16IMPROVEMENT OF RURAL TOURISM IN THE AREA OF BOSNIA AND HERZEGOVINA USING MULTI-CRITERIA ANALYSIS
Miroslav Nedeljkovic1, Adis Puska2, Minja Vlajkovic3, Vesna Miletic 4 *Corresponding author E-mail: [email protected]
A R T I C L E I N F O Original Article Received: 26 May 2023 Accepted: 15 June 2023 doi:10.59267/ekoPolj2302553N UDC 338.48-44(1-22)(497.6) Keywords:
Rural tourism, multi-criteria decision-making, fuzzy logic, TOPSIS method, Bosnia and Herzegovina.
A B S T R A C T
The aim of the study was to evaluate rural households from five different regions of Bosnia and Herzegovina based on predetermined economic, ecological, and sociological criteria, using assessments from five expert professionals in the field. The study employed the fuzzy multi-criteria decision-making method called TOPSIS to ensure research accuracy. The results indicated that the rural household "Radoja" received the highest rating, which could serve as a solid foundation for future rural tourism development in Bosnia and Herzegovina. Based on the obtained results, it is possible to conduct future research in other regions of the country, providing appropriate guidelines for the development of rural tourism in Bosnia and Herzegovina.
JEL: L83, Q01, C01
Introduction
In the era of global digitalization of work processes and lifestyles, there is an increasing need for an active way of relaxation in natural surroundings. Rural destinations have become a refuge for many people seeking a break, and their development and offered amenities serve as a draw for an increasing number of visitors from nearby and distant areas or regions. According to Cvijanovic et al. (2021), rural tourism is a broader concept than rural tourism alone, as it encompasses areas outside settlements. On the other
1 Miroslav Nedeljkovic, PhD, Research Associate, Institute of Agricultural Economics, Volgina Street no. 15, 11060 Belgrade, Republic Serbia, Phone: +381654471201, E-mail: [email protected], ORCID ID (https://orcid.org/0000-0002-7393-2146)
2 Adis Puska, PhD, Assistant Professor, Faculty of Agriculture, Bijeljina University, Pavlovica put 024, 76300 Bijeljina, Republic of Srpska, BiH, Phone: +38761305535, E-mail: [email protected], ORCID ID (https://orcid.org/0000-0003-3274-0188)
3 Minja Vlajkovic, PhD, Assistant Professor, Faculty of Culture and Media, Megatrend University, 11070 Belgrade, Serbia, Phone: +38169120799, E-mail: minjavlajkovic@ gmail.com, ORCID ID (https://orcid.org/0009-0009-0072-1434)
4 Vesna Miletic, PhD, Associate Professor, Faculty of Business and Law Belgrade, MB University, Teodor Dreiser street 27, 11000 Belgrade, Serbia, Phone: +381 64 234 54 89, E-mail: [email protected], ORCID ID (https://orcid.org/0000-0003-1287-5800)
hand, Blesic et al. (2021) highlight that rural areas significantly differ in their specific characteristics from other natural areas, especially urban and suburban environments. Rural tourism has become a driver of economic development in these areas, as tourists visiting rural regions stay there and purchase local products, thereby influencing the economic growth of the region (Nedeljkovic, et al., 2022). Ezung (2011) provides perhaps the most precise definition of rural tourism, defining it as a form of tourism that allows visitors to have personal contact with the warmth of home, physical and human environments, and, as much as possible, participate in activities, traditions, and the way of life of the local population.
When considering tourism offerings, it is necessary to take into account the social, economic, and environmental background of the local community (Puska et al., 2020). According to Prevolsek et al. (2020), in the implementation of this type of tourism, it is important to utilize not only accommodation facilities but also other forms of content in rural areas, such as sports and recreational facilities, themed attractions, ethno-parks, adventure parks, eco-villages, ethno-villages, and more. This way, tourists receive a complete service, leading to higher satisfaction and loyalty (Pantic et al., 2022; Dileep Kumar, et al., 2020; Pantovic et al., 2023).
Bosnia and Herzegovina is a rural country with a rich cultural tradition of its local population, which gives it the right to strive for further development of rural and agritourism. More and more people of different profiles and income statuses are showing interest in the rich traditions and customs of local areas across the country. In line with this, Puska et al. (2020a) conclude that there is progressive investment in improving the tourism offerings in rural regions of Bosnia and Herzegovina. On the other hand, the supply is still relatively scarce and should undergo more intensive development (Puska et al., 2022). A good example can be found in Italy, where over 23,000 farms offer some form of agritourism/rural tourism, contributing to an annual revenue of over one billion euros (Stevanovic et al., 2022; Stankov & Roganovic, 2022; Palmi and Lezzi, 2020). Similarly, in Poland, services of this kind of tourism were provided on over 8,200 rural households in 2016. (Roman, et al., 2020).
For the development of rural tourism, it is crucial to evaluate the development level of rural settlements based on specific criteria and their assessment (Nedeljkovic et al., 2022a). Nedeljkovic et al. (2022) argue that due to the importance of these criteria for tourism development, a holistic approach should be applied when evaluating tourism capacities. Therefore, the main objective of this study is to assess the offering of rural tourism in Bosnia and Herzegovina based on predetermined criteria and employing expert assessments and a multi-criteria research method, as well as selecting the most attractive rural tourist destination. The research subject consists of five selected rural households located in different regions of Bosnia and Herzegovina. They were chosen by five experts from local tourism organizations with extensive work experience in this field.
Literature review
In previous research, many authors have focused on the development of rural tourism. Puska et al. (2021) observe in their study that rural tourism becomes a promoter of rural development, with tourism emerging as one of the main industries in the development of rural populations (Puska et al., 2019). Some authors view it as a representation of rural way of life and the values that this form of tourism provides in its natural environment, offering tourists an alternative to urban lifestyles (Zolfani et al., 2015; Sagic et al., 2019). Podovac et al. (2019) examined the improvement of rural tourism in the Goc region, where they found a need to enhance the quality of accommodation facilities and additional amenities. Puska et al. (2019), on the other hand, evaluated the tourism potentials of four rural settlements in the Brcko District and provided guidelines for their future development.
Kostic and Stanisic (2022) explore rural tourism in Serbia in their study. They find that the studied areas have preserved natural environments and emphasize the need for appropriate valorization of available potentials. Maksimovic et al. (2018) investigate rural tourism in the Stara Planina region, while Sarkovic (2018) examines the impact of media on the development of rural tourism in Serbia. Ristic (2013) studies the importance of state development policies in certain rural areas, as well as the revitalization of rural areas in Serbia. Wang (2021) explores the impact of the internet on improving conditions in rural areas through a case study of rural tourism in China, while Zhang et al. (2022) identify rural settlements as the main carriers of quality rural tourism in China, emphasizing the need to utilize the spatial arrangement of rural settlements to enhance this form of tourism. Additionally, it is worth noting that some authors have recently conducted research on the development of agrotourism as one form of rural tourism in specific countries (Giaccio et al., 2018; Roman et al., 2020; Tseng et al., 2019; Adamov et al., 2020, etc.).
When it comes to the application of multi-criteria decision-making and analysis in this field, there are several examples of research by foreign and domestic authors (Park, et al., 2017; Muhacir and Tazebay, 2017; Anabestani, 2016; Jeong, et al., 2016; Mahboban and Talebi, 2015; etc.). Nikolic, et al. (2015) utilize SWOT analysis and the Analytic Hierarchy Process (AHP) method for research and providing guidelines for further development of tourist destinations in the Stara Planina region. Prelovsek, et al. (2020) use the DEX method to assess the state of tourist offerings in ethno villages in Bosnia and Herzegovina. Nedeljkovic, et al. (2022), using the DEX multi-criteria decision-making method, provide guidelines and recommendations for further development of rural tourism in the Republic of Srpska. Through the application of multi-criteria analysis of rural tourist capacities, based on a random sample of four tourist facilities, they obtain results from which they conclude that they have adequately utilized the natural resources available in the Republic of Srpska. This method is also employed by Puska, et al. (2020) in their research, justifying its role in managing rural tourist offerings. Nedeljkovic, et al. (2022a), using the fuzzy SWARA multi-criteria analysis method, recommend measures to strengthen certain criteria in order to further enhance rural tourism in the Brcko District, using it as an example.
Materials and methods
The TOPSIS method (Technique for Order Preference by Similarity to Ideal Solution) was used as the working method. The method used is based on the fact that the chosen alternative has the shortest distance from the positive ideal solution as well as the longest distance from the negative ideal solution. It is considered one of the best techniques for selecting alternatives based on their similarity to the ideal solution. Additionally, it has been found to be satisfactory in various areas of analysis (Yavuz, 2016). To obtain a larger rating interval for decision-makers, which enhances accuracy in the selection process, we expand this method by using triangular fuzzy numbers to replace the numerical linguistic scale for evaluation and weighting.
The following stages in the application of the method used are presented below:
Stages 1: Create a decision matrix
Stages 2: Create the normalized decision matrix
\cycy<*)> CJ
ra =
max-ï Cij; Positive ideal solution
fij = ; aj = mini a-ij; Negative ideal solution
Stages 3: Create the weighted normalized decision matrix
Taking into account the weights of each criterion, the weighted normalized decision matrix can be calculated according to the following formula.
vu
rijwij
Where w;] represents weight of criterion c}.
Stages 4: Determine the fuzzy positive ideal solution (FPIS, A*) and the fuzzy
negative ideal solution (FNIS, A ).
= {£¿,^2. = jfmaxvij |iEs )
^mini^j |£E C
)}
Where v* is the max value of / for all the alternatives and x\ is the min value of / for all the alternatives. B and C represent the positive and negative ideal solutions, respectively.
Stages 5: Calculating the distance and the distance between each alternative and the fuzzy positive and negative solutions A* and A-.
The distance between each alternative and FPIS and FNIS are according to the following expression:
,m
d is the distance between two fuzzy numbers, when given two triangular fuzzy numbers (alf blr c^) and («2- £>2' c2), e distance between the two can be calculated as follows:
In this case d(vtj, vj) and d^v^, are crisp numbers.
Stages 6: Closeness coefficient and ranking of alternatives
The closeness coefficient can be obtained based on the following formula:
sr
CQ =
S + + Sr
We can find sources for constructing appropriate criteria in previous studies by various authors (Romao et al., 2018; He et al., 2019; Muresan et al., 2019; Spenceley, 2019; Zhang et al., 2019; Prevolsek et al., 2020; Hopkins, 2020; Puska et al., 2020). Due to the significance of sustainability in this industry, ecological criteria are also included in the study. The criteria are divided in the following manner:
• Economic criteria: Price of tourism services/products, quality of offered services and organization, accommodation facilities, transportation connectivity, attractiveness of services for tourists.
• Ecological criteria: Availability of natural resources, quality of natural resources, landscape/environment, diversity of agricultural resources, geoFigureic characteristics.
• Sociological criteria: Offer of local products, possibility of organizing new tourism activities and events, accessibility of tourist facilities, transfer of knowledge from tradition to consumers, importance of local community development.
The research sample consisted of rural households from five different regions of Bosnia and Herzegovina engaged in rural tourism. The selection of these households was based on the expert opinions of five decision-makers with extensive experience in the field. The following selected households were used as alternatives for the research purposes:
• Household „Ostrovica", (Kulen Vakuf Municipality),
• Household „Sadrvan", (Vares Municipality),
• Household „Grabovica", (Tomislavgrad),
• Household „Dedic", (Srebrenica Municipality),
• Household „Radoja", (Sipovo Municipality).
Additionally, available and accessible professional and scientific literature in the analyzed field was used as a data source in the study, and the results were presented in tabular and Figureical form.
Results and discussion
As previously mentioned, in the study, we utilized the expert assessment of five experts in the field based on 15 predefined criteria. The following Table 1 shows the types of criteria and their weights, which were assessed as equal by the experts for the purposes of this research, assuming that each criterion has equal importance.
Continuing with the study, the results of the remaining steps of the applied research method are presented.
Table 1. Criteria used
The name of the criteria Category Criteria weight
1 Price of tourism services/products - (0.067, 0.067, 0.067)
2 Quality of offered services and organization + (0.067, 0.067, 0.067)
3 Accommodation facilities + (0.067, 0.067, 0.067)
4 Transportation connectivity + (0.067, 0.067, 0.067)
5 Attractiveness of services for tourists + (0.067, 0.067, 0.067)
6 Availability of natural resources + (0.067, 0.067, 0.067)
7 Quality of natural resources + (0.067, 0.067, 0.067)
8 Landscape/environment + (0.067, 0.067, 0.067)
9 Diversity of agricultural resources + (0.067, 0.067, 0.067)
10 GeoFigureic characteristics + (0.067, 0.067, 0.067
11 Offer of local products + 0.067, 0.067, 0.067)
12 Possibility of organizing new tourism activities and events + (0.067, 0.067, 0.067)
13 Accessibility of tourist facilities + (0.067, 0.067, 0.067)
14 Transfer of knowledge from tradition to consumers + (0.067, 0.067, 0.067)
15 Importance of local community development + (0.067, 0.067, 0.067)
Source: Authors
In the following Table 2, the fuzzy scale used in the study is presented.
Table 2. Fuzzy Scale
Code Linguistic terms L M U
1 Very low 0 0 1
2 Low 0 1 3
3 Moderately low 1 3 5
4 Moderate 3 5 7
Code Linguistic terms L M U
5 Moderately high 5 7 9
6 High 7 9 10
7 Very high 9 10 10
Source: According to Mijajlovic et al., 2020
The following table 3 shows the results of the decision matrix.
Table 3. Decision Matrix
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
A1 3.400, 5.400, 7.400 3.400, 5.400, 7.400 3.200, 5.000, 7.000 3.000, 5.000, 7.000 4.600, 6.600, 8.400 3.800, 5.800, 7.800 3.200, 5.000, 7.000 3.400, 5.400, 7.400 3.400, 5.400, 7.400 1.800, 3.400, 5.400 4.600, 6.600, 8.600 4.200, 6.200, 8.200 3.800, 5.800, 7.800 2.200, 4.200, 6.200 2.600, 4.600, 6.600
A2 5.000, 7.000, 8.800 3.800, 5.800, 7.800 3.200, 5.000, 7.000 3.000, 5.000, 7.000 5.000, 7.000, 9.000 4.200, 6.200, 8.000 3.000, 5.000, 7.000 3.400, 5.400, 7.400 4.200, 6.200, 8.000 2.000, 3.800, 5.800 3.400, 5.400, 7.400 4.200, 6.200, 8.000 4.600, 6.600, 8.600 1.600, 3.400, 5.400 1.800, 3.400, 5.400
A3 5.400, 7.400, 9.200 5.000, 7.000, 8.800 4.600, 6.600, 8.200 4.600, 6.600, 8.600 5.000, 7.000, 8.400 4.600, 6.600, 8.200 4.200, 6.200, 8.000 3.400, 5.400, 7.400 3.200, 5.000, 6.800 3.400, 5.400, 7.400 3.800, 5.800, 7.800 4.600, 6.600, 8.200 5.000, 7.000, 8.400 3.400, 5.400, 7.200 3.600, 5.400, 7.200
A4 4.600, 6.600, 8.400 5.400, 7.400, 8.800 4.600, 6.600, 8.400 4.200, 6.200, 7.800 5.800, 7.800, 9.200 3.800, 5.800, 7.600 5.400, 7.400, 9.200 4.600, 6.600, 8.400 4.200, 6.200, 8.000 5.400, 7.400, 9.000 5.000, 7.000, 8.800 4.600, 6.600, 8.600 4.600, 6.600, 8.400 5.000, 7.000, 8.600 3.400, 5.400, 7.400
A5 5.000, 7.000, 8.600 5.200, 7.000, 8.400 3.800, 5.800, 7.800 2.800, 4.600, 6.600 6.200, 8.000, 9.400 5.000, 7.000, 8.600 5.400, 7.400, 9.000 5.800, 7.800, 9.200 4.600, 6.600, 8.400 5.000, 7.000, 8.800 6.200, 8.200, 9.600 5.000, 7.000, 8.600 5.400, 7.400, 9.200 5.400, 7.400, 9.200 4.600, 6.600, 8.400
Source: Authors
The normalized decision matrix is shown in the table below.
Table 4. Normalized decision matrix
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
A1 0.459, 0.630, 1.000 0.386, 0.614, 0.841 0.381, 0.595, 0.833 0.349, 0.581, 0.814 0.489, 0.702, 0.894 0.442, 0.674, 0.907 0.348, 0.543, 0.761 0.370, 0.587, 0.804 0.405, 0.643, 0.881 0.200, 0.378, 0.600 0.479, 0.688, 0.896 0.488, 0.721, 0.953 0.413, 0.630, 0.848 0.239, 0.457, 0.674 0.310, 0.548, 0.786
A2 0.386, 0.486, 0.680 0.432, 0.659, 0.886 0.381, 0.595, 0.833 0.349, 0.581, 0.814 0.532, 0.745, 0.957 0.488, 0.721, 0.930 0.326, 0.543, 0.761 0.370, 0.587, 0.804 0.500, 0.738, 0.952 0.222, 0.422, 0.644 0.354, 0.563, 0.771 0.488, 0.721, 0.930 0.500, 0.717, 0.935 0.174, 0.370, 0.587 0.214, 0.405, 0.643
A3 0.370, 0.459, 0.630 0.568, 0.795, 1.000 0.548, 0.786, 0.976 0.535, 0.767, 1.000 0.532, 0.745, 0.894 0.535, 0.767, 0.953 0.457, 0.674, 0.870 0.370, 0.587, 0.804 0.381, 0.595, 0.810 0.378, 0.600, 0.822 0.396, 0.604, 0.813 0.535, 0.767, 0.953 0.543, 0.761, 0.913 0.370, 0.587, 0.783 0.429, 0.643, 0.857
A4 0.405, 0.515, 0.739 0.614, 0.841, 1.000 0.548, 0.786, 1.000 0.488, 0.721, 0.907 0.617, 0.830, 0.979 0.442, 0.674, 0.884 0.587, 0.804, 1.000 0.500, 0.717, 0.913 0.500, 0.738, 0.952 0.600, 0.822, 1.000 0.521, 0.729, 0.917 0.535, 0.767, 1.000 0.500, 0.717, 0.913 0.543, 0.761, 0.935 0.405, 0.643, 0.881
A5 0.395, 0.486, 0.680 0.591, 0.795, 0.955 0.452, 0.690, 0.929 0.326, 0.535, 0.767 0.660, 0.851, 1.000 0.581, 0.814, 1.000 0.587, 0.804, 0.978 0.630, 0.848, 1.000 0.548, 0.786, 1.000 0.556, 0.778, 0.978 0.646, 0.854, 1.000 0.581, 0.814, 1.000 0.587, 0.804, 1.000 0.587, 0.804, 1.000 0.548, 0.786, 1.000
Source: Authors
The following table 5 shows the weighted normalized decision matrix
Table 5. The weighted normalized decision matrix
C1 C2 C3 C4 C5 C6 C7 C8 C9 C10 C11 C12 C13 C14 C15
A1 0.031, 0.042, 0.067 0.026, 0.041, 0.056 0.026, 0.040, 0.056 0.023, 0.039, 0.055 0.033, 0.047, 0.060 0.030, 0.045, 0.061 0.023, 0.036, 0.051 0.025, 0.039, 0.054 0.027, 0.043, 0.059 0.013, 0.025, 0.040 0.032, 0.046, 0.060 0.033, 0.048, 0.064 0.028, 0.042, 0.057 0.016, 0.031, 0.045 0.021, 0.037, 0.053
A2 0.026, 0.033, 0.046 0.029, 0.044, 0.059 0.026, 0.040, 0.056 0.023, 0.039, 0.055 0.036, 0.050, 0.064 0.033, 0.048, 0.062 0.022, 0.036, 0.051 0.025, 0.039, 0.054 0.034, 0.049, 0.064 0.015, 0.028, 0.043 0.024, 0.038, 0.052 0.033, 0.048, 0.062 0.034, 0.048, 0.063 0.012, 0.025, 0.039 0.014, 0.027, 0.043
A3 0.025, 0.031, 0.042 0.038, 0.053, 0.067 0.037, 0.053, 0.065 0.036, 0.051, 0.067 0.036, 0.050, 0.060 0.036, 0.051, 0.064 0.031, 0.045, 0.058 0.025, 0.039, 0.054 0.026, 0.040, 0.054 0.025, 0.040, 0.055 0.027, 0.040, 0.054 0.036, 0.051, 0.064 0.036, 0.051, 0.061 0.025, 0.039, 0.052 0.029, 0.043, 0.057
A4 0.027, 0.035, 0.050 0.041, 0.056, 0.067 0.037, 0.053, 0.067 0.033, 0.048, 0.061 0.041, 0.056, 0.066 0.030, 0.045, 0.059 0.039, 0.054, 0.067 0.034, 0.048, 0.061 0.034, 0.049, 0.064 0.040, 0.055, 0.067 0.035, 0.049, 0.061 0.036, 0.051, 0.067 0.034, 0.048, 0.061 0.036, 0.051, 0.063 0.027, 0.043, 0.059
A5 0.026, 0.033, 0.046 0.040, 0.053, 0.064 0.030, 0.046, 0.062 0.022, 0.036, 0.051 0.044, 0.057, 0.067 0.039, 0.055, 0.067 0.039, 0.054, 0.066 0.042, 0.057, 0.067 0.037, 0.053, 0.067 0.037, 0.052, 0.066 0.043, 0.057, 0.067 0.039, 0.055, 0.067 0.039, 0.054, 0.067 0.039, 0.054, 0.067 0.037, 0.053, 0.067
Source: Authors
The positive and negative ideal solutions are presented in the following Table 6. From it, we can observe that within the group of economic criteria, the price of tourism services/products has an advantage over other criteria in that group. Similarly, the criteria of diversity of agricultural resources and importance of local community development have advantages within the group of ecological criteria and sociological criteria, respectively.
Table 6. The positive and negative ideal solutions
Positive ideal Negative ideal
Price of tourism services/products (0.025, 0.031, 0.042) (0.031, 0.042, 0.067)
Quality of offered services and organization (0.041, 0.056, 0.067) (0.026, 0.041, 0.056)
Accommodation facilities (0.037, 0.053 ,0.067) (0.026, 0.040, 0.056)
Transportation connectivity (0.036, 0.051, 0.067) (0.022, 0.036, 0.051)
Attractiveness of services for tourists (0.044, 0.057, 0.067) (0.033, 0.047, 0.060)
Availability of natural resources (0.039, 0.055, 0.067) (0.030, 0.045, 0.059)
Quality of natural resources (0.039, 0.054, 0.067) (0.022, 0.036, 0.051)
Landscape/environment (0.042, 0.057, 0.067) (0.025, 0.039, 0.054)
Diversity of agricultural resources (0.037, 0.053, 0.067) (0.026, 0.040, 0.054)
GeoFigureic characteristics (0.040, 0.055, 0.067) (0.013, 0.025, 0.040)
Offer of local products (0.043, 0.057, 0.067) (0.024, 0.038, 0.052)
Possibility of organizing new tourism activities and events (0.039, 0.055, 0.067) (0.033, 0.048, 0.062)
Accessibility of tourist facilities (0.039, 0.054, 0.067) (0.028, 0.042, 0.057)
Transfer of knowledge from tradition to consumers (0.039, 0.054, 0.067) (0.012, 0.025, 0.039)
Importance of local community development (0.037, 0.053,0.067) (0.014, 0.027, 0.043)
Source: Authors
In comparison to the results of previous similar studies in neighboring regions and countries, we can observe both similarities and differences. For example, in the research conducted by Nedeljkovic et al., (2022a) on the development of rural tourism in Brcko
District, they identified rural facilities and services as the highest-rated criterion among the analyzed households. However, Nedeljkovic et al., (2022) in a study focusing on the territory of Republic of Srpska identified criteria from the ecological group as the highest-rated among the selected households, with price being the dominant economic criterion. Another recent study (Puska et al., 2022) in this field, covering the territory of Bosnia and Herzegovina, also emphasized the dominance of economic criteria in the selection of rural households, particularly price and quality, which aligns somewhat with the findings of this research. Any differences among these studies are likely influenced by the fact that different regions were examined, each with its own specificities and variations.
Table 7 shows the distance from the positive and negative ideal solutions.
Table 7. Distance from positive and negative ideal solutions
Distance from positive ideal Distance from negative ideal
Alternative 1 0.207 0.031
Alternative 2 0.193 0.044
Alternative 3 0.112 0.126
Alternative 4 0.06 0.177
Alternative 5 0.029 0.207
Source: Authors
The best obtained alternative is the closest to the Positive Ideal Solution (FPIS) and the furthest from the Negative Ideal Solution (FNIS). The coefficients of closeness for each alternative and their ranking are shown in the following Table 8.
Table 8. Ranking of alternatives
Ci Rank
Alternative 1 0.131 5
Alternative 2 0.184 4
Alternative 3 0.53 3
Alternative 4 0.748 2
Alternative 5 0.876 1
Source: Authors
The visual representation of the closeness coefficients for each of the provided alternatives is shown in Figure 1, which follows in the paper. From the Figure, we observe that alternative 5, or the household "Radoja" is rated the highest. This particular household is located in an extremely rural area in the village of Pljeva, at an altitude of 450 meters and seven kilometers away from the city of Sipovo. The household is situated near the Pliva River, and the surrounding area is mountainous, characterized by untouched and attractive nature that fully enables the organization of all essential tourist activities.
Figure 1. Closeness coefficient
Ci values
1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00
Household Household Household Household Household "Radoja" "Dedic" "Grabovica" "Sadrvan" "Ostrovica"
Source: Authors Conclusions
The study considered and applied a multi-criteria decision-making model in selecting a household engaged in rural tourism. Based on the evaluation of five rural tourist destinations chosen by five experts in the field, the household "Radoja" from the municipality of Sipovo was selected as the best-rated alternative. The criteria considered were of economic, ecological, and sociological nature. Due to the significance of rural tourism for the entire territory of Bosnia and Herzegovina, rural households from five different regions were included in the study. The mentioned household effectively utilized all the unique features offered by the natural surroundings in this part of Bosnia and Herzegovina, making it a valuable example for the overall development of this form of tourism in the country. This, of course, has significant implications for the local development of this area.
The chosen decision-making method used in the study has proven to be highly flexible for research purposes, but it does not exclude the use of other multi-criteria analysis methods in this field. The focus of future research in rural tourism should be on the application of these methods in other rural regions of the country, with the aim of establishing rational guidelines for further development of rural tourism in those areas.
Conflict of interests
The authors declare no conflict of interest. 562
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