Sustainable Tourism Supply Chain Assessment Using Hybrid Decision-Making Methods under Fuzzy Uncertainty

Abstract

In recent years, sustainable environmental issues have attracted attention from all circles due to environmental variation and the depletion of natural resources. Modern thinking about the environment contains the human environment, historical tradition, and social life of the whole ecosystem. In this research, the design, development, and construction of the environmental system are assessed. In this regard, sustainable development, environmental issues, venue administrators and staff, tourists’ education, and service quality evaluation are investigated. After correction to build four broad categories of 26 education venue service quality evaluation indexes, the ecological environment issues venue service quality of the Hakka Romantic Avenue project was conducted by questionnaire survey in Taiwan. Moreover, the VIKOR method is used for service quality evaluation of different subjects, sorting visitors to the evaluation results of the reference, finding the government supervisors, and environmental planning venue administrators. The results showed that the Service Preference (SERVPERF) model combined with Fuzzy-AHP and VIKOR method was effective in the ranking of service quality evaluation of environmental planning venues, and it was significant to eliminate the complacent tendency of the administrators of environmental planning venues to improve the service quality of education venues.

This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1. Introduction

In recent years, the environmental problems caused by global warming have become increasingly serious. In order to restore the environment after being damaged, it is not enough to engage in pollution prevention and ecological conservation [1–3]. The only way is to let people understand the seriousness of environmental problems and change their attitude. Therefore, environmental planning has become very important for a country, and some countries have taken measures to promote environmental planning [4–6]. In order to protect and promote the development of good and popularization of environmental education, various forms of environmental planning legislation are carried out. The 1972 United Nations Conference on the human environment issued a “declaration of Human” that caused humans to pay attention to environmental problems, the starting of the Human and Natural Environment harmonious co-existence of the benign interaction with the natural ecological concept in the new era. Environmental rehabilitation cannot catch up with the pace of human development, and the concept of coexistence with nature needs to be transmitted and internalized through education and be deeply rooted in the living environment so as to actively restore the overall operation of the large ecological system [7–10].

For environmental protection, people need to have the same feeling of “human being is one of all things” and rethink the relationship between humans and nature. Environmental planning is an education mode that exists in all aspects of people’s life. It is new thinking that obtains respect for ecology and the balance between manmade and ecology through living experience [11–13]. Its scope and projects are numerous and wide. At present, Taiwan has set up many environmental planning venues for citizens, aiming at strengthening the idea of implementing ecological environment education in living areas, looking for planning factors of ecological environment education field from existing regional conditions and community development, injecting local care of environmental conservation and cultural inheritance, and trying to achieve the goal of environmental sustainability [14–16].

Environmental planning venues not only assist local teaching but also bring about attention to local tourism. The motivation of this study is how to provide unique and innovative services to attract more tourists to environmental planning venues [17–19]. In the public sector management evaluation stage, the biggest problem is not yet establishing environmental planning venue in the Taiwan certification system and evaluation standard. At present, only less than 50% of the environment education venue permits a legal registration certificate. And the operator is doing everything they can to design features of leisure venues. The “service quality” is the good, and bad are intermingled, for tourists, as the advantages and disadvantages of environmental planning could not be distinguished, may be under the condition of insufficient information of the environmental planning of the lower quality of service venue, evaluation of environmental planning venue, and the shadow’s impression of the whole industry. The uneven quality of “service quality” will impact the development of environmental planning venues [20–23].

Industry supervisors, administrators, and staff of environmental planning venues and tourists, as the four main participants of environmental planning venues, stand from different perspectives of service providers and demanders, respectively, and there may be differences in evaluation results of environmental planning service quality [24–27]. According to the service gap model, service quality is ultimately determined by the gap between customer perceived performance and expectation, which is formed in other gaps in the delivery process of enterprise managers and front-line employees to customers [28–30]. Service providers estimate the quality of service is too high or underestimated or overestimated, can produce complacency, and make the service personnel service [31–33]. Peiró et al. [20] found that, from the awareness to the cognitive process, pleasant information will be processed to be more accurate and efficient, and service quality evaluation of employees will enlarge the information to form a “positive law” (Pollyanna Principle) [3]. Schneider et al. [23] confirmed the perception gap between the service provider and customer is negatively related to the final service quality evaluation. Unlike most of the services sector, the government subsidies for environmental planning venue administrators or staff in accordance with the requirements of industry management departments to provide the corresponding services, such as professional knowledge, dress, manners, etc., and accept the inspection; regulators will pass audit means adjustment of environmental planning venue or cancel subsidies amount [3, 6]. By establishing the gap model of environmental planning service quality, different types of perceived gaps in the service delivery process can be found, but to narrow or eliminate these gaps, the consistency of service quality evaluation of the participants should also be conducted, and the root causes of different gaps should be analyzed. Therefore, this paper takes the evaluation of tourist service quality as a reference to measure the consistency between the evaluation of service providers and tourists, which not only is conducive to the development of service guidelines according to the needs of tourists but also helps to implement regulatory policies in a tourist demand-oriented manner.

2. Literature Review

The definition of the word “environmental education” by UNESCO (United Nations Educational Scientific and Cultural Organization) is as follows: “the environment education is a kind of education process; in this process, personal and social understanding of their environment and the environment of the interaction between the biological, physical, and social culture, get the knowledge, skills, and values can individually or collectively solve the present and future environmental problems.” Many environmental pollution problems are caused by global warming, climate change, and industrialization. It affects the whole ecology. The more advanced the civilization, the more problems we have with the Earth’s resources. People care about nature and environment; however, it comes mainly from the scope of their exposure to the environment, so if we would like to raise public concern degree on the nature and environment, the environmental governances should be emphasized; despoiled tourism should be replaced by the ecological tourism formulate, which is a good method of environmental education. The concept of ecological tourism was first proposed by Hetzer in 1965 [11]. He called on cultural, educational, and tourism operators to rethink the meaning of “recreation” and first proposed the concept of “ecological tourism.” The concept of Eco-tourism is to provide tourists with the greatest satisfaction and bring the greatest economic efficiency to the local area with the minimum impact on the local culture and environment.

This study analyzes the definitions of several scholars, and the connotation of Eco-tourism should cover the following major projects: (1) Eco-tourism is based on resources. Some scholars emphasize that the resources used in Eco-tourism are mainly natural resources, especially landscapes and wildlife [18, 25]. In addition, Ziffer [29] believes that resources should include local historical relics and indigenous culture. (2) Most Eco-tourism activities take place in undisturbed natural areas: Ziffer [29] and Valentine [26] emphasize that Eco-tourism mainly goes to relatively undeveloped areas. (3) Special tourist motivation/purpose: The Eco-tourism society emphasizes that Eco-tourism is a purposeful journey to natural areas to learn about nature and culture; Boo [4] and Sirakaya [25] believed that appreciating, participating, feeling, learning, and studying scenery, plants and animals are the main motivation and purpose for tourists to participate in ecological tourism activities. Eco-tourism is not only an ecologically reliable journey but also an effort to strengthen protection through tourism. Accordingly, Eco-tourism is actually nature tourism that promotes conservation. (4) Concept of sustainable development: Buckley [5] emphasized that ecological tourism must be managed in the concept of sustainability; Eco-tourism emphasizes that recreation activities should reduce the impact on the local area and reduce the consumption of local resources to make the sustainable use of local resources and make the sustainable development of local tourism industry be repeated in relevant literature [26]. (5) Emphasis on the concept of conservation: Wight [28] argues that Eco-tourism must contribute to ecosystem conservation. Hvenegaard and Dearden [12] further believed that ecological tourism should emphasize the protection of the local natural environment in tourism and convey the important connotation of ecological tourism as an ecosystem. (6) Contribution to local communities: Martin [15] stresses that ecological tourism must maintain the welfare of local communities; (7) Responsible tourist behavior: Tourists must behave in a responsible way when participating in ecological tourism activities, and the so-called responsible tourist behavior means that there will be no tourist behavior that may cause damage to the environment [12, 15]. (8) The concept of local community participation: Ziffer [29] argues that ecological tourism implies local community participation in the tourism development model. The purpose is to make the development of local tourism meet the needs of the community and properly set marketing, tourism management regulations of the standards and industry, and reasonable financial sources to support and improve the quality of resources and environment of the residential area.

Parasuraman et al. [17, 18] hold that service quality is the order and condition of service in the process of delivery and the interaction between service providers and consumers. Parasuraman et al. [17, 18] defined the service quality in the form of user authentication, the quality of service is defined as a gap, and SERVQUAL (Service Quality) model is established. Reichheld and Sasser [19] adopted a “quality of technology” and “functional” quality dimensions to measure the service quality and formulated the multi-item of agricultural tourism service quality attributes in accordance with the technical and functional dimensions proposed by Fleischer and Pizam [10] under the measure of multi-item. Akama and Kieti [1] investigated the satisfaction of service quality in a national park in Kenya’s wildlife safari, where the service quality dimensions are measured by five service quality dimensions [18], and they formulated national park service-oriented quality attributes to further study and explore the visitors’ satisfaction. Khan et al. [13] discussed the expected service quality of participation in Eco-tourism. Through factor analysis, the desired service quality will be involved in ecological tourists’ identified six dimensions , respectively, “ecological tangibility,” “authenticity,” and “reliability,” “reactivity,” and “empathy” and “tangibles,” and name it “ECOSERV.” They set up a pointer in the direction of service quality in Eco-tourism and concluded that the “ecological tangible service quality for ecological tourism" is one of the most overlooked by tourists’ dimensions; the tangible behavior of visitors is friendly to the environment, with minor damage. This study also verified the feasibility of the application of service quality in the study of ecological tourism.

Cronin and Taylor [9] proposed a revised and improved version of the SERVQUAL evaluation model of Parasuraman et al. [17, 18] and came up with a service quality evaluation method, which is the inheritance and development of the SERVQUAL evaluation method. The SERVPERF (Service Performance) evaluation model is also known as the performance perception service quality evaluation method. The SERVPERF model abandons the SERVQUAL concept of difference comparison, takes the customer perception performance evaluation score as the measurement standard, and measures the service quality with 22 indicators in 5 dimensions as the scale. Five dimensions include tangibility of physical facilities, equipment needed for the service, guarantee (such as needed to complete the service skills, knowledge of affordable factors), responsiveness (timely and active ability to fulfill the service content), reliability (the ability to service commitments accurately), and empathy (people-oriented, service level upgradeability). Subsequently, the SERVPERF model has been widely used in measuring the service quality of catering, hotel, tourism, railway, port, and air transport. In view of the invisibility, synchronicity, difference, and nonstorage of tourist services, the modified SERVPERF model was used to measure the quality of tourist services in this study.

3. Methodology

3.1. Evaluation Index

We modified 22 multi-item on the basis of the classical SERVPERF model so as to conform to the characteristics of tourist service quality and delivered the questionnaire to five experts with respect to ecology, industry, and enterprise management. The experts reviewed, revised, deleted, and finally retained the 26 multi-item after classification as the evaluation index, and four categories in this questionnaire are interpretation services, facilities, recreation facilities, and spiritual experience, as shown in Table 1.

Table 1

Service quality evaluation index of environmental planning venues (Q).

Index type	Index notation	Evaluation content
Interpretation services	Q11	The narrator’s explanation was clear and intelligible
	Q12	The narrator’s voice sounded just right
	Q13	The narrator and tourists have good interactive
	Q14	The narrators know how to use body language to strengthen interpretation content
	Q15	To let me know environmental resources treatment and cherish the focus of the environmental resources
	Q16	The narrator’s explanation form and process makes me feel satisfied
	Q17	Content on the venue with announcer richness
	Q18	The narrators on the interpretation of content have sufficient preparation
	Q19	Time arrangement of the interpretation is very appropriate
Interpretation facilities	Q21	Explanation on the placard setting place carefully design
	Q22	Explanation of the font size of clear moderate site on the placard
	Q23	Visit route smooth
	Q24	Placards with auxiliary text images
	Q25	Placards commentary content illustrated
	Q26	Site lighting, the light is downy brightness
	Q27	Proper interpretation of the words difficult to understand
Recreation facilities	Q31	Clean toilet
	Q32	The number of the toilets
	Q33	Parking convenience/the number of parking lots
	Q34	Safety facilities adequate
	Q35	Number of trash can settings
	Q36	Rest area/tourist center comfortable
	Q37	Environment clean
Spiritual experience	Q41	Satisfy curiosity
	Q42	Experience novelty
	Q42	Augmented experiences improve relations with family and friends

3.2. Index Attribute Values and Weights

The attribute value of the evaluation index is obtained through the questionnaire survey, and five levels of “very good” to “very bad” are used to determine the service quality of tourists. When the survey results are processed, the value 5 means “very good,” 1 means “very bad,” and 4, 3, and 2 mean the degree value between the two, respectively. At the same time, the weights of evaluation indexes will be obtained through Fuzzy-AHP.

3.3. Economic Justification of the Proposed MCDM Methods

In order to provide a coherent and accurate analysis of the various effects of the tourism industry on sustainable development, it is necessary to gather extensive information from various tourism areas. This requires a lot of time and money. However, using the MCDM method helps us to provide a coherent and complete analysis of the tourism industry with the help of experts and with the least time and cost.

3.4. Analytic Hierarchy Process (AHP)

The Hierarchical Analysis (AHP) method was developed by Saati in 1980. This technique is a powerful and flexible method in the category of multicriteria decision-making methods by which complex problems can be solved at different levels. For this reason, it is called a hierarchical model because it is a tree model and hierarchy. The AHP method combines both objective and subjective evaluations into an integrated structure based on scales with even comparisons, and it helps analysts organize the essential aspects of a problem into a hierarchical framework. The advantages of this method can be mentioned: measuring the consistency of decision makers’ judgments, creating pairwise comparisons in choosing the optimal solution and option, the ability to consider criteria and subcriteria in evaluating options, and creating the ability to achieve the best option through paired comparisons.

Analytic Hierarchy Process (AHP) is a way to assist in decision-making and emphasizes the importance of a decision maker’s intuitive judgments and the consistency of comparing alternative options in the decision-making process. Because a decision maker makes their judgments based on knowledge and experience and therefore makes decisions based on that, the AHP approach is consistent with the behavior of a decision maker. The strength of this approach is that it regularly organizes tangible and intangible factors and offers a structural but relatively simple solution to decision problems.

3.5. Fuzzy-AHP

The traditional analytic hierarchy process (AHP) uses the 1–9 scale method. By comparing the evaluation objects in pairs, a fuzzy judgment matrix is obtained, and the qualitative and quantitative problems are comprehensively analyzed and processed to obtain a clear quantitative conclusion, which is presented in the form of an evaluation of advantages and disadvantages. But as a result of this method to an integer between 1 and 9 as a scale structure judgment matrix, this judgment does not well reflect the fuzziness of human judgment. Therefore, the Dutch scholar Van Laarhoven and Pedrycz [27] proposed fuzzy-AHP using triangular fuzzy number and fuzzy judgment method and using the arithmetic of triangular fuzzy number and logarithm least squares method, element for evaluation, thereby expanding the AHP to the fuzzy linguistic investigation. The main steps of fuzzy-AHP are shown in Table 2.

(1) Establish a hierarchical structure of the system according to the overall goal of the problem.

(2) The evaluation index and object are compared in pairs by experts, and the fuzzy judgment matrix is constructed by using triangular fuzzy numbers. The triangular fuzzy number M can be defined by its membership function ${\mu }_M:R$ ⟶ [0, 1] as follows:$$\begin{array}{}\text{(1)}& {\mu }_M=\begin{array}{cc}\frac{1}{m-l}x-\frac{1}{m-l},& x\in l,m,\\ \frac{1}{m-u}x-\frac{1}{m-u},& x\in l,m,\\ 0,& \text{otherwise}.\end{array}\end{array}$$

  In the formula, l ≤ m ≤ u, l and u, respectively, represent the lower bound and upper bound supported by M, and m is the median of M. Generally, the triangular fuzzy number M can be denoted as (l, m, u). The fuzzy judgment matrix is A generalization of the AHP judgment matrix, which is composed of triangular fuzzy numbers, denoted as A = ${a_{ij}}_{n\times m}$ = $l_{ij},m_{ij},u_{ij}$, and $a_{ji}=a_{ij}^{-1}$ = $1/l_{ij},1/m_{ij},1/u_{ij}$ When five experts make judgments on the k th criterion, it is the comprehensive triangular fuzzy number, which is the synthesis of the judgments of the five experts. It can be obtained from the following equation:$$\begin{array}{}\text{(2)}& a_{ij}=\frac{1}{T}\otimes a_{ij}^1+a_{ij}^2+\cdots ,+a_{ij}^T,\end{array}$$

  where $a_{ij}^T=$ $l_{ij}^t,m_{ij}^t,u_{ij}^t$$i,j=1,\cdots ,k,t=1,\cdots ,5$ is the triangular fuzzy number given by the t th expert.

  Value of triangular fuzzy number in fuzzy judgment matrix follows the 1–9 scale of AHP method; the triangular fuzzy number of lower bound l and upper bound u can be confirmed according to the fuzzy degree; the greater the u − l, the fuzzier the judgment; u − l is smaller and the judge is crisper; when u − l = 0, the judge is nonfuzzy, then l = m = u with the same value judgment under the general scale. For a given criterion, if the i th element is significantly more important than the j th element, the triangle fuzzy number = (4, 5, 6) or (3, 5,7) can be expressed, where (3, 5, 7) is more ambiguous than (4, 5, 6).

  The relative importance of evaluation criteria is subjective, fuzzy, and not suitable to be expressed in numerical form. Through linguistic variables, the narration of language can be changed into logical narration, and instead of numerical values, words or sentences in natural language can be used as values to convey the degree of perception of things. After selecting the appropriate linguistic meaning, the actual value can be calculated through various predetermined linguistic scale fuzzy numbers. The scale conversion of semantic variables is shown in Table 2 [5]:

(3) Calculation of relative weight value: this study normalized the geometric mean of vectors as proposed by Buckley [5] and calculated the weight of the fuzzy pairwise comparison matrix. The calculation formula is as follows:$$\begin{array}{}\text{(3)}& {\tilde{Z}}_i=\sqrt[n]{{\tilde{a}}_{i1}\otimes {\tilde{a}}_{i2}\otimes \cdots \otimes {\tilde{a}}_{ik}},\forall i=1,\dots ,k,\text{(4)}& {\tilde{\omega }}_i={\tilde{Z}}_i\otimes {\tilde{Z}}_1\oplus {\tilde{Z}}_2\oplus \cdots \oplus {\tilde{Z}}_k,\end{array}$$

  where ${\tilde{a}}_{ij}$: fuzzy paired comparison matrix $\tilde{A}$ of the ith row the jth column of triangular fuzzy number, ${\tilde{Z}}_i$: geometric average of the column vector of a triangular fuzzy number. ${\tilde{\omega }}_i$ a fuzzy eigenvector, the fuzzy weight of ith item factors of $\tilde{A}$

(4) Defuzzification: general methods of defuzzification are Gravity Method, α-cut set method, the maximum average method, average solution center fuzzification method, etc. The focus of this study used the simplest and most practical method (Center of Gravity Method) to calculate the fuzzy numbers membership function of the geometric center (the center of gravity) and the center of gravity of the crisp values of fuzzy numbers. Assuming that the triangular fuzzy number $\tilde{A}=L_i,M_i,U_i$ , the formula for defuzzification is as follows:$$\begin{array}{}\text{(5)}& D{F}_i=\frac{M_i-L_i+U_i-L_i}{3}+L_i\approx \frac{L_i+M_i+U_i}{3};\forall i=1,\dots ,k,\end{array}$$

  where DF_i is the crisp value after defuzzification.

(5) Normalization: in order to compare the importance of each evaluation criterion, the weight value of the fuzzy solution should be normalized to obtain the relative weight value. The formula of normalized weight value is as follows:$$\begin{array}{}\text{(6)}& {\omega }_i=\frac{D{F}_i}{{\displaystyle {\sum }_{i=1}^{n}D{F}_i}},\end{array}$$

(6) Maximum eigenvalue ${\lambda }_{\max }$: First, the paired comparison matrix $\tilde{A}$ is multiplied by the eigenvector ${\omega }_i$ to obtain a new vector ${\omega }_i^{\prime }$, and then the average multiple between the two is calculated to obtain the following:$$\begin{array}{}\text{(7)}& \begin{array}{cccc}1& a_{12}& \cdots & a_{1k}\\ a_{21}& 1& \cdots & a_{2k}\\ ⋮& ⋮& \ddots & ⋮\\ a_{k1}& a_{k2}& \cdots & 1\end{array}\ast \begin{array}{c}{\omega }_1\\ {\omega }_2\\ ⋮\\ {\omega }_k\end{array}=\begin{array}{c}{\omega }_1^{\prime }\\ {\omega }_2^{\prime }\\ ⋮\\ {\omega }_k^{\prime }\end{array}.\text{(8)}& {\lambda }_{\max }=1/n\ast {\omega }_1^{\prime }/{\omega }_1+{\omega }_2^{\prime }/{\omega }_2+\cdots +{\omega }_n^{\prime }/{\omega }_n.\end{array}$$

(7) Consistency verification: consistency verification is needed to confirm whether decision makers are consistent when comparing evaluation criteria. Firstly, the consistency index (C.I.) was calculated to verify the consistency or inconsistency of the decision makers’ comparison so that it could be corrected in time to avoid bad decisions. When there are more comparison elements, the order of the paired matrix will also increase, and it is more difficult to maintain the consistency judgment. Therefore, Saaty [22] proposed the random index (R.I.) to adjust the different C.I. generated under different orders to get the consistency ratio (C.R.), C.I., and C.R. The calculation is shown in formulas (9) and (10), and C.R.  $\le$ 0.1 represents the consistency level.$$\begin{array}{}\text{(9)}& C.I.={\lambda }_{\max }-k/\pi k-1.\text{(10)}& C.R.=C.I./R.I.\end{array}$$

Table 2

The scale conversion of semantic variables.

Fuzzy scale	Linguistic variable	Triangle fuzzy number
1̃ or (1̃ : 1̃)	Equally important	(1, 1, 1)
2̃ or (2̃ : 1̃)	Somewhere between equally important and slightly important	(1, 2, 3)
3̃ or (3̃ : 1̃)	Slightly important	(2, 3, 4)
4̃ or (4̃ : 1̃)	Somewhere between slightly important and very important	(3, 4, 5)
5̃ or (5̃ : 1̃)	Quite important	(4, 5, 6)
6̃ or (6̃ : 1̃)	Somewhere between quite important and very important	(5, 6, 7)
7̃ or (7̃ : 1̃)	Very important	(6, 7, 8)
8̃ or (8̃ : 1̃)	Somewhere between very important and absolutely important	(7, 8, 9)
9̃ or (9̃ : 1̃)	Absolutely important	(8, 9, 10)

3.6. VIKOR Method

Multicriterion compromise decision-making (VIKOR for short) is a compromise decision-making method that can take into account the maximization of group benefits and the minimization of individual regrets against opinions, as well as the subjective preferences of decision makers. Therefore, it is more reasonable to use the VIKOR method to study multiattribute decision-making problems.

The evaluation subject of tourist service quality is A_i, i = 1, 2, 3, 4. The evaluation set of government supervisors, environmental planning venues administrators, venue staff, and tourists is A = {A₁, A₂, A₃, A₄}; The evaluation index is C_j, j = 1, 2, …, 26, the evaluation index set C = {C₁, C₂, …, C₂₆}, x_ij is the service quality evaluation attribute value of the participant i to the index C_j, and ${\omega }_{ij}$ is the service quality evaluation index weight of the participant i to the index C_j. The steps of ranking tourist service quality by VIKOR are as follows:

(1) Determine the maximum and minimum values of the evaluation index $x_j^{+}$ and $x_j^{-}$, namely $x_j^{+}=\underset{j}{\max }{x}_{ij}$, $x_j^{-}=\underset{j}{\min }{x}_{ij}$. And then we get the following:$$\begin{array}{}\text{(11)}& r_{ij}=\frac{x_j^{+}-x_{ij}}{x_j^{+}-x_j^{-}}.\end{array}$$

(2) Calculate the weighted evaluation value of tourist service quality S_i and Q_i of each participant. Since each evaluation index has the same dimension and its attribute value and weight do not need to be standardized, then calculate the evaluation value of tourist service quality of each participant and get the weighted evaluation matrix as follows:$$\begin{array}{}\text{(12)}& S_i={\displaystyle \sum _{j=1}^{25}{\omega }_{ij}\cdot x_{ij}}\end{array}$$

  where ${\displaystyle {\sum }_{j=1}^{26}{\omega }_{ij}}=1$. At the same time, let $Q_i=\underset{j}{\max }{r}_{ij}|j=1,\dots ,25$, then calculate $S^{+}$, $S^{-}$, $Q^{+}$ and $Q^{-}$ where $S^{+}=\underset{i}{\max }{S}_i$, $S^{-}=\underset{i}{\min }{S}_i$, $Q^{+}=\underset{i}{\max }{Q}_i$, $Q^{-}=\underset{i}{\min }{Q}_i$

(3) Calculate $R_i$,$$\begin{array}{}\text{(13)}& R_i=\nu \frac{S_i-S^{-}}{S^{+}-S^{-}}-1-\nu \frac{Q_i-Q^{-}}{Q^{+}-Q^{-}}.\end{array}$$

  Where $\nu$ is the decision-making mechanism. Under the balanced decision-making mechanism of general compromise group benefit maximization and individual regret minimization, $\nu$ = 0.5.

(4) Rank the tourist service quality evaluation of participant i according to the value of $R_i$, rank $R_i$ from small to large, and the top scheme in each sequence is higher than the latter.

4. Results and Discussion

4.1. Research Background and Research Design

The plan of Hakka Romantic Avenue in Taiwan will be launched in 2018, connecting 16 key development areas of Hakka culture in Taiwan to promote the trend of leisure Eco-tourism. The first part of this survey includes respondents’ occupation, gender, age and education background, etc. The second part includes 26 service quality evaluation indexes of environmental planning venues, which are obtained after modification by the classic SERVPERF model.

The survey was conducted in five counties and cities in northern Taiwan in February 2019. The respondents included the Ministry of Culture and Culture, Hakka Affairs Council, Council of Agriculture, Sports Administration of the Ministry of Education, Ministry of Education and other government supervisors, administrators of environmental planning venues, venue staff, and tourists. A total of 1,150 questionnaires were issued by means of snowball sampling. This research of government supervisors surveys , 350 questionnaires of 800 to participate in other subjects. 12 investigators in 7 days, respectively, to 15 Hakka environmental planning venues to issue 200 questionnaires to administrators, to rest, eating venue staff to issue 300 questionnaires, and 300 questionnaires distributed to the tourists. Questionnaires were filled in and collected on-site, and the distribution and recovery of questionnaires were shown in Table 3.

Table 3

Questionnaire on service quality of environmental planning venues.

Survey objects	Government supervisors	Venue administrator	Venue staff	Tourist
Issuing questionnaires (copies)	350	200	300	300
Collecting valid documents (copies)	332	112	235	240
Effective recovery rate (%)	94.85	56.00	78.33	80.00

In this study, factor analysis was used to test questionnaire validity, and Cronbach $\alpha$ was used to test the reliability of the questionnaire. The overall KMO value of the questionnaire was 0.935, which exceeded 0.7, and Bartlett’s spherical test was remarkable, the significance of which is 0.000. After 21 orthogonal iterations by principal component analysis, common factors with an eigenvalue greater than 1 and factor loading greater than 0.4 were extracted and selected. The cumulative variance interpretation rate was 64.423%, and the overall Cronbach $\alpha$ was 0.942. It indicates that the overall validity and reliability of the questionnaire are both high.

4.2. Survey Results

Through the data collection and data processing of the collected questionnaires, the results are shown in Table 4: evaluation results of service quality for government supervisors (R), environmental planning venues administrators (M), venue staff (S), and tourists (T). According to the attribute values of service quality evaluation indicators in Table 4, the comparison of the mean gap of service quality evaluation between government supervisors, environmental planning venues administrators, venue staff, and tourists was obtained by an independent normal test. On the whole, by comparing the tourist evaluation results of the service quality of the environmental planning venues with those of government supervisors, administrators of the environmental planning venues, and tourists, it can be found that the gap between the venue staff and tourists is the largest, and it is difficult to determine the gap between the government supervisors and administrator of the environmental planning of venues and tourists. Therefore, to further analyze the gap of service quality evaluation among different participants, the above evaluation results should be ranked by the VIKOR method.

Table 4

Service quality survey results.

Criterions		Attribute values of environmental planning venues				Gap
		R	M	S	T	R-T	M-T	S-T
Q1	Q11	4.307	3.876	4.382	3.825	0.482 (${}^{\ast \ast \ast }$)	0.051	0.557 (${}^{\ast \ast \ast }$)
	Q12	4.185	3.895	4.325	3.794	0.391 (${}^{\ast \ast \ast }$)	0.101	0.531 (${}^{\ast \ast \ast }$)
	Q13	3.568	3.532	4.125	3.428	0.14	0.104	0.697 (${}^{\ast \ast \ast }$)
	Q14	3.387	3.342	4.011	3.381	0.006	−0.039	0.63 (${}^{\ast \ast \ast }$)
	Q15	3.652	3.713	3.976	3.573	0.079	0.14	0.403 (${}^{\ast \ast \ast }$)
	Q16	4.014	3.901	4.125	3.612	0.402 (${}^{\ast \ast \ast }$)	0.289 (${}^{\ast \ast \ast }$)	0.513 (${}^{\ast \ast \ast }$)
	Q17	3.896	4.121	3.987	3.428	0.468 (${}^{\ast \ast \ast }$)	0.693 (${}^{\ast \ast \ast }$)	0.559 (${}^{\ast \ast \ast }$)
	Q18	3.874	4.085	4.385	3.45	0.424 (${}^{\ast \ast \ast }$)	0.635 (${}^{\ast \ast \ast }$)	0.935 (${}^{\ast \ast \ast }$)
	Q19	3.69	3.923	4.136	3.574	0.116	0.349 (${}^{\ast \ast \ast }$)	0.562 (${}^{\ast \ast \ast }$)
Q2	Q21	3.845	3.802	4.203	3.768	0.077	0.034	0.435 (${}^{\ast \ast \ast }$)
	Q22	3.598	3.702	3.895	3.255	0.343 (${}^{\ast \ast \ast }$)	0.447 (${}^{\ast \ast \ast }$)	0.64 (${}^{\ast \ast \ast }$)
	Q23	3.458	3.325	4.015	3.342	0.116	−0.017	0.673 (${}^{\ast \ast \ast }$)
	Q24	3.765	3.854	4.155	3.488	0.277 (${}^{\ast \ast \ast }$)	0.366 (${}^{\ast \ast \ast }$)	0.667 (${}^{\ast \ast \ast }$)
	Q25	3.856	3.751	3.965	3.456	0.4 (${}^{\ast \ast \ast }$)	0.295 (${}^{\ast \ast \ast }$)	0.509 (${}^{\ast \ast \ast }$)
	Q26	3.788	3.875	4.214	3.332	0.456 (${}^{\ast \ast \ast }$)	0.543 (${}^{\ast \ast \ast }$)	0.882 (${}^{\ast \ast \ast }$)
	Q27	3.876	3.758	4.133	3.125	0.751 (${}^{\ast \ast \ast }$)	0.633 (${}^{\ast \ast \ast }$)	1.008 (${}^{\ast \ast \ast }$)
Q3	Q31	3.952	3.858	3.856	3.133	0.819 (${}^{\ast \ast \ast }$)	0.725 (${}^{\ast \ast \ast }$)	0.723 (${}^{\ast \ast \ast }$)
	Q32	3.874	3.955	3.863	3.245	0.629 (${}^{\ast \ast \ast }$)	0.71 (${}^{\ast \ast \ast }$)	0.618 (${}^{\ast \ast \ast }$)
	Q33	3.855	3.763	4.033	3.565	0.29 (${}^{\ast \ast \ast }$)	0.198	0.468 (${}^{\ast \ast \ast }$)
	Q34	3.896	3.957	4.135	3.655	0.241 (${}^{\ast \ast \ast }$)	0.302 (${}^{\ast \ast \ast }$)	0.48 (${}^{\ast \ast \ast }$)
	Q35	3.955	3.845	3.931	3.339	0.616 (${}^{\ast \ast \ast }$)	0.506 (${}^{\ast \ast \ast }$)	0.592 (${}^{\ast \ast \ast }$)
	Q36	3.896	3.967	4.223	3.64	0.256 (${}^{\ast \ast \ast }$)	0.327 (${}^{\ast \ast \ast }$)	0.583 (${}^{\ast \ast \ast }$)
	Q37	3.485	3.587	3.782	3.32	0.165	0.267 (${}^{\ast \ast \ast }$)	0.462 (${}^{\ast \ast \ast }$)
Q4	Q41	3.659	3.788	3.855	3.599	0.06	0.189	0.256 (${}^{\ast \ast \ast }$)
	Q42	3.95	4.113	3.982	3.623	0.327 (${}^{\ast \ast \ast }$)	0.49 (${}^{\ast \ast \ast }$)	0.359 (${}^{\ast \ast \ast }$)
	Q43	4.307	3.876	4.382	3.825	0.482 (${}^{\ast \ast \ast }$)	0.051	0.557 (${}^{\ast \ast \ast }$)

Notes: p values of ${}^{\ast \ast \ast }$Represent 0.001 significance level.

4.3. FAHP Weighting Results

Firstly, the evaluation experts of ecology, tourism management, and environmental planning who are both academic and practical (a total of 5 experts) can construct the fuzzy paired comparison matrix of the criterion layer and the target layer by comparing the importance of each criterion in pairs, as shown in Tables 5–10. Then, the relative weights of the corresponding evaluation criteria (Table 9) are calculated according to formulas (3)–(6), and the judgments are consistent before and after.

Table 5

Fuzzy judgment matrix of Q₁–Q₄ to Q.

Q	Q1	Q2	Q3	Q4
Q1	(1, 1, 1)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 2, 3)
Q2	(1, 1/2, 1/3)	(1, 1, 1)	(1/2, 1/3, 1/4)	(1, 2, 3)
Q3	(1, 2, 3)	(2, 3, 4)	(1, 1, 1)	(2, 3, 4)
Q4	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1/2, 1/3, 1/4)	(1, 1, 1)

Table 6

Fuzzy judgment matrix of Q₁₁–Q₁₉ to Q₁.

Q1	Q11	Q12	Q13	Q14	Q15	Q16	Q17	Q18	Q19
Q11	(1, 1, 1)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1, 1)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)
Q12	(1, 1/2, 1/3)	(1, 1, 1)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1, 1)
Q13	(1, 2, 3)	(2, 3, 4)	(1, 1, 1)	(1, 2, 3)	(3, 4, 5)	(1, 2, 3)	(1, 2, 3)	(2, 3, 4)	(2, 3, 4)
Q14	(1, 2, 3)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1, 1)	(2, 3, 4)	(1, 2, 3)	(1, 2, 3)	(1, 2, 3)	(1, 2, 3)
Q15	(1, 1, 1)	(1, 2, 3)	(1/3, 1/4, 1/5)	(1/2, 1/3, 1/4)	(1, 1, 1)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1, 1)
Q16	(1, 1, 1)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 2, 3)	(1, 1, 1)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1, 1, 1)
Q17	(1, 2, 3)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 2, 3)	(2, 3, 4)	(1, 1, 1)	(1, 2, 3)	(1, 2, 3)
Q18	(1, 2, 3)	(1, 2, 3)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1, 2, 3)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1/2, 1/3)
Q19	(1, 2, 3)	(1, 1, 1)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1, 1)	(1, 1/2, 1/3)	(1, 2, 3)	(1, 1, 1)

Table 7

Fuzzy judgment matrix of Q₂₁–Q₂₇ to Q₂.

Q2	Q21	Q22	Q23	Q24	Q25	Q26	Q27
Q21	(1, 1, 1)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)
Q22	(1, 2, 3)	(1, 1, 1)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)
Q23	(1, 2, 3)	(2, 3, 4)	(1, 1, 1)	(3, 4, 5)	(2, 3, 4)	(1, 2, 3)	(1, 2, 3)
Q24	(1, 2, 3)	(1, 2, 3)	(1/3, 1/4, 1/5)	(1, 1, 1)	(2, 3, 4)	(1, 2, 3)	(1, 1, 1)
Q25	(1, 2, 3)	(1, 2, 3)	(1/2, 1/3, 1/4)	(1/2, 1/3, 1/4)	(1, 1, 1)	(1, 2, 3)	(1, 2, 3)
Q26	(2, 3, 4)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1, 1)	(2, 3, 4)
Q27	(1, 2, 3)	(1, 2, 3)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1/2, 1/3)	(1/2, 1/3, 1/4)	(1, 1, 1)

Table 8

Fuzzy judgment matrix of Q₃₁–Q₃₇ to Q₃.

Q3	Q31	Q32	Q33	Q34	Q35	Q36	Q37
Q31	(1, 1, 1)	(1, 1, 1)	(1, 1, 1)	(1, 2, 3)	(1, 1, 1)	(2, 3, 4)	(1, 1, 1)
Q32	(1, 1, 1)	(1, 1, 1)	(1, 1, 1)	(2, 3, 4)	(1, 1, 1)	(1, 2, 3)	(1, 1, 1)
Q33	(1, 1, 1)	(1, 1, 1)	(1, 1, 1)		(1, 1, 1)	(2, 3, 4)	(1, 1, 1)
Q34	(1/2, 1/3, 1/4)	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1/2, 1/3)
Q35	(1, 1, 1)	(1, 1, 1)	(1, 1, 1)	(1, 2, 3)	(1, 1, 1)	(1, 2, 3)	(1, 1, 1)
Q36	(1/2, 1/3, 1/4)	(1, 1/2, 1/3)	(1/2, 1/3, 1/4)	(1, 1, 1)	(1, 1/2, 1/3)	(1, 1, 1)	(1, 1/2, 1/3)
Q37	(1, 1, 1)	(1, 1, 1)	(1, 1, 1)	(1, 2, 3)	(1, 1, 1)	(1, 2, 3)	(1, 1, 1)

Table 9

Fuzzy judgment matrix of Q₄₁–Q₄₃ to Q₄.

Q4	Q41	Q42	Q43
Q41	(1, 1, 1)	(1, 1, 1)	(1, 1/2, 1/3)
Q42	(1, 1, 1)	(1, 1, 1)	(1, 1/2, 1/3)
Q43	(1, 1/2, 1/3)	(1, 1/2, 1/3)	(1, 1, 1)

Table 10

Weights of indicator layer relative to target layer.

Q	Q1	0.069	Q11	0.021114
		0.034	Q12	0.010404
		0.209	Q13	0.063954
		0.174	Q14	0.053244
		0.055	Q15	0.01683
		0.087	Q16	0.026622
		0.162	Q17	0.049572
		0.122	Q18	0.037332
		0.089	Q19	0.027234
	Q2	0.037	Q21	0.007141
		0.079	Q22	0.015247
		0.248	Q23	0.047864
		0.182	Q24	0.035126
		0.162	Q25	0.031266
		0.175	Q26	0.033775
		0.116	Q27	0.022388
	Q3	0.193	Q31	0.086078
		0.193	Q32	0.086078
		0.193	Q33	0.086078
		0.029	Q34	0.012934
		0.170	Q35	0.07582
		0.029	Q36	0.012934
		0.193	Q37	0.086078
	Q4	0.219	Q41	0.012264
		0.219	Q42	0.012264
		0.561	Q43	0.031416

5. VIKOR Evaluation Ranking

The questionnaire data in Table 4 are used to rank the service quality evaluation results of government supervisors, environmental planning venues administrators, venue staff, and tourists according to the calculation steps of formulas (11)–(13), and the $S_j$, $R_j$, and $Q_j$ values of each questionnaire item are obtained, as shown in Table 11. According to VIKOR calculation, the final result is S > R; M > T. That is, the quality ranking order of the four projects is as follows: venues’ staff > government supervisors; administrators of environmental planning venues > tourists, indicating that the venues’ staff is the highest, followed by the evaluation value by the government supervisor and the venues administrator, and the evaluation value by the tourists is the lowest.

Table 11

Evaluation indexes of S, R, and Q of government supervisors (R), administrators of environmental planning venues (M), staff (S), and tourists (T).

Subject	S		R		Q		The overall rank
	Value	Rank	Value	Rank	Value	Rank
R	0.4139	2	0.0553	3	0.4909	2	2
M	0.4412	3	0.0544	2	0.4992	3	2
S	0.0371	1	0.0112	1	0.0000	1	1
T	0.9968	4	0.0861	4	1.0000	4	3

6. Discussion

Sustainable development has been defined as the process by which the current needs of society are met without affecting the ability of future generations to meet their own needs. By definition, sustainable development has four dimensions: (1) environmental development, (2) economic development, (3) social development, and (4) legal development. Each of these dimensions has indicators. Environmental indicators of sustainable development include the atmosphere, land, oceans and seas and coastlines, freshwater, and biodiversity. Today, attention to the urban environment and urban ecological development has been reborn. People have realized that the connection between the city and the natural environment is inseparable. For most people in the future, living in a quality environment means living in a city where the environment is carefully managed. One way to improve the environmental management of a city is to analyze the city as an ecological system. For the sustainability of the city ecosystem, the biological capacity and carrier of these cities should be measured, and efforts should be made to maintain, develop, and distribute its proper location. Over time, positive and negative changes in carrier capacity that can be used to measure the practicality of management activities should be given serious attention by officials and those involved in urban management in the field of preservation, development, and maintenance of natural spaces. To improve the living conditions of cities, measures such as determining the desired population for each city according to the possibility of providing water and other essential needs and adequate, and appropriate establishment of production and industrial activities with respect to environmental issues, especially in the field of pollution, seem necessary.

7. Conclusions

An environmental planning venue is an area with environmental characteristics that provides appropriate fields, exhibits, education, facilities, and activities, supplemented by professional instruction, guidance, and education, to enable people of all ages to develop responsible environmental behavior. How to ensure that tourists can achieve the purpose of environmental planning and Eco-tourism is the focus of this study. Based on the point of view of Parasuraman et al. [17, 18], tourist service quality is the core of environmental planning venue service quality management. The study measured environmental planning venue service quality evaluation. The evaluation result shows that government supervisors, environmental planning venue managers, and its staff overestimate the environment education venues’ service quality. As service providers, administrators, and staff of environmental planning venues overestimate the service quality, which confirms the “Pollyanna Principle” proposed by Peiró et al. [20] also exists in the service field of environmental planning venues, the survey found that there were significant differences in interpretation services, interpretation facilities, recreation facilities, and spiritual experience between the venue’s staff and tourists. Since the optimistic estimation of service providers tends to lead to service degradation, the most optimistic evaluation of the venue staff should be to reduce the complacency of the venue’s staff, which should be the main task of improving the service quality of the environmental planning venues. At the same time, we should also pay attention to the administrators’ optimistic estimate of the service quality of the environmental planning venues. In addition to interpretation services and spiritual feelings, administrators’ evaluation of service quality of environmental planning venues also lags behind that of tourists in the evaluation of interpretation facilities and recreation facilities. Eliminating the blind optimism of the administrators of environmental planning venues will help to formulate and implement the service standards of environmental planning venues that better meet the needs of tourists.

Different from the previous studies that only focused on service providers, this paper compares the consistency of government supervisors’ and tourists’ evaluations of the service quality of environmental planning venues. Theoretically, the public sector subsidies in environmental planning venues are less likely to cause moral hazards and adverse selection caused by information asymmetry, so the government will decide on the follow-up subsidy scheme based on the service quality of environmental planning venues. Therefore, the evaluation of government supervisors should be consistent with that of tourists. However, the evaluation results show that there are significant differences with tourists in interpretation services, interpretation facilities, recreation facilities, and spiritual experiences. The results appear, on the one hand, maybe environmental planning venues service quality policy measures by government supervisors, administrators of environmental planning venues; its staff multiple hierarchies can be passed to the tourists; degree of execution or complaint mechanism is not sound, causes the distortion of information feedback, and makes the government supervisors have errors of perception of tourist demand. On the contrary, the daily supervision of the government supervisors may pay more attention to the opinions of the administrators or staff of the environmental planning venues but ignore the communication with tourists and have insufficient understanding of the needs of tourists.

Based on the results of empirical research, the following suggestions are developed and provided for the administrators of environmental planning venues, government environmental planning supervisors, and subsequent researchers as references. (1) The venues can design some mixed environment teaching courses for the physically and mentally disabled or vulnerable groups so that they can understand and learn from each other under the common topic of caring for the environment. (2) Manpower cultivation is a very important subject in environmental education. Taiwan is becoming an aging society. If the competent authority can use these high and middle-aged volunteers and give them appropriate professional training, it will help develop more diversified environmental planning courses and achieve mutual benefit. (3) Environmental planning venues are not only places with the characteristics of natural resources, environmental planning value, and practice opportunities but also need four elements, namely program, facility, personnel, and operation management. By promoting the process of Eco-tourism, people can understand the natural ecology and local and global environmental issues, which will generate more positive environmental attitudes and responsible environmental behaviors. (4) Strengthening the perception of tourists’ demands by staff, government supervisors, and administrators of the environmental planning venues has become an important issue of service quality management. First of all, the consistency between venue staff and tourists should be improved to eliminate the blind optimism of venue staff. The staff of the venues is encouraged to pass the national tour guide qualification examination. Tour guides should communicate with tourists emotionally and hone their skills in practice. In order to improve the service quality of the venues. (5) It is suggested that future researchers should first identify specific types of places and extensively search for venue administrators, teaching plan providers, and activity participants with different backgrounds for an in-depth discussion of venues with different characteristics.

Acknowledgments

This research was supported by the following projects: fund project: Guangdong Universities Scientific Research Platform and Industry-Education Integration Project, name: Industry-Education Integration Innovation Platform for 3D Digitization of Clothing Industry and C2M Personalized Customization, project no. 2021CJPT018, fund project: 2021 Characteristic Innovation Projects of Ordinary Universities in Guangdong Province, name: Analysis of rope knot cultural products to promote the regional economic development of Conghua, project no. 2021WTSCX305, and fund project: Higher Vocational Education Teaching Reform Project, name: Relying on the innovation and strengthening the school project “clothing topic design” course ideological and political, project no. NY-2019CQ2KC-13.

References

[1] J. S. Akama, D. M. Kieti, "Measuring tourist satisfaction with Kenya’s wildlife safari: a case study of Tsavo West National Park," Tourism Management, vol. 24 no. 1, pp. 73-81, DOI: 10.1016/s0261-5177(02)00044-4, 2003.

[2] A. Awasthi, S. S. Chauhan, H. Omrani, A. Panahi, "A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality," Computers & Industrial Engineering, vol. 61 no. 3, pp. 637-646, DOI: 10.1016/j.cie.2011.04.019, 2011.

[3] M. Bacache-Beauvallet, L. Janin, "Taxicab licence value and market regulation," Transport Policy, vol. 19 no. 1, pp. 57-62, DOI: 10.1016/j.tranpol.2011.08.001, 2012.

[4] E. Boo, "Making eco-tourism sustainable: recommendations for planning, development, and management," Nature tourism, vol. 1991, 1991.

[5] J. J. Buckley, "Fuzzy hierarchical analysis," Fuzzy Sets and Systems, vol. 17 no. 3, pp. 233-247, DOI: 10.1016/0165-0114(85)90090-9, 1985.

[6] T. Çetin, K. Y. Eryigit, "Estimating the effects of entry regulation in the Istanbul taxicab market," Transportation Research Part A: Policy and Practice, vol. 45 no. 6, pp. 476-484, 2011.

[7] F.-Y. Chen, Y.-H. Chang, "Examining airline service quality from a process perspective," Journal of Air Transport Management, vol. 11 no. 2, pp. 79-87, DOI: 10.1016/j.jairtraman.2004.09.002, 2005.

[8] K.-K. Chen, C.-T. Chang, C.-S. Lai, "Service quality gaps of business customers in the shipping industry," Transportation Research Part E: Logistics and Transportation Review, vol. 45 no. 1, pp. 222-237, DOI: 10.1016/j.tre.2008.02.005, 2009.

[9] J. J. Cronin, S. A. Taylor, "Measuring service quality: a reexamination and extension," Journal of Marketing, vol. 56 no. 3, pp. 55-68, DOI: 10.1177/002224299205600304, 1992.

[10] A. Fleischer, A. Pizam, "Rural tourism in Israel," Tourism Management, vol. 18 no. 6, pp. 367-372, DOI: 10.1016/s0261-5177(97)00034-4, 1997.

[11] N. D. Hetzer, "Environment, tourism, culture," Links, vol. 1 no. 3, 1965.

[12] G. T. Hvenegaard, P. Dearden, "Eco-tourism versus tourism in a Thai national park," Annals of Tourism Research, vol. 25 no. 3, pp. 700-720, DOI: 10.1016/s0160-7383(98)00020-6, 1998.

[13] M. A. Khan, J. Mustafa, J. Musarrat, "Mechanism of DNA strand breakage induced by photosensitized tetracycline Cu complex," Mutation Research, vol. 525 no. 1-2, pp. 109-119, DOI: 10.1016/s0027-5107(03)00008-3, 2003.

[14] J. J. H. Liou, C.-Y. Tsai, R.-H. Lin, G.-H. Tzeng, "A modified VIKOR multiple-criteria decision method for improving domestic airlines service quality," Journal of Air Transport Management, vol. 17 no. 2, pp. 57-61, DOI: 10.1016/j.jairtraman.2010.03.004, 2011.

[15] D. W. Martin, "An importance/performance analysis of service providers’ perception of quality service in the hotel industry," Journal of Hospitality & Leisure Marketing, vol. 3 no. 1,DOI: 10.1300/j150v03n01_02, 1995.

[16] M. L. Miller, "The rise of coastal and marine tourism," Ocean & Coastal Management, vol. 20 no. 3, pp. 181-199, DOI: 10.1016/0964-5691(93)90066-8, 1993.

[17] A. Parasuraman, V. A. Zeithaml, L. L. Berry, "A conceptual model of service quality and its implications for future research," Journal of Marketing, vol. 49 no. 4, pp. 41-50, DOI: 10.1177/002224298504900403, 1985.

[18] A. Parasuraman, V. A. Zeithaml, L. L. Berry, "Servqual: a multiple-item scale for measuring consumer perc," Journal of Retailing, vol. 64 no. 1, 1988.

[19] F. F. Reichheld, W. E. Sasser, "Zero defections: quality comes to services.fions: quoliiy comes to services," Harvard Business Review, vol. 68 no. 5, pp. 105-111, 1990.

[20] J. M. Peiró, V. Martínez-Tur, J. Ramos, "Employees’ overestimation of functional and relational service quality: a gap analysis," Service Industries Journal, vol. 25 no. 6, pp. 773-788, DOI: 10.1080/02642060500103324, 2005.

[21] S. Ross, G. Wall, "Eco-tourism: towards congruence between theory and practice," Tourism Management, vol. 20 no. 1, pp. 123-132, DOI: 10.1016/s0261-5177(98)00098-3, 1999.

[22] T. L. Saaty, The Analytic Hierarchy Process, 1980.

[23] B. Schneider, A. N. Salvaggio, M. Subirats, "Climate strength: a new direction for climate research," Journal of Applied Psychology, vol. 87 no. 2, pp. 220-229, DOI: 10.1037/0021-9010.87.2.220, 2002.

[24] H.-S. Shih, "Incremental analysis for MCDM with an application to group TOPSIS," European Journal of Operational Research, vol. 186 no. 2, pp. 720-734, DOI: 10.1016/j.ejor.2007.02.012, 2008.

[25] E. Sirakaya, "Attitudinal compliance with eco-tourism guidelines," Annals of Tourism Research, vol. 24 no. 4, pp. 919-950, DOI: 10.1016/s0160-7383(97)00050-9, 1997.

[26] P. S. Valentine, "Eco-tourism and nature conservation," Tourism Management, vol. 14 no. 2, pp. 107-115, DOI: 10.1016/0261-5177(93)90043-k, 1993.

[27] P. J. Van Laarhoven, W. Pedrycz, "A fuzzy extension of Saaty’s priority theory," Fuzzy Sets and Systems, vol. 11 no. 1-3, pp. 229-241, DOI: 10.1016/s0165-0114(83)80082-7, 1983.

[28] P. A. Wight, "North American ecotourists: market profile and trip characteristics," Journal of Travel Research, vol. 34 no. 4,DOI: 10.1177/004728759603400401, 1996.

[29] K. Ziffer, Eco-tourism: The Uneasy Alliance, Conservation International, 1989.

[30] C. Haiyun, H. Zhixiong, S. Yüksel, H. Dinçer, "Analysis of the innovation strategies for green supply chain management in the energy industry using the QFD-based hybrid interval valued intuitionistic fuzzy decision approach," Renewable and Sustainable Energy Reviews, vol. 143,DOI: 10.1016/j.rser.2021.110844, 2021.

[31] N. B. Mabrouk, "Green supplier selection using fuzzy Delphi method for developing sustainable supply chain," Decision Science Letters, vol. 10 no. 1, pp. 63-70, DOI: 10.5267/j.dsl.2020.10.003, 2021.

[32] R. B. Bire, Y. E. Nugraha, F. R. A. Welly, "A fuzzy-analytic hierarchy process of tourism supply chain performance: customer perspectives," Enlightening Tourism. a Pathmaking Journal, vol. 11 no. 2, pp. 531-557, DOI: 10.33776/et.v11i2.5257, 2021.

[33] V. Nalluri, L.-S. Chen, "Risk assessment for sustainability on telecom supply chain: a hybrid fuzzy approach," Uncertain Supply Chain Management, vol. 10 no. 2, pp. 559-576, DOI: 10.5267/j.uscm.2021.11.007, 2022.