalyt8dxxxxx|A~B~ 234mn$ Va$gd $h^ha$gd $ & Fa$gd$a$gd$a$gdr $`a$gd$ & F ^`a$gd$ ^a$gd gd8B~~~@ uy8?134OVmn<=abz{ѐzvzgzh>OLh0J>*B*phh-0(jh-0(U h>OLhG`` h>OLh>OL h>OLhh>~<hmHsH h1h]5B*mHphsHh8h5mHsH hKh hhhh] h\h>OL h\h h\hhr h?Bhh hh%<=9Dfg[aݖ򽶪Ą|h`YYR h>OLh>OL h>OLh8hrmHsH&h>OLhB*fHphq hY CJaJh>OLCJaJh>OLhrCJaJ h>OLhY hrCJaJmHsHh8CJaJmHsH h|jh8 h|jhh>OLhCJaJh"hCJaJmHsHhCJaJmHsHhmHsH h>OLh h>OLhr <=K`ez $$Ifa$gdK@ $ a$gd $ a$gd$ Va$gd G5#$ $Ifa$gdK@$ $Ifa$gdK@kdY$$IfTlrt| j 6 t0644 laytK@T5kdZ$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@$ $Ifa$gdK@$ $Ifa$gdK@G5#$ $Ifa$gdK@$ $Ifa$gdK@kdE[$$IfTlrt| j 6 t0644 laytK@T5kd[$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@ÍōǍ$ $Ifa$gdK@$ $Ifa$gdK@ǍȍЍ׍G5#$ $Ifa$gdK@$ $Ifa$gdK@kd\$$IfTlrt| j 6 t0644 laytK@T׍ٍۍݍލ5kdX]$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@ލ$ $Ifa$gdK@$ $Ifa$gdK@G5#$ $Ifa$gdK@$ $Ifa$gdK@kd ^$$IfTlrt| j 6 t0644 laytK@T 5kd^$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@ $ $Ifa$gdK@$ $Ifa$gdK@&-G5#$ $Ifa$gdK@$ $Ifa$gdK@kdk_$$IfTlrt| j 6 t0644 laytK@T-/1345kd`$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@4;BEGI$ $Ifa$gdK@$ $Ifa$gdK@IJPWG5#$ $Ifa$gdK@$ $Ifa$gdK@kd`$$IfTlrt| j 6 t0644 laytK@TWZ\^_5kd~a$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@_gnqsu$ $Ifa$gdK@$ $Ifa$gdK@uvG5#$ $Ifa$gdK@$ $Ifa$gdK@kd/b$$IfTlrt| j 6 t0644 laytK@T5kdb$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@$ $Ifa$gdK@$ $Ifa$gdK@G5#$ $Ifa$gdK@$ $Ifa$gdK@kdc$$IfTlrt| j 6 t0644 laytK@T5kdBd$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@ĎˎΎЎҎ$ $Ifa$gdK@$ $Ifa$gdK@ҎӎG5#$ $Ifa$gdK@$ $Ifa$gdK@kdd$$IfTlrt| j 6 t0644 laytK@T5kde$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@$ $Ifa$gdK@$ $Ifa$gdK@G5#$ $Ifa$gdK@$ $Ifa$gdK@kdUf$$IfTlrt| j 6 t0644 laytK@T5kdg$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@&-024$ $Ifa$gdK@$ $Ifa$gdK@45>EG5#$ $Ifa$gdK@$ $Ifa$gdK@kdg$$IfTlrt| j 6 t0644 laytK@TEHJLM5kdhh$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@MX_bdf$ $Ifa$gdK@$ $Ifa$gdK@fgovG5#$ $Ifa$gdK@$ $Ifa$gdK@kdi$$IfTlrt| j 6 t0644 laytK@Tvy{}~5kdi$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@~$ $Ifa$gdK@$ $Ifa$gdK@G5#$ $Ifa$gdK@$ $Ifa$gdK@kd{j$$IfTlrt| j 6 t0644 laytK@T5kd,k$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@$ $Ifa$gdK@$ $Ifa$gdK@ɏˏG5#$ $Ifa$gdK@$ $Ifa$gdK@kdk$$IfTlrt| j 6 t0644 laytK@TˏΏЏӏԏ5kdl$$IfTlrt| j 6 t0644 laytK@T$ $Ifa$gdK@ԏ׏ُ܏ޏ$ $Ifa$gdK@$ $Ifa$gdK@@7** $ a$gd8^8gdrkd?m$$IfTlrt| j 6 t0644 lap ytK@Tfg38 $$Ifa$gdK@$a$gd $ a$gd$a$gdr 89@BDFG;/// $$Ifa$gdK@ $$Ifa$gdK@kdm$$IfTlr= Glk t0644 laytK@TFHIQSU;/ $$Ifa$gdK@kdn$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@UWYZfh;/ $$Ifa$gdK@kd_o$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@hjlnox;/ $$Ifa$gdK@kdp$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@xz|~;kdp$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@ $$Ifa$gdK@ $$Ifa$gdK@G;/// $$Ifa$gdK@ $$Ifa$gdK@kdrq$$IfTlr= Glk t0644 laytK@T;/ $$Ifa$gdK@kd#r$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@Ĕ˔;/ $$Ifa$gdK@kdr$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@˔͔ϔєҔ;2 $IfgdK@kds$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@;kd6t$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@ $$Ifa$gdK@ $$Ifa$gdK@  G;/// $$Ifa$gdK@ $$Ifa$gdK@kdt$$IfTlr= Glk t0644 laytK@T "#+14;/ $$Ifa$gdK@kdu$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@4;>?KM;/ $$Ifa$gdK@kdIv$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@MPWZ[b;/ $$Ifa$gdK@kdv$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@bdgilm;kdw$$IfTlr= Glk t0644 laytK@T $$Ifa$gdK@mݖޖRSijopqMNOZ[ $ & Fa$gd$a$gd8^8gdݖޖ&4;ÚȚߚRSǞО͡ӡhijopqأߣ?d37>IJag򵮢ـ|hS= h`hS=hs" h>h] h>h>hmHsHhh5mHsH h>OLh* h>OLh$ h>OLhN^ h>OLhT h>OLhhrmHsHh h>OLht h>OLh>OL h>OLh,c h>OLh h>OLhr/q{ڧYZ Xx-.ЬѬXY^_MNOZ񺶺񤠙񒎃{pdh{h5mHsHhjhl]mHsHhUmHsHh1h_AB*phhl] h:dh:d h7-h7-hU h8hHh8hH hhhihK hihi h/!h/!h~ h~h~h]h> h&h&hzkh'Q h'Qh'Qh hS=hS=h81T&Z[xǯٯ<ذܰ.6BCI{ͲԲ޲'RSѳVGj"_¶W껳껳h>OLh>OL56h>OLhS^,56h>OLh6h>OLh56h>OLh>OL5h>OLh5 h>OLhh>OLh56B*]phh>OLh5B*phh>OLh>"jB*phh>OLhB*phh^hmHsH4[LT޲d{)ζιĺPX$h7$8$H$^h`a$gdl]h7$8$H$^h`gd$h7$8$H$^h`a$gdS^,$h^h`a$gd$h7$8$H$^h`a$gdWek0~Z~Lּ/BERn4B "-CEVXabpqڢۢ"#\ h>OLh% h]h% 56h]h% 5h% hUh1hl]56B*phh1hl]B*phh>OLh>OL5h>OLhS^,56h>OLh6h>OLh56h>OLh5 h>OLh>OL h>OLh6ess in Tourism Management, 24(4), 357-369. Jeremi, V. M. (2012). Statisti ki model efikasnosti zasnovan na Ivanovievom odstojanju. Doktorska disertacija, Fakultet organizacionih nauka, Univerzitet u Beogradu, Beograd. Kosmaczewska, J. (2014). Tourism interest and the efficiency of its utilization based on the example of the EU countries. Oeconomia, 13(1), 77-90. Luo, Y., Qian, X. (2017). Efficiency Evaluation and Countermeasures of Inter-provincial Tourism in China. Revista de la Facultad de Ingenieria U.C.V., Vol. 32, No.5, pp. 282-290 Manasakis, C., Apostolakis, A., and Datseris, G. (2013). Using data envelopment analysis to measure hotel efficiency in Crete. International Journal of Contemporary Hospitality Management, 25, 510535. Marciki Horvat, A. and Radanov, B. (2016). Analiza efikasnosti razvoja turizma regiona primenom DEA metode. International Thematic Monograph  Thematic Proceedings: Modern Management Tools and Economy of Tourism in Present Era. Martn, J.C., Mendoza, C., Romn, C. (2015). A DEA Travel-Tourism Competitiveness Index. Social Indicators Researh, 1-21. doi:10.1007/s11205-015-1211-3. Meesters, A. J. (2009). Efficiency of financial institutions: a stochastic frontiel analysis approach. Groningen: University of Groningen. Oukil, A., Channouf, N. and Al-Zaidi, A. (2016). Performance evaluation of the hotel industry in an emerging tourism destination:The case of Oman. Journal of Hospitality and Tourism Management,29, 60-68. Peypoch, N. (2007). On measuring tourism productivity. Asia Pacific Journal of Tourism Research, 12(3). 237-244. Pecina, Marijana. 2006. Metode multivarijantne analize - osnove, Sveu iliate u Zagrebu, Agronomski fakultet Phillips, P. and P. Louvieris (2005). Performance measurement systems in tourism, hospitality, and leisure small medium-sized enterprises: A balanced scorecard perspective. Journal of Travel Research, 44(2), 201211. Prorok, V., Bonjak, N. (2018). The analysis of the overall technical efficiency of insurance companies in Bosnia and Herzegovina.VI REDETE Conference, Banja Luka 2018. Prorok, V., Bonjak, N. (2018). Measuring the technical efficiency of insurance companies in Bosnia and Herzegovina. Congress Proceedings, Mediterranean International Congress on Social Sciences, Mecas III, Budapest Prorok, V., Popovi, B., Timoti, V., Baloti, G. (2017). Identification of Key Determinants for the Tourism Performance Improvement in the Western Balkans Countries.Congress Proceedings, Mediterranean International Congress on Social Sciences, Mecas II, Ohrid Sanjeev, G. M. (2007). Measuring efficiency of the hotel and restaurant sector: the case of India. International Journal of Contemporary Hospitality Management, 19, 378 387 Savi, G. (2011). Matemati ki modeli efikasnosti  skripta, FakXq#ʥ~ ֧h7(&Ѹm9$h7$8$H$^h`a$gdl]$h^h`a$gd$h7$8$H$^h`a$gd2$h7$8$H$^h`a$gd\ߣXڤƥȥX ;ʧ˧֧lza{|$7W̿ޜzrh>OLh>OL5h>OLh>OL56KHh>OLh56KHh>OLh5KHh>OLhKHh>OLh256h>OLh56B*]phh>OLh5B*phh>OLhB*ph h]56 h>OLhh>OLh6h>OLh56h>OLh5,jVhn9¸@\>Ļƻ+89:;<>?ABDEGtu´ܫm"h hu[6CJ]aJmHsH)h hu[6CJ]aJmHnHsHuh mjh mU h>OLhl]h% h^y h]5h1hl]56B*phh1hl]B*ph hl]5Uh>OLh6 h>OLhh>OLh56h>OLh256h>OLh5)ultet organizacionih nauka, Beograd. Sigala, M. (2004). Using Data Envelopment Analysis for Measuring and Benchmarking Productivity in the Hotel Sector, Journal of Travel and Tourism Marketing, 16(2), 39-60. Soysal-Kurt, H. (2017). Measuring tourism efficiency of European countries by using data envelopment analysis. European Scientific Journal, vol. 13. No.10. `eri, N., Ljubica, J. (2018). Market research methods in the sport industry, Emerald Publishing Limited, Bingley UK Toma, E. (2014). Regional scale efficiency evaluation by input-oriented data envelopment analysis of tourism.International Journal of Academic Research in Environment and Geography, 1(1). 15-20.     Session Name (Please DO NOT CHANGE THIS TEXT) The Thirteenth International Conference: Challenges of Europe: Growth, Competitiveness, Innovation and Well-being PAGE \* MERGEFORMAT14 PAGE \* MERGEFORMAT1 9:;=>@ACDFGuv"#$a$gdcjgdcjgd] $&dPa$gd] &dPgd $h7$8$H$^h`a$$h7$8$H$^h`a$gd2uv  !"#$%ֹ h>OLhl]h mh#0 h#0 CJmHnHsHuhcjhu[CJjhcjh-0(CJU#h#0 h#0 CJaJmHnHsHuhcjhu[CJaJjhcjh-0(CJUaJ h] hu[hu[6CJ]aJmHnHuhT#$%$h7$8$H$^h`a$?0P1h:p. A!"#$% DpDd D  3 A?"?2Go9oߣx;#D`!o9oߣx;:@`s x=O=OBA]_l0?Ғg $P~?Bao1w>(nwvnnfOPR5V SR;JYWҾ<%fUPkQ4 p֝|F OM+{OWy趻6e`4-q1XRqH2d4b99a V)]5Dd |D  3 A?"?2m)o6b;I#`!A)o6b;Ô`x}P=OA}3w",$^-NC?`vX(&P#$0V k_0Xؓ]VJ7}*@4 ,*HZk랬ZuiGx  F#a L =e@kO5w^jHyOO>aFɆ :JR2~o,S-ft<5̣{[ rQuN|Fgtj'Xٻߊn ,h O<7.C ~{\M ILDd TD  3 A?"?2&!\A(`!&!\A2  JxRKBQUSVTQk1Ĩ@-Zkq-5 "hm:.<9}W <.eqFW*ƸeYZdvs_($(?uK_ <#.' e~-PHB7w + :NJ\l-A> Zh1H\sHM\$NEuGL2Em-S6 t7l2[" 7j7؏h#jw?z%GϽCm^K?ZkWz$5xR[9-=AN;%O̞OW}}M&\.7P \{- G`B/29|bbv϶[9_Dd D  3 A?"?2G)M'3ew{fhWX#`!)M'3ew{fhWX: `;!xcdd`` $X bbd12,(ㆫaJ`T`3H1g`Yj@  5< %! `35J,L ! ~ Ay ]*d`bX񼗉Zh*6b`t` Hl$Cv;#RpeqIj."CPL Ì7 ;Q`56Dd @|D  3 A?"?2e+0])w)kA`!9+0])w)k`h+x}PNA}3 BJʼnN F !F#hrJĜv&~ = uMfgfVPJ܁5FM* h@6V/x =Whx-sV9*t 0K֣w-+EL6>NWXQ^Ńާ:XK76Rg+&Q'0Bw&gw C=B՟jMI9C <%(?HDd (TD  3 A?"?2 Qzemw>(ܝz@ `!Qzemw>(ܝz@B@  JxRKBQJ_VTѠ RPB6-AEQQHAo Zjii"pihjjh¡ sEBsߏ{\(@cnIMj*QT9z_@ z@{.90̮' eVySIW#&P*/```x8F&W-_.f^]<-5t0HkW(X^u&6T&4ʅfr O5*G팛)J8<֊'1D[+ZQ1o=:%oh>wCi.gwh"RWsu<ٿ?=`blu- 4d#AtHv';[lcT3Ysb_!ʪfn0I? O_v i!qzsbCv[]T+47,x~'?_pW5$Dd |D  3 A?"?2i#q[U{߅E`!=#q[U{߅`í x}PNBA=3 B 6`HhƆĎ>@ks_7PP|$\  !"#$%&'()*+,-./0123456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefghijklmnopqrstuvwxyz{|}~2Root Entry F-01Data \xWordDocumentAObjectPool'b+0-0_1610792879Fb+0b+0Ole CompObjfObjInfo "',/27<?BEFGJMNORUX[^chmrw| FMicrosoft Equation 3.0 DS Equation Equation.39q cX_1610792881Fb+0b+0Ole  "@^l] DMU j FMicrosoft Equation 3.0 DS Equation Equation.39q8x^L] j=,2,& ,n()CompObj fObjInfo Equation Native T_1610792882 Fb+0b+0Ole CompObjfObjInfoEquation Native ) FMicrosoft Equation 3.0 DS Equation Equation.39q pzl s FMicrosoft Equation 3.0 DS Equation Equation.39q_1610792884Fb+0b+0Ole CompObjfObjInfo$Q y rj FMicrosoft Equation 3.0 DS Equation Equation.39q<M r=1,2,& ,s()Equation Native :_1610792885"Fb+0b+0Ole CompObj fObjInfo!Equation Native X_1610792886$Fb+0b+0Ole   FMicrosoft Equation 3.0 DS Equation Equation.39q pjZdgY m FMicrosoft Equation 3.0 DS Equation Equation.39qCompObj#%!fObjInfo&#Equation Native $)_1610792887O)Fb+0b+0Ole %CompObj(*&fObjInfo+(Equation Native ):hrhg x ij FMicrosoft Equation 3.0 DS Equation Equation.39q85nfm i=1,2,& m()_1610792888.Fb+0b+0Ole *CompObj-/+fObjInfo0-      "!#%$&(')*+.,-/01g3456789:;<=>?@ABCDEFGHIJKLMNOPQRSTUVWXYZ[\]^_`abcdefhklmnopqrstuvwxyz{|}~Equation Native .T_1610792889,63Fb+0b+0Ole 0CompObj241f FMicrosoft Equation 3.0 DS Equation Equation.39q ZmY k FMicrosoft Equation 3.0 DS Equation Equation.39qObjInfo53Equation Native 4)_16107928908Fb+0b+0Ole 5CompObj796fObjInfo:8Equation Native 9>_16107928911E=Fb+0b+0"SuS DMU k FMicrosoft Equation 3.0 DS Equation Equation.39q<ZjY k=1,2,& ,n()Ole :CompObj<>;fObjInfo?=Equation Native >X_1610792892BFb+0b+0Ole @CompObjACAfObjInfoDC FMicrosoft Equation 3.0 DS Equation Equation.39q(z^_] max()h k =u r y rkr=1s " v i x iki=1m "Equation Native D_1610792893@JGFb+0+0Ole HCompObjFHIf FMicrosoft Equation 3.0 DS Equation Equation.39qpUp u r y rjr=1s " v i x iji=1m " d"1,j=1,2,& ,nObjInfoIKEquation Native L_1610792894LF+0+0Ole P FMicrosoft Equation 3.0 DS Equation Equation.39qOX_hd|g u r e"0,r=1,2,& ,s FMicrosoft Equation 3.0 DS EqCompObjKMQfObjInfoNSEquation Native Tk_1610792895;wQF+0+0Ole VCompObjPRWfObjInfoSYEquation Native Zkuation Equation.39qO -njm v i e"0,i=1,2,& ,m FMicrosoft Equation 3.0 DS Equation Equation.39q_1610792896VF+0+0Ole \CompObjUW]fObjInfoX_Equation Native `6_1610792897T^[F+0+0Ole aCompObjZ\bf0nhm h k FMicrosoft Equation 3.0 DS Equation Equation.39q ZmY k FMicrosoft Equation 3.0 DS EqObjInfo]dEquation Native e)_1610792898`F+0+0Ole fCompObj_agfObjInfobiEquation Native j)_1610792899YmeF+0+0uation Equation.39q XwRLjQ n FMicrosoft Equation 3.0 DS Equation Equation.39q XlX mOle kCompObjdflfObjInfognEquation Native o)_1610792900jF+0+0Ole pCompObjikqfObjInfols FMicrosoft Equation 3.0 DS Equation Equation.39q `XX s FMicrosoft Equation 3.0 DS Equation Equation.39qEquation Native t)_1610792901hroF+0+0Ole uCompObjnpvfObjInfoqxEquation Native y6_1610792902tF+0+0Ole zXX v i FMicrosoft Equation 3.0 DS Equation Equation.39q XX iCompObjsu{fObjInfov}Equation Native ~)_1610792903cyF+0+0Ole CompObjxzfObjInfo{Equation Native 6 FMicrosoft Equation 3.0 DS Equation Equation.39q8mv$nu u r FMicrosoft Equation 3.0 DS Equation Equation.39q_1610792904~F+0+0Ole CompObj}fObjInfoEquation Native )_1610792905|F+0+0Ole CompObjf |f{ r FMicrosoft Equation 3.0 DS Equation Equation.39qX|e{ x ij FMicrosoft Equation 3.0 DS EqObjInfoEquation Native :_1610792906F+0+0Ole CompObjfObjInfoEquation Native )_1610792907F+0+0uation Equation.39q |dg{ i FMicrosoft Equation 3.0 DS Equation Equation.39q x|tc{ jOle CompObjfObjInfoEquation Native )_1610792908F+0+0Ole CompObjfObjInfo FMicrosoft Equation 3.0 DS Equation Equation.39qf2qhp DMU j () FMicrosoft Equation 3.0 DS EqEquation Native N_1610792909F+0+0Ole CompObjfuation Equation.39q|$nu y rj FMicrosoft Equation 3.0 DS Equation Equation.39q 0 |d{ rObjInfoEquation Native :_1610792910F+0+0Ole CompObjfObjInfoEquation Native )_1610792911F+0+0Ole CompObjfObjInfoEquation Native ) FMicrosoft Equation 3.0 DS Equation Equation.39q x|tc{ j FMicrosoft Equation 3.0 DS Equation Equation.39q_1610792912F+0+0Ole CompObjfObjInfof2qhp DMU j ()Oh+'0t  $ 0 < HT\dlProf djelatnikNormal Korisnik14MicrEquation Native N1TablejmSummaryInformation(DocumentSummaryInformation8LϮ ٳg<vxTcis){^R"~!MOB)ݛ,gTޣ0K@`gYiwylVoV V7^и -{]ND3Ou388}1pUq5l9FDd TD   3 A ?"?2*=h}Q @U`!*=h}Q @U2  4 JxKAǿ3j*%uZ,dKPj bϝV:DC;n[A{3n ,ޛ7y!xn1|cqF[2xە*X q'(4( &끾;?B !䬲m.E#̣)j"M.h;X{)qG.:EK忼̼.xAp[}m&C!_GLu=S2E(RgBqk؈VOn@J[gJ}A?DȜS~]yE0.Пu;Ir]t\ k~MLى K#@MN5[az38ej01%4Ə l6dlf#pq<Dd D   3 A ?"? 2E?%؂+vꮳ_ !`!?%؂+vꮳ_ :@xcdd`` $X bbd12,(ㆫa:(Wcgb x@ڀ7$# lo@I A $37X/\!(?71Ф\ \ 'Xuri#F+5 \@.K@b P;=`321)W20(2tŀ:ҲAlUgd?b67Dd hD   3 A ?"? 2mTE"#mwYMI`!ATE"#mwYM@+|xcdd``> $X bbd12,(ㆫar`Z"Wcgb q 3aP5< %! `3),L ! ~ Ay &V2``bPbʠ 4Hq=` ro6Լ@7/Hd^[W}`r gzB0n0pmP7CT,ў!.w>۔7;nI/XdZ_>,dЫ$Ob=Wj6Pzi\2+Hm.BPZ*gXbF-cԮwC̿YDd (D   3 A ?"? 2h;/i@\r `!h;/i@\r` @T/8 cxTkA~3$VѠ+ړXQzPkG4ĵTA""yUxU*xE  &76kLu`wͼy73#?8#)vjuLPϑgSםaY{oEWy@+ 0>\,,-w2=w&o,i-9HWyjnJw S}sh2Ż5'Aqe 'vj{gv,h94~?p:8}s`nTrrDs iz7!)\%㴒T<4V- h_8 >@OTْ_z_ T{t/oU~ce޲6m:qj Txl7 ʮuic__a sͤx5u.qVq"gV^j=Ld!0Wct{?a|^Dd  (D  3 A?"? 2ЈPJeW+Vy`!ЈPJeW+V. @8 hx}ToP>qBcCKRBa &9HD* mPy@EtXX@bEea@ "jũ 񓜼ݽ4o~<'PL` ћY󒘟p~LP!M~Z RI[.j=759)]1>GcKNR+7UwaV!u, ; jjo\Kzg㌫ auk*a>@+*/$yA::&>%<}bb~PwIUPb<*Ǟ9E W=@xTT>_ nxsoj"| Cҁ'N V}} ۄ/|gB|ƒ03śecY&oQ\ѽjpe7YPPD3sSGn-.=Ko3|??\7<]5}@/Cߑ~k9W?Թ<}Q}Hϝh==S}:/KTo[nK?/f\ lҚ c|眺FgX9qDd TD  3 A?"?2n$ yTAT$ `!n$ yTAT$* Jx}RKBQ>)dMI/hPI #҆  couph1]uw /<=;p] 3@˃_@:;)cBڶͻǂ*/$u M`|;Ӏ6@q wwY!69!9ɽ;!+Q[%#_.2z(ݘGV?S%^2:Oڑ:j5_#S'OV}.A#0Ab. 7 PXęO8]e<ȿyR?^qƋfϏX$Iw&ի{_}>{x1W2σzT*8p/4 _㏜_us;gg͓ Gu3Σ6#Ļ/oԎpx]}<A]SM3zi<ʁ!rWvi;O9<Jne?b R뽱OMDd hD  3 A?"?2f7g=Cĝ A fB&`!:7g=Cĝ A f@2|xcdd``> $X bbd12,(ㆫar`:(Wcgb qРg@ UXRY7PL ZZh '.FyL,@պ@S9Ư fCk~nrb2F+\@q>KF&&\ ]@1.iP6Ì7`;@n oI3Dd D  3 A ?"?2E?%؂+vꮳ_ !(`!?%؂+vꮳ_ :@xcdd`` $X bbd12,(ㆫa:(Wcgb x@ڀ7$# lo@I A $37X/\!(?71Ф\ \ 'Xuri#F+5 \@.K@b P;=`321)W20(2tŀ:ҲAlUgd?b67Dd D  3 A?"?2EakЋf4ccKh԰!)`!akЋf4ccKh԰:@`s x=!OAJM(AB H*B&  ,'v{ v۷mh<ζ*Y͙WUnl:_6VB 2oBud.'0e" aRN6)i4 ?/)\ԏvcA5I32ZizMy9$.ˮ#~XfX߲jv5^Dd D  3 A?"?2DN/?CdLR}` +`!N/?CdLR}`:`];!xcdd`` $X bbd12,(ㆫaJ`\(Wcgb ԀtР6 憪aM,,He`0P&meabM-VK-WMc4Xk#ÅL,@պ@S9Ð v0oLLJ% ]@1.fմVo$ v0@+_5QDd D  3 A?"?2Gs\R粨v3#-`!s\R粨v3: `;!xcdd`` $X bbd12,(ㆫaJ`T`3H1g`Yj@  5< %! `35J,L ! ~ Ay -RŰq P.T mro1 v Hj;vF&&\E.C@Uo$ v0@V60Dd hD  3 A?"?2g 608hRCw/`!; 608hR`@!| xcdd``> $X bbd12,(ㆫar`J`3H1g`YˀXh3 憪aM,,He`HI&meabM-VK-WMc4IN.C&j]@ڈ#{3r=P7d &`sˀ >d `p021)W2Rdq1TKfr;.Ff~HIjDd 7D  3 A?"?2Hfd!$"3B$v1`!fd!$"3B:Zxcdd`` $X bbd12,(ㆫaZA( TA?d=@v0n RXRY`7S?$L ZZhR:.GL,@պ@S9Ƚ\@d % 1 `+KRsb\ U $X bbd12,(ㆫaR`Z(Wcgb qРc@ UXRY7PL ZZh,.&j]@ڈ{A~H~bԹR@5cdnn\@q>u;&LLJ% /b\ [%Ӡlo$vFI;Dd D  3 A?"?2GuVl. #W5`!uVl. : @<xcdd`` $X bbd12,(ㆫar`T`3H1g`Yj@  5< %! `35J,L ! ~ Ay ɩsc`bhab@-:$Ap]c!I)$5A (W)m# Ì7 ;Q`6Dd |D  3 A?"?2isC.;C-Z,[|t-E67`!=sC.;C-Z,[|t-`í x}PJBA..Ү>A-ōn )ݵ+!z-73oΏ>(yn"heRgFSH~sh'dC{!kyu]GMÛ?J ɾrir(vѓe銎3zVO`uvCBfC6\DTxxㅘ彏ag\hH,Dd D  3 A?"?2G3&H )}#7A`!3&H )}: @<xcdd`` $X bbd12,(ㆫar`T`3H1g`Yj@  5< %! `35J,L ! ~ Ay u(s00p1ab@-R [.1@``Ĥ\Y\b*b$F0h3X?v5Dd ,D   3 A?"?2DiS*hi I1Wk|l XCozJVdGDd ` (d # s @A ?Picture 75"20^s7 `I`!0^s7 H@ A8 uxTkA3&5 UQiPIAzIZ׊ikԃћ=+ |Dd  (d $ s @A!?Picture 76#2P"a$_QL`!P"a$_Q^@@8 x}T1o@~w>; C *D&HY"TҔ&" JB7, #Cc7$"QJHiIww{>1cM N5b'=!.G/zl,h69 8 8 @{BI_1='sNm9>|Q j i&5GeJeĞ=[* \柧K  մ؞6IlpQWEA]Y? q㮞cLF}һ|++ًm5[Jx7rqAǷW&KǷ2HWUMdžC3*zW$;cfyH#T{8yxxd<(ߍoX xzu<˥n4j2~z^`]R}n?S ni {h_m}o(ؕK&i[ =/>|u|`~3{>M> <(G( olh }gj-_u^ iU }_jܠ5EDd @d % s @A"?Picture 77$2~|s1rt\FWP`!~|s1rt\FWr` 3-hxR=O0=_B XPX[P BQ2 N, u@OW ;'1 С޳A 8L,VaH1\ͱ|Sy:^q:=> W]E\CV# `]/ ⸒Uʞm55ܨ۵Cyy\X}+v$f"m>Mn(\-iZTKFG2EgxS#\TL<SB5Dd @d & s @A#?Picture 78%2InFha4S`!InFhar@ xR=KA|o\B8S(ZIDL~@pz؈` U 6=g6DM̼yow" Њ/gRg{v,ux@0q d4,/zw7q]QXa[U|NڷWqVn6SȚ(xnb+a%]nFZc@};{U؇2&H/ s:~p/-NY?>tI RL| KCZϿ-vu;!ȋwj;}[j2>R^,W?MPLg̦B-5[[6di+/{Fy =ETР$$Ifl!vh#v#v#v :V l4 t06+555 alyt8$$Ifl!vh#v#v#v#v`#v#v}#v#v`:V l4 t06+5555`55}55`alyt8$$Ifl!vh#v#v#v#v`#v#v}#v#v`:V l4 t06+5555`55}55`alyt8$$Ifl!vh#v#v#v#v`#v#v}#v#v`:V l4q t06++5555`55}55`alyt8$$Ifl!vh#v#v#v#v`#v#v}#v#v`:V l4q t06++5555`55}55`alyt8$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556ytK@T$$If!vh#v#v#v #v#v:V l t06555 556p ytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@T$$If!vh#v#v#v#vl#vk:V l t065555l5kytK@Tosoft Office Word@*g@a@co@%L՜.+,D՜.+,0 hp|  K] Prof Title 8@ _PID_HLINKSA R fhttps://knoema.com/atlas"mailto:nevenseric@efst.hr  F Microsoft Word 97-2003 Document MSWordDocWord.Document.89q^ 662 0@P`p2( 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p 0@P`p8XV~_HmH nH sH tH @`@ ^0NormalCJ_HaJmH sH tH F@F ^0 Heading 1 $@&a$5\mH sH B@B ^0 Heading 2$@&5\mH sH F@F ^0 Heading 3 $@&5\^JaJDA D Default Paragraph FontRi@R 0 Table Normal4 l4a (k ( 0No List @&`@ ^0Footnote ReferenceH*6U`6 ^00 Hyperlink >*B*ph>@> ^0 Footnote TextCJaJ.)!. ^0 Page Number4 @24 ^0Footer p#BB@BB ^0 Body Texta$5CJmH sH 4@R4 ^0Header p#LC@bL ^0Body Text Indent ^`mH sH FV`qF ^0FollowedHyperlink >*B* phBoB ] Header CharCJaJmH sH tH D@D  Balloon TextCJOJQJaJZoZ Balloon Text Char CJOJQJ^JaJmH sH tH ^@^  List Paragraphd^m$CJOJPJQJaJPK![Content_Types].xmlN0EH-J@%ǎǢ|ș$زULTB l,3;rØJB+$G]7O٭V$ !)O^rC$y@/yH*񄴽)޵߻UDb`}"qۋJחX^)I`nEp)liV[]1M<OP6r=zgbIguSebORD۫qu gZo~ٺlAplxpT0+[}`jzAV2Fi@qv֬5\|ʜ̭NleXdsjcs7f W+Ն7`g ȘJj|h(KD- dXiJ؇(x$( :;˹! I_TS 1?E??ZBΪmU/?~xY'y5g&΋/ɋ>GMGeD3Vq%'#q$8K)fw9:ĵ x}rxwr:\TZaG*y8IjbRc|XŻǿI u3KGnD1NIBs RuK>V.EL+M2#'fi ~V vl{u8zH *:(W☕ ~JTe\O*tHGHY}KNP*ݾ˦TѼ9/#A7qZ$*c?qUnwN%Oi4 =3N)cbJ uV4(Tn 7_?m-ٛ{UBwznʜ"Z xJZp; {/<P;,)''KQk5qpN8KGbe Sd̛\17 pa>SR! 3K4'+rzQ TTIIvt]Kc⫲K#v5+|D~O@%\w_nN[L9KqgVhn R!y+Un;*&/HrT >>\ t=.Tġ S; Z~!P9giCڧ!# B,;X=ۻ,I2UWV9$lk=Aj;{AP79|s*Y;̠[MCۿhf]o{oY=1kyVV5E8Vk+֜\80X4D)!!?*|fv u"xA@T_q64)kڬuV7 t '%;i9s9x,ڎ-45xd8?ǘd/Y|t &LILJ`& -Gt/PK! ѐ'theme/theme/_rels/themeManager.xml.relsM 0wooӺ&݈Э5 6?$Q ,.aic21h:qm@RN;d`o7gK(M&$R(.1r'JЊT8V"AȻHu}|$b{P8g/]QAsم(#L[PK-![Content_Types].xmlPK-!֧6 0_rels/.relsPK-!kytheme/theme/themeManager.xmlPK-!0C)theme/theme/theme1.xmlPK-! ѐ' theme/theme/_rels/themeManager.xml.relsPK] Է ;P Xlu$t0k<FKtL MNOtP!QQRR^VgY8_itvB~ݖZW\u%`bdeghijklmopqrstuwxz{} w";N!T[nu'u(uuvv2x3xcxdxǍ׍ލ -4IW_uҎ4EMfv~ˏԏ8FUhx˔ 4Mbm[X9#%acfnvy|~;;;;;;; < <<*<,<`<s<u<v<<<<<<<<<<<<2=E=G=b=u=w=x===x>>>>??D?W?Y????? @ @/@B@D@_@r@t@@@@@@@@AA3AFAHAKA^A`AAAAAAAAAAAAAB)B+B.BABCB\BoBqB{BBBBBByyyԷX:::::::::::::::::::::::::::::::::X!!T # @H 0(  n(  6R  "?B S  ?)fԷg dt_GoBack}շ}շINOUchir<BCL8INS   (-24);)a)f)****--l2q2y5~555>7O7T7Y7p7w777g;m;r;y;;;;;;; <8<K<<<<<=#=L=M==x>>>>>?>?C?D?Z???????? @@@@H@I@w@@@@@@AAIA_AAAAA,BBBrBsBBBBHHHHNNNNeeeeeeef3f;fFfNfXfbfgggghhyooooot~hm!~ݡ>A٢ &£ʣFLRX ϥҥgpzĦŦ˦̦Цצݦަ "'-2;DUflqȨѨ©Ωϩ٩۩ '3?ƪɪϪӪ07>FJTxBJKQZanuvBGȭ LQW_kpЮïɯ˯֯ٯ ϰհ۰gly=BQW]djqw~FKPUV] !'(36=?GHVW\hn%)зҷշ?L|~c7n7^<`<>>>?C?????KIPIIIIIJ JOsO3f:f;fEfFfMfNfWfXfafss'{{߁ǝѝӝno<̟0V|}ܠ Hfjp֣͡ף!FotuΥϥ1suvyצ Pڧۧ3U}ި O۩1/xBmnTL|̮Ю ,NOϰwyQmfhz{+͵6#ҷշ333333333333333333333333333333333333333333333333333333333333333333333#c    ~55`7`7;;;;; <<-<`<<<<<=2=H=b==x>>>?D?Z???? @/@E@_@u@@@@@@A3AIAKAaAAAAAAAAAB,B.BDB\BrB{BBBBfEgEҷշF?+jf {;a Ws Gx:'X,0#v|#2޺Z6؁w ;HhANf{BjOr{D6t/!FZL{G.t,OdNHMUj{D/XH^S e^fP?ib c7 cXX^X`o(.^`o(..0^`0o(...0^`0o(.... ^`o( ..... 5 5 ^5 `o( ...... 6'`6'^6'``o(....... ,`,^,``o(........ 33^3`o(.........hh^h`OJQJ^Jo(h^h`o(.^`o(..@ 0^@ `0o(...x0^x`0o(.... ^`o( ..... P^P`o( ...... `^``o(....... (#`^(#``o(........ (^(`o(.........h^h`5o(.^`5o(..0^`05o(...0^`05o(.... ^`5o( ..... X ^X `5o( ...... `'`^`'``5o(....... -`^-``5o(........ 4^4`5o(.........^`OJQJ^Jo(^`B*OJQJ^Jo(phpp^p`OJQJ^Jo(@ @ ^@ `OJQJ^Jo(^`OJQJ^Jo(o^`OJQJ^Jo(^`OJQJ^Jo(^`OJQJ^Jo(oPP^P`OJQJ^Jo(^`o()^`.pL^p`L.@ ^@ `.^`.L^`L.^`.^`.PL^P`L.^`o()^`.pL^p`L.@ ^@ `.^`.L^`L.^`.^`.PL^P`L.h^h`5o(.^`5o(..0^`05o(...0^`05o(.... ^`5o( ..... X ^X `5o( ...... `'`^`'``5o(....... -`^-``5o(........ 4^4`5o(.........^`o(^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.8^8`5o(.8^8`o(..0^`0o(...0^`0o(.... ^`o( ..... ^`o( ...... p`^p``o(....... p`^p``o(........  ^ `o(.........^`o(.^`.pL^p`L.@ ^@ `.^`.L^`L.^`.^`.PL^P`L.^`o(.^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.`^``o(^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.hh^h`.88^8`.L^`L.  ^ `.  ^ `.xLx^x`L.HH^H`.^`.L^`L. ^` o(. ^` o(..p0p^p`0o(...@ 0@ ^@ `0o(.... xx^x`o( ..... HH^H`o( ...... `^``o(....... P`P^P``o(........ ^`o(.........8^8`5o(.8^8`o(..0^`0o(...0^`0o(.... ^`o( ..... ^`o( ...... p`^p``o(....... p`^p``o(........  ^ `o(.........hh^h`o(.hh^h`o(..0^`0o(...0^`0o(.... 88^8`o( ..... 88^8`o( ...... `^``o(....... `^``o(........ ^`o(.........^`o(.^`o(..0^`0o(...0^`0o(.... 88^8`o( ..... 88^8`o( ...... `^``o(....... `^``o(........ ^`o(......... ^ `o(.^`. L^ `L.| ^| `.L^L`.L^`L.^`.^`.L^`L.hh^h`o(.g^`o(^`.pLp^p`L.@ @ ^@ `.^`.L^`L.^`.^`.PLP^P`L.r{D6c{B/!F?ibjfWs {D/X{GMUF? ;,0:'hAOdN{#2;a S e^        $3                                   t#4/        p"        Q$%g;QVH,[>(p  OUi 2t7-#0 x{ s" W2 T0G% Tl8 Zu(W mK"2#3#P6#-0(g(}(a)S^,~ /0T'0^022B33x3k6 7+;E;S=K@_A9 B3VFCmFfH8hH:zHJ>OLLJ.NlOOGP'Qi$S5SPT81T"V-VY]U]l]^_G``ValaEoa8\b'c,curc dH}d gRg~h>"jcj|juDmjnbohq| qDcq/t)DwzwJy^y;HzD{K| }2} 5~ _C F>&{!w= /4T^zkFl|09^*]xm"b] 8/6"86l>m~u Pr{b~:dI0&XNq}G]n#~!mj1}8`Y cQEJD.^vi{yf%o:r9{^F^b%0Sap@M$|8qN^D 6/!u1 $_t u[