Selecting investment strategies for European Tourism Industry using the hybrid decision making approach based on interval type-2 fuzzy sets

Abstract

The selection of investment strategies is one of prominent issues for the service industries in the competitive market environment. The aim of the study is to evaluate the investment strategies for the European tourism industry using a hybrid decision making approach based on the interval type-2 fuzzy sets. For this purpose, a set of criteria and dimensions for the investments strategies of the European tourism is proposed. Interval type-2 fuzzy DEMATEL is applied to weight the criteria and dimensions and interval type-2 fuzzy MOORA is used for selecting the best investment strategies for the European tourism industry. The novelty of the study is to propose a set of investment strategies for the European tourism and the extended methods for DEMATEL and MOORA under the fuzzy environment. A novel hybrid approach is also proposed to the decision-making process based on the interval type-2 fuzzy sets. The major results are summarized as European countries should firstly focus on increasing the number of touristic facilities in the coast line to attract the attention of the tourists. Similarly, it can also be beneficial for these countries make more private security investment for the tourists to feel more secured while visiting historical places. Owing to these strategies, it can be possible to develop European tourism industry.

Keywords

Tourism investment strategies interval type-2 fuzzy DEMATEL interval type-2 fuzzy MOORA industry

1 Introduction

Tourism is a growing industry almost all around the world. The number of arrivals for international tourism at world level reached to 1.4 billion in 2017 whereas it was only 0.5 billion in 1995. It means that the demand of international tourism has increased about three times since 2015. It can be accepted that tourism has many advantages for the countries. First of all, tourism has an increasing effect on the investment amount of the countries which lead to higher economic growth. In addition to this aspect, it also plays a significant role to decrease unemployment rate in the countries [67]. Moreover, with the help of expenditures of the tourists, there can be increase in the amount of foreign currencies in the countries. This situation makes a contribution to the current account balances of the countries. Due to this situation, countries aim to implement strategies and policies to have more powerful tourism industry [33].

Tourism is also a very significant industry for Europe in many different ways. 2008 global mortgage crisis and 2010 European debt crisis have negative effects on the economies of countries in Europe. Owing to this aspect, the governments of these countries aim to stimulate tourism industry to increase the income [27, 52]. According to World Bank report, travel and tourism industry in Europe contributed more than 5% to the GDP in 2017. This situation gives information that European countries should take necessary actions to develop appropriate tourism policy so that macroeconomic development can be provided much easily.

However, the selection of investment strategies is an important issue for the sustainable growth of the service industries [18]. Accordingly, the aim of this study is to assess the investment strategies for the European tourism industry. Within this framework, a hybrid decision making approach based on the interval type-2 fuzzy sets is taken into the consideration. Therefore, it is aimed to reach more appropriate results with the benefit of using fuzzy logic in decision making process. In this context, a set of criteria and dimensions for the investments strategies of the European tourism are identified based on the components of SWOT analysis. After that, alternative investment strategies for the European tourism industry are selected by considering these dimensions and criteria. In the analysis process, interval type-2 fuzzy DEMATEL is applied to weight the criteria and dimensions and interval type-2 fuzzy MOORA is used for selecting the best investment strategies. As a result of this analysis, it can be possible to make recommendations for the development of the tourism in European region.

There are several novelties in the study. First, it is the first study considering the investment strategies in the European tourism industry under the fuzzy environment. So, it is aimed to increase the originality of this study. Additionally, a hybrid decision making approach is proposed for selecting the investment strategies. The decision making model includes the extend method of DEMATEL and MOORA is provided based on interval type-2 fuzzy sets to evaluate the criteria and dimensions and rank the investment strategies for the investment strategies. Because, the DEMATEL method is not only a tool of measuring the relative importance of criteria, but also provides an impact and relation map among them by considering the mutual effects of each other [66, 18]. Similarly, the MOORA uses the positive and negative effects of the decision matrix simultaneously [22, 17]. And also, the type-2 fuzzy sets and their extended methods give more successful results under the uncertainty problems [17 , 61–63]. Accordingly, it is aimed that the extended method of DEMATEL and MOORA with interval type 2 sets contributes to the literature by obtaining more accurate and comprehensive results.

There are 5 different sections in this study. After this introduction part, there is a literature review part in the second section. In this context, the similar studies and missing part of the literature related to this subject are identified. Moreover, the methods used in the studies are detailed in the third section of this study. In addition, an application on European region is stated in the fourth section. Furthermore, in the final section, analysis results and necessary recommendations are shared to develop tourism industry in Europe.

2 Literature review

Tourism industry plays a very significant role for European countries. Therefore, this subject attracted the attentions of many different researches. As a result of this aspect, it is possible to see many different studies related to European tourism industry. Selected recent studies are given below: For example, [39] underlined that education has a significant contribution to the tourism industry. Similarly, [7] also identified the importance of effective marketing by using interview methodology. [58] focused on corporate social responsibility activities and [8] put emphasis on social factors to have higher financial performance of the European tourism companies with the help of survey method. Furthermore, [29] aimed to analyze the competitiveness in European tourism industry and concluded that sospitality is the key indicator. Also, [33] identified that Health and hygiene factors play a key role in this situation.

In addition to these studies, it is also defined that some studies analyze the impacts of tourism on economic development of European countries. For instance, [51] aimed to analyze this relationship by using structural equation model. They reached the conclusion that tourism has a significant power on the sustainable economic improvement. [67] determined that There is a strong relationship between economic growth and tourism for European countries. Similar to this study, [2] also underlined the importance of this relationship for European countries that has financial problems. [38, 41] are also other studies that identified the positive relationship between economic improvement of European countries and tourism.

Furthermore, some studies in the literature stated that funds allocated to the tourism should be used efficiently to increase the performance of the tourism industry. As an example, [56] aimed to understand whether fund usage is efficient in European tourism industry or not. As a result of regression analysis, they defined that funds should be used more efficiently in these countries to have sustainable tourism. [28, 48] underlined the significance of cost reduction policies for this condition. [52] also focused on this aspect with the help of logit methodology. Additionally, [25] stated that the actions to have higher efficiency depend on the features of the countries.

Moreover, some other studies analyzed the impacts of financial crisis on the European tourism industry. For example, [11] aimed to evaluate the performance of European tourism and concluded that the debt crisis in Europe has a significant and negative influence on tourism performance. [27] determined that 2008 global mortgage crisis has negative effects on European tourism. Furthermore, [12] tries to analyze the relationship between tourism income and oil prices. According to the results of structural VAR model, it is defined that volatility in oil prices leads to significant problems for tourism industry. Therefore, they recommended that this volatility should be hedged by European tourism companies.

There are also some studies which aimed to define the relationship between tourism and environmental issues. For instance, [3] defined that there is a negative relationship between tourism expenditure and municipal solid waste. Moreover, [49] also concluded that CO₂ emission should be lowered to have sustainable tourism. In addition to the environmental issues, some studies also focused on the subject of rural tourism. As an example, [53] aimed to explain the relationship between rural tourism and national identity. They reached a conclusion that there is a significant relationship between these variables. Moreover, [4] also determined that rural tourism has a positive contribution to the economic development of European countries.

Additionally, some studies focused on the different subjects related to European tourism industry. For instance, [9] aimed to analyze the market attractiveness of the tourism and concluded that special services should be provided for disabled tourists in Europe. Moreover, [26] stated that there is a need for a dynamic and flexible tourism model for Europe. On the other side, [57] focused on the significance of agritourism to increase the income. Furthermore, [40] identified that social interaction among different countries plays a crucial role in European tourism.

While considering the studies in the literature, it is defined that European tourism industry in analyzed in the literature in many different aspects, such as impacts, indicators, providing efficiency and performance during crisis period. Another important point is that various methodologies are taken into the consideration in these studies like regression, structural VAR model, survey, interview and Westerlund panel cointegration test. Nonetheless, it is concluded that there is a need for a new study which considers the investment strategies in the European tourism under the fuzzy environment.

3 Methodology

3.1 Interval type-2 fuzzy sets

A type 2 fuzzy set can be explained as $\tilde{A}$ and it is defined by a type-2 membership function $μ_{\tilde{A} (x, u)}$ , where x ∈ X and u ∈ J_x ⊆ [0, 1]. This situation can be demonstrated as Equation (1) (Mendel et al. 2006).

$\begin{matrix} \tilde{A} = {((x, u), μ_{\tilde{A} (x, u)}) | \forall_{x} \in X, \forall_{u} \in J_{x} \subseteq [0, 1]}, or \\ \tilde{A} = \int x \in X \int u \in J_{x} μ_{\tilde{A}} (x, u) / (x, u) J_{x} \subseteq [0, 1] \end{matrix}$ (1)

In this equation, ∫∫refers to the union over all admissible x and u and $μ_{\tilde{A}} (x, u)$ takes value between 0 and 1. In addition, ∫is replaced by ∑for discrete universes of discourse. When all $μ_{\tilde{A}} (x, u)$ is equal to 1, the definition of interval type-2 fuzzy sets can be shown as Equation (2). $\tilde{A} = \int x \in X \int u \in J_{x} 1 / (x, u) J_{x} \subseteq [0, 1]$ (2) ${\tilde{A}}_{i} = ({\tilde{A}}_{i}^{U}, {\tilde{A}}_{i}^{L}) = (\begin{matrix} (a_{i 1}^{U}, a_{i 2}^{U}, a_{i 3}^{U}, a_{i 4}^{U}; H_{1} ({\tilde{A}}_{i}^{U})), \\ (a_{i 1}^{L}, a_{i 2}^{L}, a_{i 3}^{L}, a_{i 4}^{L}; H_{2} ({\tilde{A}}_{i}^{L})) \end{matrix})$ (3)

In equation (3), $a_{i 1}^{U}, a_{i 2}^{U}, a_{i 3}^{U}, a_{i 4}^{U}$ $a_{i 1}^{L}, a_{i 2}^{L}, a_{i 3}^{L}, a_{i 4}^{L}$ refer to the reference values of the interval type-2 fuzzy set ${\tilde{A}}_{1}$ . Moreover, $H_{j} ({\tilde{A}}_{i}^{U})$ explains the membership value of the element $a_{i (j + 1)}^{U}$ . In this case, j takes values between 1 and 2.

In addition to these calculations, arithmetic operations of the interval type-2 fuzzy sets are demonstrated on Equations (4)–(8). $\begin{matrix} {\tilde{A}}_{1} \oplus {\tilde{A}}_{2} = ({\tilde{A}}_{1}^{U}, {\tilde{A}}_{1}^{L}) \oplus ({\tilde{A}}_{2}^{U}, {\tilde{A}}_{2}^{L}) \\ = ((a_{11}^{U} + a_{21}^{U} + a_{12}^{U} + a_{22}^{U}, a_{13}^{U} + a_{23}^{U}, a_{14}^{U} + a_{24}^{U}; \end{matrix}$ $\begin{matrix} min (H_{1} ({\tilde{A}}_{1}^{U}), H_{1} ({\tilde{A}}_{1}^{U}), min (H_{2} ({\tilde{A}}_{1}^{U}), H_{2} ({\tilde{A}}_{2}^{U}))), \\ (a_{11}^{L} + a_{21}^{L} + a_{12}^{L} + a_{22}^{L} + a_{13}^{L} + a_{23}^{L}, a_{14}^{L} + a_{24}^{L}; \\ min (H_{1} ({\tilde{A}}_{1}^{L}), H_{1} ({\tilde{A}}_{2}^{L})), min (H_{2} ({\tilde{A}}_{1}^{L}), H_{2} ({\tilde{A}}_{2}^{L}))) \end{matrix}$ (4) $\begin{matrix} {\tilde{A}}_{1} ⊖ {\tilde{A}}_{2} = ({\tilde{A}}_{1}^{U}, {\tilde{A}}_{1}^{L}) ⊖ ({\tilde{A}}_{2}^{U}, {\tilde{A}}_{2}^{L}) = ((a_{11}^{U} - a_{24}^{U}, \\ a_{23}^{U}, a_{13}^{U} - a_{22}^{U}, a_{14}^{U} - a_{21}^{U}; min (H_{1} ({\tilde{A}}_{1}^{U}), H_{1} ({\tilde{A}}_{2}^{U})), \\ min (H_{2} ({\tilde{A}}_{1}^{U}), H_{2} ({\tilde{A}}_{2}^{U}))), (a_{11}^{L} - a_{24}^{L}, a_{12}^{L} - a_{23}^{L}, \\ a_{13}^{L} - a_{22}^{L}, a_{14}^{L} - a_{21}^{L}; min (H_{1} ({\tilde{A}}_{1}^{L}), H_{1} ({\tilde{A}}_{2}^{L})), \\ min (H_{2} ({\tilde{A}}_{1}^{L}), H_{2} ({\tilde{A}}_{2}^{L})))) \end{matrix}$ (5) $\begin{matrix} {\tilde{A}}_{1} \otimes {\tilde{A}}_{2} = ({\tilde{A}}_{1}^{U}, {\tilde{A}}_{1}^{L}) \otimes ({\tilde{A}}_{2}^{U}, {\tilde{A}}_{2}^{L}) = ((a_{11}^{U} \times a_{21}^{U}, \\ a_{12}^{U} \times a_{22}^{U}, a_{23 \times}^{U}, a_{14}^{U} \times a_{24}^{U}; min (H_{1} ({\tilde{A}}_{1}^{U}), H_{1} ({\tilde{A}}_{2}^{U})), \\ min (H_{2} ({\tilde{A}}_{1}^{U}), H_{2} ({\tilde{A}}_{2}^{U}))), (a_{11}^{L} \times a_{21}^{L}, a_{12}^{L} \times a_{22}^{L}, \\ a_{13}^{L} \times a_{23}^{L}, a_{14}^{L} \times a_{24}^{L}; min (H_{1} ({\tilde{A}}_{1}^{L}), H_{1} ({\tilde{A}}_{2}^{L})), \\ min (H_{2} ({\tilde{A}}_{1}^{L}), H_{2} ({\tilde{A}}_{2}^{L})))) \end{matrix}$ (6) $\begin{matrix} K {\tilde{A}}_{1} = (K \times a_{11}^{U}, k \times a_{12}^{U}, k \times a_{13}^{U}, k \times a_{14}^{U}; \\ H_{1} ({\tilde{A}}_{1}^{U}), H_{1} ({\tilde{A}}_{1}^{U})), (K \times a_{11}^{L}, k \times a_{12}^{L}, k \times a^{L} \\ , k \times a_{14}^{L}; H_{1} ({\tilde{A}}_{1}^{L}), H_{1} ({\tilde{A}}_{1}^{L})) \end{matrix}$ (7) $\begin{matrix} \frac{{\tilde{A}}_{1}}{k} = (\frac{1}{k} \times a_{11}^{U}, \frac{1}{k} \times a_{12}^{U}, \frac{1}{k} \times a_{13}^{U}, \frac{1}{k} \times \\ a_{14}^{U}; H_{1} ({\tilde{A}}_{1}^{U}), H_{2} ({\tilde{A}}_{1}^{U})), \\ (\frac{1}{k} \times a_{11}^{L}, \frac{1}{k} \times a_{12}^{L}, \frac{1}{k} \times a_{13}^{L}, \frac{1}{k} \times \\ a_{14}^{L}; H_{1} ({\tilde{A}}_{1}^{L}), H_{2} ({\tilde{A}}_{1}^{L})) \end{matrix}$ (8)

3.2 IT2 fuzzy DEMATEL

Geneva Research Centre of the Battelle Memorial Institute is introduced the The DEMATEL (decision making trial and evaluation laboratory) to analyze the interdepence among the factors as well as the effectiveness of the items [6 , 50]. The method is utilized to visualize the sophisticated relationship among the criteria by converting the causal relationship into a visible structure [60, 30]. In the literature, there are several examples of DEMATEL such as suppliers [44], human resource [1, 14], banking [31 , 22] and economy [16, 20].

The DEMATEL approach is examined in some essential procedures [59 , 64]. We extend the method based on interval type-2 fuzzy sets is called as IT2 Fuzzy DEMATEL and detailed in the following steps [5].

Step 1. Collect the expert scores for the criteria and dimensions. Decision makers provide the linguistic evaluations and the results are converted to the interval fuzzy sets for evaluating the influence degrees of each criteria and dimension. Table 4 represents the linguistic scales and fuzzy numbers for the criteria and dimensions.

Step 2. Construct the initial direct-relation fuzzy matrix. The matrix is computed by considering the scores of each decision maker collectively. After that, the average scores of pairwise comparisons are constructed for dimensions and criteria based on interval type-2 fuzzy numbers. The initial direct-relation fuzzy matrix $\tilde{Z}$ is constructed from the convergence of the linguistic terms provided by the experts scores where ${\tilde{Z}}_{ij} = (a_{ij}, b_{ij}, c_{ij}, d_{ij}; H_{1} (Z_{ij}^{U}), H_{2} (Z_{ij}^{U})), (e_{ij}, f_{ij}$ , $g_{ij}, h_{ij}; H_{1} (Z_{ij}^{L}), H_{2} (Z_{ij}^{L}))$ presents the degree of the influence using the fuzzy numbers with the Equation (9).

$\tilde{Z} = [\begin{matrix} 0 & {\tilde{z}}_{12} & \dots & \dots & {\tilde{z}}_{1 n} \\ {\tilde{z}}_{21} & 0 & \dots & \dots & {\tilde{z}}_{2 n} \\ ⋮ & ⋮ & ⋱ & \dots & \dots \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ {\tilde{z}}_{n 1} & {\tilde{z}}_{n 2} & \dots & \dots & 0 \end{matrix}]$ (9)

The average fuzzy scores are used to obtain the initial direct-relation matrix with the Equation (10). $\tilde{Z} = \frac{{\tilde{Z}}^{1} + {\tilde{Z}}^{2} + {\tilde{Z}}^{3} + . . . . {\tilde{Z}}^{n}}{n}$ (10)

Step 3. Normalize the pairwise matrix. The initial influence matrix is normalized by the formula (11).

$\tilde{X} = [\begin{matrix} {\tilde{x}}_{11} & {\tilde{x}}_{12} & \dots & \dots & {\tilde{x}}_{1 n} \\ {\tilde{x}}_{21} & {\tilde{x}}_{22} & \dots & \dots & {\tilde{x}}_{2 n} \\ ⋮ & ⋮ & ⋱ & \dots & \dots \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ {\tilde{x}}_{n 1} & {\tilde{x}}_{n 2} & \dots & \dots & {\tilde{x}}_{nn} \end{matrix}]$ (11) where $\begin{matrix} {\tilde{x}}_{ij} = \frac{{\tilde{Z}}_{ij}}{r} \\ = (\frac{{\tilde{Z}}_{a_{ij}^{'}}}{r}, \frac{{\tilde{Z}}_{b_{ij}^{'}}}{r}, \frac{{\tilde{Z}}_{c_{ij}^{'}}}{r}, \frac{{\tilde{Z}}_{d_{ij}^{'}}}{r}; H_{1} (Z_{ij}^{U}), H_{2} (Z_{ij}^{U})), \\ (\frac{{\tilde{Z}}_{e_{ij}^{'}}}{r}, \frac{{\tilde{Z}}_{f_{ij}^{'}}}{r}, \frac{{\tilde{Z}}_{g_{ij}^{'}}}{r}, \frac{{\tilde{Z}}_{h_{ij}^{'}}}{r}; H_{1} (Z_{ij}^{L}), H_{2} (Z_{ij}^{L})) \end{matrix}$ (12) $r = max (max_{1 \leq i \leq n} \sum_{j = 1}^{n} Z_{d_{ij}^{'}}, max_{1 \leq i \leq n} \sum_{j = 1}^{n} Z_{d_{ij}^{'}})$ (13)Step 4. Construct the total influence fuzzy matrix. The total influence fuzzy matrix is calculated by the Equations (14)–(18). Eight crisp matrices are defined as: $X_{a^{'}} = [\begin{matrix} 0 & a_{12}^{'} & \dots & \dots & a_{1 n}^{'} \\ a_{21}^{'} & 0 & \dots & \dots & a_{2 n}^{'} \\ ⋮ & ⋮ & ⋱ & \dots & \dots \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ a_{n 1}^{'} & a_{n 2}^{'} & \dots & \dots & 0 \end{matrix}], \dots,$ $X_{h^{'}} = [\begin{matrix} 0 & h_{12}^{'} & \dots & \dots & h_{1 n}^{'} \\ h_{21}^{'} & 0 & \dots & \dots & h_{2 n}^{'} \\ ⋮ & ⋮ & ⋱ & \dots & \dots \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ h_{n 1}^{'} & h_{n 2}^{'} & \dots & \dots & 0 \end{matrix}]$ (14)

The total influence matrix is defined as: $\tilde{T} = lim_{k \to \infty} \tilde{X} + {\tilde{X}}^{2} + . . . . + {\tilde{X}}^{k}$ (15) $\tilde{T} = [\begin{matrix} {\tilde{t}}_{11} & {\tilde{t}}_{12} & \dots & \dots & {\tilde{t}}_{1 n} \\ {\tilde{t}}_{21} & {\tilde{t}}_{22} & \dots & \dots & {\tilde{t}}_{2 n} \\ ⋮ & ⋮ & ⋱ & \dots & \dots \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ {\tilde{t}}_{n 1} & {\tilde{t}}_{n 2} & \dots & \dots & {\tilde{t}}_{nn} \end{matrix}]$ (16) where $\begin{matrix} {\tilde{t}}_{ij} = (a_{ij}^{″}, b_{ij}^{″}, c_{ij}^{″}, d_{ij}^{″}; H_{1} ({\tilde{t}}_{ij}^{U}), H_{2} ({\tilde{t}}_{ij}^{U})), \\ (e_{ij}^{″}, f_{ij}^{″}, g_{ij}^{″}, h_{ij}^{″}; H_{1} ({\tilde{t}}_{ij}^{L}), H_{2} ({\tilde{t}}_{ij}^{L})) \end{matrix}$ (17) $[a_{ij}^{″}] = X_{\dot{a}} \times {(I - X_{\dot{a}})}^{- 1}, . . . ., [h_{ij}^{″}] = X_{\dot{h}} \times (I - X_{\dot{h}})^{- 1}$ (18)

Step 5. Compute the defuzzified total influence matrix. The defuzzified values of the total influence matrix is obtained by using the defuzzification method [34] for the trapezoidal membership function of the interval type-2 fuzzy set with the formula $\begin{matrix} {Def}_{T} = \frac{\frac{(u_{U} - l_{U}) + (β_{U} - m_{1 U} - l_{U}) + (α_{U} - m_{2 U} - l_{U})}{4} + l_{U} + \frac{[(u_{L} - l_{L}) + (β_{L} - m_{1 L} - l_{L}) + (α_{L} - m_{2 L} - l_{L}) + l_{L}]}{4}}{2} \end{matrix}$ (19)

Where α and β are the maximum membership degrees of the lower membership function of the considered type-2 fuzzy set, u_U is the largest possible value of the upper membership function, l_U is the least possible value of the upper membership function, m_1U and m_2U are the second and third parameters of the upper membership function, u_L is the largest possible value of the lower membership function, l_L is the least possible value of the lower membership function, m_1L and m_2L are the second and third parameters of the lower membership function. ${Def}_{T} = T = [t_{ij}]_{n \times n}, i, j = 1, 2, . . . ., n$ (20) ${\tilde{D}}_{i}^{def} = r = {[\sum_{j = 1}^{n} t_{ij}]}_{n \times 1} = (r_{i})_{n \times 1} = (r_{1}, . . ., r_{2}, . . ., r_{n}$ (21) ${\tilde{R}}_{i}^{def} = y = {[\sum_{i = 1}^{n} t_{ij}]}_{1 \times n}^{'} = (y_{j})_{1 \times n} = (y_{1}, y_{i}, . . ., y_{n})$ (22)

The defuzzification of process is employed to obtain the influential network relation map with the values of $({\tilde{D}}_{i} + {\tilde{R}}_{i})^{def}$ and $({\tilde{D}}_{i} - {\tilde{R}}_{i})^{def}$ by the following equations. ${\tilde{D}}_{i}^{def}$ indicates the the sum of all vector rows while ${\tilde{R}}_{i}^{def}$ represents the sum of all vector colums. $({\tilde{D}}_{i} + {\tilde{R}}_{i})^{def}$ denotes the total degree of the influence among criteria, and the higher its value, the closer the criterion is to object’s central point. $({\tilde{D}}_{i} - {\tilde{R}}_{i})^{def}$ is the degree of causality among criteria. When $({\tilde{D}}_{i} - {\tilde{R}}_{i})^{def}$ is positive, it means that criterion i influences other criteria. Otherwise, the criterion is influenced by other criteria.

3.3 IT2 fuzzy MOORA

The Multi-Objective Optimization by Ratio Analysis (MOORA) was developed [10] with the aim of decision making under complex environment. In other words, this methodology is used to rank the alternatives while analyzing the beneficial and/or non-beneficial criteria. This method was preferred in many different studies in the literature. For example, [36] used this model for energy industry, [35] and [42] considered this approach for supplier selection process. It can also be seen that MOORA methodology is used in banking and airline industry [19, 21]. In this study, the MOORA approach is taken into the consideration based on with the interval type-2 fuzzy sets. In the first step, decision matrix is developed by considering the evaluations of decision makers. This matrix under interval type-2 fuzzy sets is given on Equation (23). $X_{i j} = \overset{C1 C2 C3 \dots Cn}{\begin{matrix} A_{1} \\ A_{2} \\ A_{3} \\ ⋮ \\ A_{m} \end{matrix} (\begin{matrix} x_{11} & x_{12} & x_{13} & \dots & x_{1 n} \\ x_{21} & x_{22} & x_{23} & \dots & x_{2 n} \\ x_{31} & x_{32} & x_{33} & \dots & x_{3 n} \\ ⋮ & ⋮ & ⋮ & ⋱ & ⋮ \\ x_{m 1} & x_{m 2} & x_{m 3} & \dots & x_{m n} \end{matrix})}$

In this matrix, A represents alternatives while C gives information about the criteria. On the other side, x_ij shows the aggregated ratings and the calculation is demonstrated on Equation (24). $x_{ij} = \frac{1}{k} [\sum_{e = 1}^{n} x_{ij}^{e}], i = 1, 2, 3, \dots, m$ (23)

In the second step, defuzzified values of the fuzzy decision matrix is calculated as following. $\begin{matrix} Def (x_{ij}) = Rank ({\tilde{x}}_{ij})_{m \times n} = M_{1} ({\tilde{A}}_{i}^{U}) + M_{1} ({\tilde{A}}_{i}^{L}) + \\ M_{2} ({\tilde{A}}_{i}^{U}) + M_{2} ({\tilde{A}}_{i}^{L}) + M_{3} ({\tilde{A}}_{i}^{U}) + M_{3} ({\tilde{A}}_{i}^{L}) \\ - \frac{1}{4} (S_{1} ({\tilde{A}}_{i}^{U}) + S_{1} ({\tilde{A}}_{i}^{L}) + S_{2} ({\tilde{A}}_{i}^{U}) + S_{2} ({\tilde{A}}_{i}^{L}) + \\ S_{3} ({\tilde{A}}_{i}^{U}) + S_{3} ({\tilde{A}}_{i}^{L}) + S_{4} ({\tilde{A}}_{i}^{U}) + S_{4} ({\tilde{A}}_{i}^{L}) + S_{1} ({\tilde{A}}_{i}^{U}) + \\ H_{1} ({\tilde{A}}_{i}^{U}) + H_{1} ({\tilde{A}}_{i}^{L}) + H_{2} ({\tilde{A}}_{i}^{U}) + H_{2} ({\tilde{A}}_{i}^{L})) \end{matrix}$ (24) $M_{p} ({\tilde{A}}_{i}^{j}) = (a_{ip}^{j} + a_{i (p + 1)}^{j}) / 2$ (25)

In these equations, $M_{p} ({\tilde{A}}_{i}^{j})$ represents the average of the elements $a_{ip}^{j}$ and $a_{i (q + 1)}^{j}$ and p takes value between 1 and 3. Moreover, $S_{q} ({\tilde{A}}_{ip}^{j})$ refers to the standard deviation of the elements $a_{iq}^{j}$ and $a_{i (q + 1)}^{j}$ . In this circumstance, q can take values between 1 and 3 as well. The details are demonstrated on Equation (27). $S_{q} ({\tilde{A}}_{i}^{j}) = \sqrt{\frac{1}{2} \sum_{k = q}^{q + 1} {(a_{ik}^{j} - \frac{1}{2} \sum_{k = q}^{q + 1} a_{ik}^{j})}^{2}}$ (26)

In the third step, the defuzzified decision matrix is normalized by using following equation. $X_{ij}^{*} = \frac{X_{ij}}{\sqrt{\sum_{j = 1}^{m} X_{ij}^{2}}}$ (27)

In addition to them, the positive and negative effects of the normalized values are calculated on the fourth step with the following equations. $Y_{i} \sum_{j = 1}^{h} X_{ij}^{*} - \sum_{j = h + 1}^{n} X_{ij}^{*}$ (28)

Furthermore, in the fifth step, the weighted values are calculated while using Equation (30). $y_{i}^{*} = \sum_{j = 1}^{h} w_{j} x_{i j}^{*} - \sum_{j = h + 1}^{n} w_{j} x_{i j}^{*}$ (29)

In Equation (30), w refers to the weight of the criteria. In the last step, alternatives are ranked with the descending order.

4 An application on the European Tourism Industry

An IT2-based hybrid model is proposed for ranking the alternatives of investment strategies for the European tourism industry. The interval type 2 sets are frequently used for the analyzing of uncertainty with the better modelling. However, the DEMATEL method has an advantage by considering the mutual effects of each criterion and The MOORA considers both criterion sets with positive and negative impacts and thus provides coherent results in the decision making process. For that, the extended DEMATEL and MOORA methods are combined for the proposed approach. The proposed model includes two stages that are IT2 fuzzy DEMATEL and IT2 fuzzy MOORA for weighting the criterion set and ranking alternatives respectively. The model is summarized in the following section.

4.1 Model construction

A hybrid decision making model is structured based on interval type-2 fuzzy sets for selecting the best investment strategies for the European tourism industry. The details of the model are illustrated in Fig. 1.

Fig.1

The flowchart of the proposed hybrid decision making model for investment strategies of the European Tourism Industry.

The proposed model is summarized in the following steps.

The problem is defined for constructing the decision making model. The investment strategies of the European tourism industry are handled for ranking them.

The set of dimension and criteria are defined for weighting them. Literature-based factors are selected for each dimension and criterion.

An expert team is appointed for providing the linguistic evaluations of factors and alternatives and the expert scores are collected for constructing the relation and decision matrices.

The scores are converted into the numbers based on interval type-2 fuzzy sets for providing the fuzzy matrices.

The normalization procedures are applied for total influence fuzzy matrix and the defuzzified values are obtained to weight the dimensions and criteria.

The aggregated ratings and defuzzified values are computed for the normalized matrix.

The weighted values are provided by multiplying the weights of criteria and the normalized values

Total effects of positive and negative criteria are calculated for ranking alternatives.

Initial step of the proposed hybrid decision making model is to define the problem of investment strategies for the European tourism industry. For this purpose, the investment strategies of the European tourism industry are defined by considering global changes in the sector. Table 1 shows the strategies for the European tourism industry.

Table 1

Investment Strategies for the European Tourism Industry

Purpose	Alternative	Definition
strategies
Increasing strength by benefiting from opportunity	A1	Making advertising investments to increase global demand (A1)
	A2	Increase the number of facilities for coastal areas which have global transportation convenience (A2)
Increasing strength by avoiding threat	A3	Increasing private security investments in historical areas to reduce the risk of terrorism (A3)
	A4	Not reflecting foreign currency risks to the customers by making pricing policies with the hedging methods (A4)
Decreasing weakness by benefiting from opportunity	A5	Providing services in local languages by tourism companies to benefit from the increasing global tourism demand (A5)
	A6	Providing all-inclusive packets for the customers who are sensitive to high security (A6)
Decreasing weakness by avoiding threat	A7	Establishing new tourist facilities which provide competitive pricing policy (A7)
	A8	Providing all-inclusive packets to the customers with local currencies (A8)

Secondly, a set of criteria and dimensions of investment strategies is defined as more historical places than other regions (Criterion 1), more of available coastline (Criterion 2), well-established and experienced tourism companies (Criterion 3) for strength (Dimension 1); inadequate tourist services for tourists who do not speak sufficient English (Criterion 4), inadequate number of tourist facilities (Criterion 5), relatively fewer hotels with all-inclusive packets (Criterion 6) for weakness (Dimension 2); high security measures at entry to Europe (Criterion 7), increase in global population (Criterion 8), easy access to Europe from many different regions in the world (Criterion 9) for opportunity (Dimension 3); terrorist attacks (Criterion 10), increase in popularity of other touristic areas (Criterion 11), volatility in currency exchange rates (Criterion 12) for threat (Dimension 4) respectively.

In the following process, 3 decision makers who are experts in the field of global tourism industry are appointed to evaluate the criteria, dimensions, and investments strategies for the European tourism industry. They provide the linguistic evaluations and then, the results are converted to the interval type-2 fuzzy numbers for interval type-2 fuzzy evaluations. The details of evaluations for factors and alternatives are given in Tables 2 and 3 respectively.

Table 2

Linguistic Evaluations and Interval Type 2 Fuzzy Numbers for the Criteria and Dimensions

Linguistic Scales	Interval Type 2 Fuzzy Numbers
Very very low (VVL)	((0,0.1,0.1,0.2;1,1), (0.05,0.1,0.1,0.15;0.9,0.9))
Very low (VL)	((0.1,0.2,0.2,0.35;1,1), (0.15,0.2,0.2,0.3;0.9,0.9))
Low (L)	((0.2,0.35,0.35,0.5;1,1), (0.25,0.35,0.35,0.45;0.9,0.9))
Medium (M)	((0.35,0.5,0.5,0.65;1,1), (0.4,0.5,0.5,0.6;0.9,0.9))
High (H)	((0.5,0.65,0.65,0.8;1,1), (0.55,0.65,0.65,0.75;0.9,0.9))
Very high (VH)	((0.65,0.8,0.8,0.9;1,1), (0.7,0.8,0.8,0.85;0.9,0.9))
Very very high (VVH)	((0.8,0.9,0.9,1;1,1), (0.85,0.9,0.9,0.95;0.9,0.9))

Source: [5].

Table 3

Linguistic Evaluations and Interval Type 2 Fuzzy Numbers for the Alternatives

Linguistic Scales	Interval Type 2 Fuzzy Numbers
Very Poor (VP)	((0,0,0,0.1;1,1), (0,0,0,0.05;0.9,0.9))
Poor (P)	((0,0.1,0.1,0.3;1,1), (0.05,0.1,0.1,0.2;0.9,0.9))
Medium Poor (MP)	((0.1,0.3,0.3,0.5;1,1), (0.2,0.3,0.3,0.4;0.9,0.9))
Fair (F)	((0.3,0.5,0.5,0.7;1,1), (0.4,0.5,0.5,0.6;0.9,0.9))
Good (G)	((0.5,0.7,0.7,0.9;1,1), (0.6,0.7,0.7,0.8;0.9,0.9))
Very Good (VG)	((0.7,0.9,0.9,1;1,1), (0.8,0.9,0.9,0.95;0.9,0.9))
Best (B)	((0.9,1,1,1;1,1), (0.95,1,1,1;0.9,0.9))

Source: [5, 13].

Expert scores for the criteria, dimensions, and alternatives are obtained in the linguistic scales and the results are shown in the following sections.

4.2 Analysis results

4.2.1 Weighting the dimensions and criteria with IT2 fuzzy DEMATEL

In the first phase of the analysis with IT2 fuzzy DEMATEL, direct relation matrix is created with the process of converting linguistic terms to the fuzzy numbers. Within this framework, averaged values of the decision makers’ evaluations are taken into the consideration. The details of this matrix are given on Table 4.

Table 4
Initial direct relation matrix for the dimensions

	D1	D2	D3	D4
D1	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))	((0.45, 0.60, 0.60, 0.75; 1, 1), (0.50, 0.60, 0.60, 0.70; 0.90, 0.90))	((0.60, 0.75, 0.75, 0.87; 1, 1), (0.65, 0.75, 0.75, 0.82; 0.90, 0.90))	((0.45, 0.60, 0.60, 0.75; 1, 1), (0.50, 0.60, 0.60, 0.70; 0.90, 0.90))
D2	((0.30, 0.45, 0.45, 0.60; 1, 1), (0.35, 0.45, 0.45, 0.55; 0.90, 0.90))	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))	((0.30, 0.45, 0.45, 0.60; 1, 1), (0.35, 0.45, 0.45, 0.55; 0.90, 0.90))	((0.45, 0.60, 0.60, 0.75; 1, 1), (0.50, 0.60, 0.60, 0.70; 0.90, 0.90))
D3	((0.55, 0.70, 0.70, 0.83; 1, 1), (0.60, 0.70, 0.70, 0.78; 0.90, 0.90))	((0.40, 0.55, 0.55, 0.70; 1, 1), (0.45, 0.55, 0.55, 0.65; 0.90, 0.90))	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))	((0.40, 0.55, 0.55, 0.70; 1, 1), (0.45, 0.55, 0.55, 0.65; 0.90, 0.90))
D4	((0.50, 0.65, 0.65, 0.80; 1, 1), (0.55, 0.65, 0.65, 0.75; 0.90, 0.90))	((0.60, 0.75, 0.75, 0.87; 1, 1), (0.65, 0.75, 0.75, 0.82; 0.90, 0.90))	((0.40, 0.55, 0.55, 0.70; 1, 1), (0.45, 0.55, 0.55, 0.65; 0.90, 0.90))	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))

After that, this matrix is normalized by considering the maximum value of sum of the rows defined as vector r. This matrix is detailed in Table 5.

Table 5

The normalized direct-relation matrix for the dimensions

	D1	D2	D3	D4
D1	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))	((0.19, 0.25, 0.25, 0.32; 1, 1), (0.21, 0.25, 0.25, 0.30; 0.90, 0.90))	((0.25, 0.32, 0.32, 0.37; 1, 1), (0.27, 0.32, 0.32, 0.35; 0.90, 0.90	((0.19, 0.25, 0.25, 0.32; 1, 1), (0.21, 0.25, 0.25, 0.30; 0.90, 0.90))
D2	((0.13, 0.19, 0.19, 0.25; 1, 1), (0.15, 0.19, 0.19, 0.23; 0.90, 0.90))	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))	((0.13, 0.19, 0.19, 0.25; 1, 1), (0.15, 0.19, 0.19, 0.23; 0.90, 0.90))	((0.19, 0.25, 0.25, 0.32; 1, 1), (0.21, 0.25, 0.25, 0.30; 0.90, 0.90))
D3	((0.23, 0.30, 0.30, 0.35; 1, 1), (0.25, 0.30, 0.30, 0.33; 0.90, 0.90))	((0.17, 0.23, 0.23, 0.30; 1, 1), (0.19, 0.23, 0.23, 0.27; 0.90, 0.90))	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))	((0.17, 0.23, 0.23, 0.30; 1, 1), (0.19, 0.23, 0.23, 0.27; 0.90, 0.90))
D4	((0.21, 0.27, 0.27, 0.34; 1, 1), (0.23, 0.27, 0.27, 0.32; 0.90, 0.90))	((0.25, 0.32, 0.32, 0.37; 1, 1), (0.27, 0.32, 0.32, 0.35; 0.90, 0.90))	((0.17, 0.23, 0.23, 0.30; 1, 1), (0.19, 0.23, 0.23, 0.27; 0.90, 0.90))	((0, 0, 0, 0; 1, 1), (0, 0, 0, 0; 0.90, 0.90))

In the next process, identity matrix is created. By calculating the difference between normalized and identity matrixes, total relation matrix can be created which is demonstrated on Table 6.

Table 6

The total relation matrix for the dimensions

	D1	D2	D3	D4
D1	((0.23, 0.23, 0.68, 3.99; 1, 1), (0.33, 0.68, 0.68, 1.71; 0.90, 0.90))	((0.40, 0.91, 0.91, 4.35; 1, 1), (0.52, 0.91, 0.91, 1.99; 0.90, 0.90))	((0.43, 0.91, 0.91, 4.17; 1, 1), (0.53, 0.91, 0.91, 1.92; 0.90, 0.90	((0.38, 0.87, 0.87, 4.19; 1, 1), (0.49, 0.87, 0.87, 1.92; 0.90, 0.90))
D2	((0.28, 0.71, 0.71, 3.64; 1, 1), (0.38, 0.71, 0.71, 1.64; 0.90, 0.90))	((0.18, 0.58, 0.58, 3.54; 1, 1), (0.26, 0.58, 0.58, 1.50; 0.90, 0.90))	((0.28, 0.70, 0.70, 3.56; 1, 1), (0.37, 0.70, 0.70, 1.60; 0.90, 0.90))	((0.33, 0.74, 0.74, 3.64; 1, 1), (0.42, 0.74, 0.74, 1.66; 0.90, 0.90))
D3	((0.40, 0.87, 0.87, 4.08; 1, 1), (0.50, 0.87, 0.87, 1.88; 0.90, 0.90))	((0.37, 0.86, 0.86, 4.16; 1, 1), (0.48, 0.86, 0.86, 1.90; 0.90, 0.90))	((0.21, 0.63, 0.63, 3.74; 1, 1), (0.30, 0.63, 0.63, 1.59; 0.90, 0.90))	((0.35, 0.82, 0.82, 4.01; 1, 1), (0.45, 0.82, 0.82, 1.83; 0.90, 0.90))
D4	((0.40, 0.89, 0.89, 4.22; 1, 1), (0.51, 0.89, 0.89, 1.94; 0.90, 0.90))	((0.45, 0.95, 0.95, 4.36; 1, 1), (0.56, 0.95, 0.95, 2.01; 0.90, 0.90))	((0.36, 0.85, 0.85, 4.11; 1, 1), (0.47, 0.85, 0.85, 1.87; 0.90, 0.90))	((0.22, 066, 0.66, 3.94; 1, 1), (0.31, 0.66, 0.66, 1.68; 0.90, 0.90))

Moreover, with the help of defuzzification methodology, the defuzzified total relation matrix is generated and detailed on Table 7.

Table 7

Defuzzified total relation matrix and the weights for the dimensions

	D1	D2	D3	D4	r	y	r+y	r-y	Weights
D1	1.10	1.34	1.31	1.29	5.04	4.76	9.80	0.28	0.26
D2	1.08	0.96	1.06	1.11	4.21	4.95	9.15	-0.74	0.24
D3	1.27	1.27	1.03	1.22	4.79	4.65	9.44	0.13	0.25
D4	1.31	1.37	1.26	1.09	5.02	4.70	9.72	0.32	0.25

Similar procedures are also computed for the criteria of each dimension. Defuzzified values and weights of the criteria of the dimensions are also computed. After this process, local and global weights of the dimensions and criteria can be calculated. The details of the values are shown on Table 8.

Table 8

Local and global weights of the strategic dimensions and criteria for the European Tourism Industry

Dimensions	Local weights	Criteria	Local weights	Global weights
Strength (D1)	0.257	C1	0.331	0.085
		C2	0.340	0.087
		C3	0.330	0.085
Weakness (D2)	0.240	C4	0.326	0.078
		C5	0.343	0.082
		C6	0.331	0.079
Opportunity (D3)	0.248	C7	0.340	0.084
		C8	0.322	0.080
		C9	0.338	0.084
Threat (D4)	0.255	C10	0.358	0.091
		C11	0.362	0.092
		C12	0.280	0.072

Table 8 demonstrates that strength (D1) is the most significant dimension with the weight of 0.257. Similar to this situation, it is also identified that threat (D4) has the second highest weight (0.255). On the other side, opportunity (D3) and threat (D4) are other dimensions that have lowest weight. While considering these results, European countries should firstly focus on their strengths and potential threat in the strategy development process. Owing to this aspect, it can be much easier to increase the performance of European tourism industry. [51 , 40] also underlined the importance of this issue in their studies.

In addition to the dimensions, Table 8 also gives information about the weights of the criteria. It is concluded that the criterion of “increase in popularity of other touristic areas (C11)” has the highest importance among all criteria with the weight of 0.092. Parallel to this aspect, the criteria of “Terrorist attacks (C10)”, “More of available coastline (C2)” and “Well-established and experienced tourism companies (C3)” have also highest weights. These results show that tourism popularity of other areas should be firstly taken into the consideration by European countries in the process of generating tourism strategies. Moreover, these countries should also consider their potential, such as coastline and experienced tourism companies in this process. The same issues are also emphasized in many different studies in the literature [8 , 33].

4.2.2 Ranking the alternatives with IT2 fuzzy MOORA

In the first aspect, the fuzzy decision matrix is defuzzified for interval type-2 fuzzy sets. The details of this matrix are demonstrated on Table 9.

Table 9
Defuzzified values of fuzzy decision matrix

A1 A2 A3 A4 A5 A6 A7 A8

C1 9.25 7.07 9.25 6.27 7.47 7.07 7.47 7.47

C2 9.47 9.47 5.47 7.07 6.27 7.87 9.25 8.86

C3 9.69 9.47 7.47 8.86 8.64 9.47 9.47 9.25

C4 6.27 6.27 5.47 5.87 9.69 9.69 8.26 6.67

C5 7.47 5.87 5.87 6.27 8.26 9.25 9.47 8.86

C6 7.47 5.87 5.87 6.27 6.27 7.07 8.86 9.47

C7 8.46 7.07 9.47 5.47 5.87 7.07 5.87 5.87

C8 9.47 9.25 8.64 8.64 7.87 8.26 8.26 8.86

C9 8.26 9.69 8.64 8.64 9.25 7.47 7.87 8.26

C10 7.07 6.27 9.47 5.47 5.87 9.69 5.47 5.47

C11 9.47 9.25 5.87 9.25 9.47 9.25 9.25 8.86

C12 7.47 7.07 5.47 9.69 6.67 6.27 8.64 9.69

	A1	A2	A3	A4	A5	A6	A7	A8
C1	9.25	7.07	9.25	6.27	7.47	7.07	7.47	7.47
C2	9.47	9.47	5.47	7.07	6.27	7.87	9.25	8.86
C3	9.69	9.47	7.47	8.86	8.64	9.47	9.47	9.25
C4	6.27	6.27	5.47	5.87	9.69	9.69	8.26	6.67
C5	7.47	5.87	5.87	6.27	8.26	9.25	9.47	8.86
C6	7.47	5.87	5.87	6.27	6.27	7.07	8.86	9.47
C7	8.46	7.07	9.47	5.47	5.87	7.07	5.87	5.87
C8	9.47	9.25	8.64	8.64	7.87	8.26	8.26	8.86
C9	8.26	9.69	8.64	8.64	9.25	7.47	7.87	8.26
C10	7.07	6.27	9.47	5.47	5.87	9.69	5.47	5.47
C11	9.47	9.25	5.87	9.25	9.47	9.25	9.25	8.86
C12	7.47	7.07	5.47	9.69	6.67	6.27	8.64	9.69

After that, with the help of vector normalization, defuzzified values are normalized. Within this framework, the normalized matrix is defined as dimensionless number and, positive and negative effects of this matrix is calculated. In this process, the weights, identified by interval type 2 DEMATEL analysis, are taken into the consideration. As a result of this situation, alternatives are ranked, and the results are demonstrated on Table 10.

Table 10

Benefit-cost criteria and ranking of the investment strategies

Alternatives	Benefit criteria	Cost criteria	Y_i values	Ranking
A1	0.199	0.172	0.028	3
A2	0.189	0.154	0.035	1
A3	0.180	0.148	0.032	2
A4	0.162	0.160	0.002	4
A5	0.164	0.174	-0.010	6
A6	0.172	0.197	-0.025	8
A7	0.175	0.187	-0.013	7
A8	0.176	0.184	-0.008	5

Table 10 shows that the alternative of “Increase the number of facilities for coastal areas which have global transportation convenience (A2)” is on the first rank regarding investment strategies. Similarly, “increasing private security investments in historical areas to reduce the risk of terrorism (A3)” is also the alternative which has the second highest importance. A2 is a type of strategy in which the strengths of European tourism industry can be used effectively. Parallel to this aspect, in A3 alternative, the strengths of European tourism industry and possible threats can be taken into the consideration at the same time. The findings show that European countries should firstly focus on increasing the number of touristic facilities in the coast line. Therefore, it can be possible to attract the attention of the coastal visitors. This conclusion is also stated in some studies in the literature [40, 25]. Another important point is that European countries should make more private security investment so that tourists can feel themselves more secured while visiting historical places. This situation has a positive influence on the development of the tourism in Europe. [41, 33] defined the importance of this issue in their studies.

5 Discussion and conclusion

5.1 Discussion

Tourism has an important contribution to the economic development, such as increasing economic growth and decreasing unemployment. Thus, countries aim to develop strategies in order to have sustainable tourism. Tourism is also a very significant industry for Europe. This industry has a significant percentage in the GDP amount of Europe. Hence, it is obvious that European countries should develop effective tourism strategies to increase this income. Europe has lots of strengths with respect to the tourism like having many historical places, coastlines and well-established and experienced tourism companies. There are also some external factors that may affect the tourism performance of Europe. For example, increase in global population can be accepted an important opportunity whereas terrorism leads to significant threat for the tourism industry. Therefore, it is obvious that European countries should consider many different factors at the same time to develop investment strategies to develop tourism.

The investment strategy selection is one of prominent issues for the effective decision making in the European tourism industry. For this scope, a hybrid decision making approach based on the interval type-2 fuzzy sets is taken into the consideration. Within this scope, 4 different dimensions and 12 different criteria are identified based on SWOT analysis by analyzing similar studies in the literature. In addition to them, 8 different investment strategies for the European tourism industry are determined with the help of these dimensions and criteria. Moreover, interval type-2 fuzzy DEMATEL methodology is used to weight dimensions and criteria while interval type-2 fuzzy MOORA approach is considered to rank the alternative strategies.

In this study, it is aimed to identify the best investment strategy for European tourism industry. Within this context, the analysis is performed under fuzzy environment to reach more meaningful results. There are several recent studies on business and finance with the fuzzy DEMATEL [15, 18] and fuzzy MOORA [22, 23]. However, the social studies with IT2 fuzzy DEMATEL and IT2 fuzzy MOORA are extremely limited in the literature [66 , 17]. Accordingly, using the extend method of DEMATEL and MOORA is provided based on interval type-2 fuzzy increases the originality for selecting the investment strategies of the European Tourism Industry. Thus, it is believed that this study makes a contribution to the literature.

5.2 Conclusion

As a result of interval type-2 fuzzy DEMATEL, it is concluded that strength and weakness are the most important dimensions for the European tourism industry. Additionally, “increase in popularity of other touristic areas”, “terrorist attacks” and “more of available coastline” are the most significant criteria. While considering these results, it is identified that European countries should firstly focus on their strengths and potential threat in the strategy development process to increase tourism performance. Also, tourism popularity of other areas should be taken into the consideration by European countries in this process.

In addition, it is determined that “increase the number of facilities for coastal areas which have global transportation convenience” is the best investment strategy for European tourism industry. Similarly, it is also concluded that “increasing private security investments in historical areas to reduce the risk of terrorism” is another outstanding investment strategy. The findings show that European countries should firstly focus on increasing the number of touristic facilities in the coast line to attract the attention of the tourists. Another important point is that European countries should make more private security investment for the tourists to feel more secured while visiting historical places.

The overall results demonstrate that The European tourism industry should mostly construct the innovative investment strategies that have a strength dimension of the tourism industry. Especially, the combining strategies of strengths and opportunities have the most prominent issues for the European tourism investments. Accordingly, [41, 43] provide the similar results on the opportunities of the European tourism industry. And also, [54, 55] highlights the challenges for the European tourism policies. Because, Europe is a critical region with respect to the tourism. First of all, in European area, there are lots of historical places and coast line [11, 40]. Another important point is that most of the tourism companies are well-established and experienced in European countries [57]. This situation makes an important contribution to the power of this region for tourism. There are also some external factors that can affect tourism in Europe. For example, increasing population in the world can be accepted as the opportunity to develop tourism [51]. On the other side, terrorist attacks decrease the motivation of the people to make touristic visit [29]. Because of this condition, it is obvious that structural reforms can be developed by European countries to have sustainable tourism while considering these aspects. Nevertheless, the study could be widen by using the different decision making models such as VIKOR and TOPSIS based on interval type-2 fuzzy sets. Additionally, the ranking alternatives could be extended by considering the tourism industries of the emerging economies for the further studies.

References

Abdullah

and Zulkifli

, Integration of fuzzy AHP and interval type-2 fuzzy DEMATEL: An application to human resource management, Expert Systems with Applications 42(9) (2015), 4397–4409.

Antonakakis

, Dragouni

and Filis

, How strong is the linkage between tourism and economic growth in Europe?, Economic Modelling 44 (2015), 142–155.

Arbulú

, Lozano

and Rey-Maquieira

, Tourism and solid waste generation in Europe: A panel data assessment of the Environmental Kuznets Curve, Waste management 46 (2015), 628–636.

Aytuğ

H.K.

and Mikaeili

, Evaluation of Hopa’s Rural Tourism Potential in the Context of European Union Tourism Policy, Procedia Environmental Sciences 37 (2017), 234–245.

Baykasoğglu

and Gölcük

İ.

, Development of an interval type-2 fuzzy sets based hierarchical MADM model by combining DEMATEL and TOPSIS, Expert Systems with Applications 70 (2017), 37–51.

Baykasoğglu

, Kaplanoğlu

, Durmuşoğlu

Z.D.

and şahin

, “Integrating fuzzy DEMATEL and fuzzy hierarchical TOPSIS methods for truck selection”, Expert Systems with Applications 40(3) (2013), 899–907.

Bitsani

and Kavoura

, Host Perceptions of Rural Tour Marketing to Sustainable Tourism in Central Eastern Europe. The Case Study of Istria, Croatia, Procedia-Social and Behavioral Sciences 148 (2014), 362–369.

Blancas

F.J.

, Lozano-Oyola

and González

, A European sustainable tourism labels proposal using a composite indicator, Environmental Impact Assessment Review 54 (2015), 39–54.

Bowtell

, Assessing the value and market attractiveness of the accessible tourism industry in Europe: A focus on major travel and leisure companies, Journal of Tourism Futures 1(3) (2015), 203–222.

10.

Brauers

W.K.M.

and Zavadskas

E.K.

, The MOORA method and its application to privatization in a transition economy, Control and Cybernetics 35(2) (2006), 445–469.

11.

Briec

, Kerstens

, Prior

and Van de Woestyne

, Testing general and special Färe-Primont indices: A proposal for public and private sector synthetic indices of European regional expenditures and tourism, European Journal of Operational Research (2018).

12.

Chatziantoniou

, Filis

, Eeckels

and Apostolakis

, Oil prices, tourism income and economic growth: A structural VAR approach for European Mediterranean countries, Tourism Management 36 (2013), 331–341.

13.

Chen

S.M.

and Lee

L.W.

, Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method, Expert systems with applications 37(4) (2010), 2790–2798.

14.

Chou

Y.C.

, Sun

C.C.

and Yen

H.Y.

, Evaluating the criteria for human resource for science and technology (HRST) based on an integrated fuzzy AHP and fuzzy DEMATEL approach, Applied Soft Computing, 12(1) (2012), 64–71.

15.

Dincer

, HHI-based evaluation of the European banking sector using an integrated fuzzy approach, Kybernetes (2018).

16.

Dincer

and Hacioglu

, Investigating the Economic Vulnerability Factors of Emerging Markets After the Global Financial Crisis of With a Hybrid Multi-Criteria Decision-Making Approach, InHandbook of Research on Economic, Financial, and Industrial Impacts on Infrastructure Development (pp. 313–333). IGI Global (2017).

17.

Dincer

and Yuksel

, IT2-based Fuzzy Hybrid Decision Making Approach to Soft Computing, IEEE Access (2019)a.

18.

Dinçer

and Yüksel

, Multidimensional evaluation of global investments on the renewable energy with the integrated fuzzy decision-making model under the hesitancy, International Journal of Energy Research (2019)b.

19.

Dinçer

, Hacíoğlu

Ü.

and Yüksel,

, Balanced scorecard based performance measurement of European airlines using a hybrid multicriteria decision making approach under the fuzzy environment, Journal of Air Transport Management 63 (2017), 17–33.

20.

Dinçer

, Akdeniz

Olgu O.

and Hacioglu

, Competitive strategy selection in the European banking sector using a hybrid decision-making approach, Zbornik radova Ekonomskog fakulteta u Rijeci: časopis za ekonomsku teoriju i praksu 36(1) (2018)a, 211–240.

21.

Dincer

, Ümit

and Serhat

, Performance assessment of deposit banks with CAMELS analysis using fuzzy ANP-moora approaches and an application on Turkish banking sector, Asian Journal of Research in Business Economics and Management 6(2) (2016), 32–56.

22.

Dinçer

, Yuksel

and Bozaykut-Buk

, Evaluation of Financial and Economic Effects on Green Supply Chain Management With Multi-Criteria Decision-Making Approach: Evidence From Companies Listed in BIST, InHandbook of Research on Supply Chain Management for Sustainable Development (pp. 144–175). IGI Global, (2018)b.

23.

Dinçer

, Yüksel

and Pínarbaşí

, SERVQUAL-Based Evaluation of Service Quality of Energy Companies in Turkey: Strategic Policies for Sustainable Economic Development, InThe Circular Economy and Its Implications on Sustainability and the Green Supply Chain (pp. 142–167). IGI Global (2019)a.

24.

Dinçer

, Yüksel

, Korsakienė

, Raišienė

A.G.

and Bilan

, IT2 Hybrid Decision-Making Approach to Performance Measurement of Internationalized Firms in the Baltic States, Sustainability 11(1) (2019)b, 296.

25.

Silva

F.B.e

, Herrera

M.A.M.

, Rosina

, Barranco

R.R.

, Freire

and Schiavina

, Analysing spatiotemporal patterns of tourism in Europe at high-resolution with conventional and big data sources, Tourism Management 68 (2018), 101–115.

26.

Estol

and Font

, European tourism policy: Its evolution and structure, Tourism Management 52 (2016), 230–241.

27.

Ferrante

, Magno

G.L.L.

and De Cantis

, Measuring tourism seasonality across European countries, Tourism Management 68 (2018), 220–235.

28.

Font

, Garay

and Jones

, Sustainability motivations and practices in small tourism enterprises in European protected areas, Journal of Cleaner production 137 (2016), 1439–1448.

29.

Gabor

M.R.

, Conţiu

L.C.

and Oltean

F.D.

, A comparative analysis regarding European tourism competitiveness: Emerging versus developed markets, Procedia Economics and Finance 3 (2012), 361–366.

30.

S.K.

, Lu

M.T.

and Tzeng

G.H.

, “Exploring smart phone improvements based on a hybrid MCDM model”, Expert Systems with Applications 41(9) (2014), 4401–4413.

31.

Jafari-Eskandari

, Roudabr

and Kamfiroozi

M.H.

, Banks’ Performance Evaluatíon Model Based on the Balanced Score Card Approach, Fuzzy Dematel and Analytíc Network Process, International Journal of Information, Security and Systems Management 2(2) (2013), 191–200.

32.

Jeng

D.J.F.

and Tzeng

G.H.

, “Social influence on the use of clinical decision support systems: Revisiting the unified theory of acceptance and use of technology by the fuzzy DEMATEL technique”, Computers & Industrial Engineering 62(3) (2012), 819–828.

33.

Jovanović

, Janković-Milić

and Ilić

, Health and Hygiene Importance for the Improvement of Tourism Sector Competitiveness in Serbia and the South-Eastern Europe Countries, Procedia Economics and Finance 19 (2015), 373–382.

34.

Kahraman

, Öztayşi

, Sarı

İ.U.

and Turanoğlu

, Fuzzy analytic hierarchy process with interval type-2 fuzzy sets, Knowledge-Based Systems 59 (2014), 48–57.

35.

Karande

and Chakraborty

, Application of multi-objective optimization on the basis of ratio analysis (MOORA) method for materials selection, Materials and Design 37 (2012), 317–324.

36.

Kracka

, Brauers

W.K.M.

and Zavadskas

E.K.

, Ranking heating losses in a building by applying the MULTIMOORA, Inzinerine Ekonomika-Engineering Economics 21(4) (2010), 352–359.

37.

Kuo

M.S.

, “Optimal location selection for an international distribution center by using a new hybrid method”, Expert Systems with Applications 38(6) (2011), 7208–7221.

38.

Lee

J.W.

and Brahmasrene

, Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union, Tourism Management 38 (2013), 69–76.

39.

Liburd

J.J.

, Mihalič

and Guia

, Values in tourism higher education: The European master in tourism management, Journal of Hospitality, Leisure, Sport and Tourism Education 22 (2018), 100–104.

40.

Makkonen

, Williams

A.M.

, Weidenfeld

and Kaisto

, Cross-border knowledge transfer and innovation in the European neighbourhood: Tourism cooperation at the Finnish-Russian border, Tourism Management 68 (2018), 140–151.

41.

Marrocu

and Paci

, They arrive with new information. Tourism flows and production efficiency in the European regions, Tourism Management 32(4) (2011), 750–758.

42.

Matawale

C.R.

, Matawale

C.R.

, Datta

, Mahapatra

S.S.

and Mahapatra

S.S.

, Supplier selection in agile supply chain: Application potential of FMLMCDM approach in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA., Benchmarking: An International Journal 23(7) (2016), 2027–2060.

43.

Matlay

and Westhead

, Innovation and collaboration in virtual teams of e-entrepreneurs: Case evidence from the European tourism industry, The International Journal of Entrepreneurship and Innovation 8(1) (2007), 29–36.

44.

Mavi

R.K.

, Kazemi

, Najafabadi

A.F.

and Mousaabadi

H.B.

, Identification and assessment of logistical factors to evaluate a green supplier using the fuzzy logic DEMATEL method, Polish journal of environmental studies 22(2) (2013), 445–455.

45.

Mendel

J.M.

, John

R.I.

and Liu

F.L.

, Interval type-2 fuzzy logical systems made simple, IEEE Transactions on Fuzzy Systems 14(6) (2006), 808–821.

46.

Middleton

, The importance of micro-businesses in European tourism, Rural tourism and recreation: Principles to practice (2001), 197–201.

47.

Najafinasab

, Karbassi

A.R.

and Ghoddousi

, “Fuzzy analytic network process approach to evaluate land and sea criteria for land use planning in coastal areas”, Ocean & Coastal Management 116 (2015), 368–381.

48.

Panfiluk

, Analysis of the effectiveness the European Regional Development Fund disbursement for the selected tourism services with the use of the counterfactual method, Procedia Engineering 182 (2017), 540–547.

49.

Paramati

S.R.

, Shahbaz

and Alam

M.S.

, Does tourism degrade environmental quality? A comparative study of Eastern and Western European Union, Transportation Research Part D: Transport and Environment 50 (2017), 1–13.

50.

Patil

S.K.

and Kant

, “A hybrid approach based on fuzzy DEMATEL and FMCDM to predict success of knowledge management adoption in supply chain”, Applied Soft Computing 18 (2014), 126–135.

51.

Romao

and Neuts

, Territorial capital, smart tourism specialization and sustainable regional development: Experiences from Europe, Habitat International 68 (2017), 64–74.

52.

Santos

and Cincera

, Tourism demand, low cost carriers and European institutions: The case of Brussels, Journal of Transport Geography (2018), In press, corrected proof, Available online 14 June 2018.

53.

Silva

and Leal

, Rural tourism and national identity building in contemporary Europe: Evidence from Portugal, Journal of Rural Studies 38 (2015), 109–119.

54.

Smeral

, The impact of globalization on small and medium enterprises: New challenges for tourism policies in European countries, Tourism management 19(4) (1998), 371–380.

55.

Smeral

, The impact of the financial and economic crisis on European tourism, Journal of Travel Research 48(1) (2009), 3–13.

56.

Stoica

, Orzan

, Dobrescu

and Radu

A.C.

, Online Marketing Communication Using Websites. A Case Study of Website Utility in Accessing European Funds in the Tourism Field Regarding Northeastern Romania, Procedia Economics and Finance 23 (2015), 926–935.

57.

Streifeneder

, Agriculture first: Assessing European policies and scientific typologies to define authentic agritourism and differentiate it from countryside tourism, Tourism Management Perspectives 20 (2016), 251–264.

58.

Tamajón

L.G.

and Font

, Corporate social responsibility in tourism small and medium enterprises evidence from Europe and Latin America, Tourism Management Perspectives 7 (2013), 38–46.

59.

Uygun

Ö.

, amak

KaçH.

and Kahraman

Ü. A.

, “An integrated DEMATEL and Fuzzy ANP techniques for evaluation and selection of outsourcing provider for a telecommunication company”, Computers & Industrial Engineering 86 (2015), 137–146.

60.

Wang

C.H.

and Chen

J.N.

, “Using quality function deployment for collaborative product design and optimal selection of module mix”, Computers & Industrial Engineering 63(4) (2012), 1030–1037.

61.

, Chang

, Cao

and Liang

, A trust propagation and collaborative filtering based method for incomplete information in social network group decision making with type-2 linguistic trust, Computers & Industrial Engineering 127 (2019)b, 853–864.

62.

, Chiclana

and Liao

, Isomorphic multiplicative transitivity for intuitionistic and interval-valued fuzzy preference relations and its application in deriving their priority vectors, IEEE Transactions on Fuzzy Systems 26(1) (2018), 193–202.

63.

, Sun

, Fujita

and Chiclana

, An attitudinal consensus degree to control the feedback mechanism in group decision making with different adjustment cost, Knowledge-Based Systems 164 (2019)a, 265–273.

64.

Yeh

T.M.

and Huang

Y.L.

, “Factors in determining wind farm location: Integrating GQM, fuzzy DEMATEL, and ANP”, Renewable Energy 66 (2014), 159–169.

65.

Yüksel

, Dinçer

and Emir

ş.

, Comparing the performance of Turkish deposit banks by using DEMATEL, Grey Relational Analysis (GRA) and MOORA approaches, World Journal of Applied Economics 3(2) (2017), 26–47.

66.

Yüksel

, Dinçer

and Meral

, Financial Analysis of International Energy Trade: A Strategic Outlook for EU-15, Energies 12(3) (2019), 431.

67.

Zurub

H.H.

, Ionescu

and Constantin

V.D.

, Measuring the Economic Impact of Tourism in European Emerging Markets, Procedia Economics and Finance 32 (2015), 95–102.

Selecting investment strategies for European Tourism Industry using the hybrid decision making approach based on interval type-2 fuzzy sets

Abstract

Keywords

1 Introduction

2 Literature review

3 Methodology

3.1 Interval type-2 fuzzy sets

4.1 Model construction

4.2.1 Weighting the dimensions and criteria with IT2 fuzzy DEMATEL

Table 4 Initial direct relation matrix for the dimensions

5.1 Discussion

5.2 Conclusion

References

Table 4
Initial direct relation matrix for the dimensions