1. Introduction
In recent years, green tourism has received greater attention from tourists because they prefer learning, creating, and participating in environmental activities to engaging in traditional tourism [
1]. Increasingly, the development of green tourism is diversified to attract investment and creativity from tourism developers. Agritourism is a typical alternative to traditional tourism, involving environmentally friendly activities and demonstrating great potential for sustainable development in the future [
2,
3]. With many unique elements, agritourism creates an open tourist space, encouraging harmony with nature, and a return to an idyllic life, where visitors can plant, care for, and harvest agricultural products by themselves [
4]. In the context of the COVID-19 epidemic, this model also meets the present needs of tourists such as limiting contact between groups of people and improving both mental and physical health through experiential activities. As the Fortune Business Insights report, the agritourism market was worth USD 69.24 billion in 2019. This value is forecast to reach USD 117.37 billion in 2027, with a 7.42% compound annual growth rate (CAGR) from 2020 to 2027 [
5]. It is clear that this type of tourism has become a globally sustainable and economically green growth trend based on its comprehensive development in both size and quality. Specifically, the planning of agricultural land in developed countries was initially conducted for recreational purposes, to efficiently use natural resources based on the conditions of geography, the economy, and society [
6]. One example is the US, where more than USD 800 million is spent organizing many events related to agritourism every year. In Austria, agricultural development associated with tourism and traditional cuisine is professionally organized, despite farmers making up only 3% of the population. In France, the policy of countryside development has encouraged the promotion of rural tourism. As a result, this program has now become a popular tourism phenomenon in rural areas, which can help to quickly increase a region’s value [
7,
8]. Developing countries where the agricultural economy is predominant are now enacting plans to diversify the cultivation of land and exploit the inherent potential of agricultural products and regional culture. In Japan, the combination of agricultural products and rural landscapes is defined in the local economic development strategy [
9]. The Chinese government has built more than 15 unique agritourism routes with 251 agro-ecological gardens, contributing to the effective improvement in agricultural production [
8]. Thailand is applying agritourism in local communities by expanding the tourism industry and raising the incomes of local people. The advantage of this activity is that it not only helps agricultural sustainable development but also decreases pressure on crowded tourist destinations in the area [
4].
In the case of Vietnam, tourism associated with new rural reconstruction has become an inevitable trend, in which agriculture is a key economic sector, accounting for 72.84% of the country’s economic structure (General Statistics Office of Vietnam, Vietnam, 2022). According to the Vietnam National Administration of Tourism, rural tourism plays an important role in overall tourism development by connecting urban areas and tourist centers, expanding the spatial scope, and extending the guests’ stay. This is an appropriate direction for the development of Vietnam’s tourism industry, especially in the wake of the COVID-19 pandemic. The countryside of Vietnam possesses rich natural and human resources, such as rice fields, fruit gardens, community identities, and traditional craft villages; these are important factors in the establishment of tourist destinations. Furthermore, these destinations create value for local products, contributing to farm income stabilization and promoting local natural values. Hence, many agricultural products, such as food, beverages, handicrafts, fruits, and confectionery, have been displayed in restaurant chains, hotels, and resorts. Many destinations have exploited different agritourism models based on specific regional features such as the Moc Chau dairy farm, the Sa Pa terraced fields, the Tra Que vegetable village, the Dong Trieu ceramic craft village, the Da Lat hydroponic vegetable and high-tech flower gardens, the Ninh Thuan vineyards and sheep farms, the Dong Nai fruit garden, and the Cai Rang floating market.
However, Vietnam’s agritourism cannot fulfill the requirements of sustainable development, leading to many challenges. Firstly, the conflict-of-interest issue in choosing local agricultural and tourism development models must be considered. In particular, the link between the agriculture and tourism sectors is an essential factor in ensuring consistency in the implementation process. This not only directly affects the direction and policies but also relates to the outcome and expectations of the local agritourism industry. Moreover, the benefit to farmers must be considered to help them improve their income and the quality of their products [
10]. Next, the planning and preservation of traditional agricultural villages associated with tourism is a complicated issue, which requires a long-term vision. According to each locality’s strengths, close attention should be paid to the diversification of agricultural land due to links with the development of the commodity economy and agricultural communities such as farm, households, agricultural centers, and cooperatives [
11]. Another challenge is building the assessment criteria for agritourism products. These criteria need to be established to evaluate and support agritourism models based on local characteristics. With the increasing requirements from travelers, high-quality services are notably focused. Moreover, this standardization also assists in managing travelling destinations and the continuous improvement in leisure activities [
12]. Training funds are another difficulty while running this model. Despite their direct participation in production, the farmer’s skills in tourism services, including welcoming visitors, communicating, and promoting tourist products, are very limited. In addition, when the operation scale is expanded, the demand for labor resources is very large. Thus, it is necessary train human resources in the chain to provide related information, such as information on the production process, product characteristics, customs and traditions, and culture of the region, to incoming travelers [
13]. Finally, tourism promotion and marketing are the main concerns in the development strategy. The variety of destination information is a highlight of this form of tourism, providing convenience and attractiveness to tourists. With the advancement of modern technology, it is extremely important to be innovative and creative in advertising strategies. Thus, an appropriate plan regarding the promotion agritourism is identified, looking at the combination of value locality and market demand [
14].
Despite facing challenges in the development process, Vietnam’s agritourism has created many opportunities for innovation, breakthroughs, and sustainability. It encourages the exploitation of tourism with agricultural values in rural areas by investors and tourism developers. The selection of a suitable location is greatly important when deciding whether to invest in a project because it directly affects the business strategy, operational capability, and the surrounding environment [
15]. Before deciding, tourist developers need to investigate and evaluate the destination’s characteristics linked to economic, social, and environmental resources [
16]. An optimal site would result in high tourism potential and competitive advantages [
17]. Furthermore, sustainability is an indispensable factor in the agritourism model, enhancing service quality, allowing for the efficient use of resources, preserving traditional values, and creating a stable income for the local people [
18].
The location of tourism development is decided as specified by different assessment criteria. In scientific research, location selection issues are commonly clarified by the multi-criteria decision-making (MCDM) technique. Specifically, this technique is applied in renewable energy construction location selection [
19,
20], hotel construction selection [
21], shopping center choice [
17], service apartment location selection [
15], logistics distribution center location decision [
16], and warehouse location selection [
22]. However, extensive research on choosing agritourism locations focusing on sustainability is still limited. Therefore, the authors propose a hybrid MCDM model to assist decision-makers in finding the most potential destination. The highlight of this study is the use of a spherical fuzzy set to obtain the solution, including two main steps. First, the spherical fuzzy DEMATEL calculates the assessment criteria’s importance and analyzes the causal relationship between them. Next, the spherical fuzzy EDAS method is carried out to rank the alternative destinations. The effectiveness of this model is demonstrated through the case study, and it can be applied to similar subsequent projects.
The research has the following sections:
Section 2 conveys the literature review,
Section 3 explains the methodology,
Section 4 introduces the case study, and
Section 5 displays the discussion and conclusions.
3. Methodology
The decision model of agritourism destination is implemented by integrating the SF-DEMATEL and SF-EDAS techniques with the procedure in
Figure 1.
3.1. Spherical Fuzzy Sets
Fuzzy sets have been introduced, evolved, and applied in recent years to deal with uncertainties in decision-making. Spherical fuzzy sets (SFS), recently defined as fuzzy set extensions developed by Gundogdu et al. [
44,
45,
46], have attracted the attention of researchers. This theory is a combination of Pythagorean fuzzy sets and Neuromorphic sets, in which the uncertain opinion of the decision-maker is individually expressed at the level of membership and non-membership, as denoted by the conditions shown below.
Definition 1. The SFS
of the universe of
L is described in Equation (1):
where
and
.
The numbers , , are the level of membership, non-membership, and hesitance of l to .
Definition 2. The SFS of two values and of the universe of and are illustrated based on some calculations, as shown by the following Equations (2)–(5):
Multiplication by a scalar Definition 3. Spherical weighted arithmetic mean (SWAM) and spherical weighted geometric mean (SWGM) are represented through the weight vector
, where
and
, as shown by the following Equations (6) and (7):
Definition 4. The SFS
of two values
and
of the universe of
and
under the condition
,
,
, are described in Equations (8)–(13):
Definition 5. The value of defuzzification (DeF) of SFS
is demonstrated by the following Equation (14):
To evaluate the interrelationships of factors in complex systems, the DEMATEL method was initially proposed by Fontela and Gabus. A systematic review of MCDM studies found that the DEMATEL method is increasingly used as weighting criteria. In terms of prioritizing alternatives, distance-based evaluation methods, such as TOPSIS or EDAS, are applied effectively, with high frequency. The details of the proposed method are described in the following sections.
3.2. SF DEMATEL
Decision-Making Trial and Evaluation Laboratory (DEMATEL) is a tool of the MCDM approach, which deals with complex and interdependent issues in various fields such as manufacturing [
47,
48], supply chain [
49,
50], technology [
51,
52], hospitality [
53,
54], education [
55,
56], services [
57,
58], and others [
59,
60]. As a result, DEMATEL has become a commonly used tool to determine the potential relationships among these factors and select the best one for the evaluation process. However, the preferences of decision-makers are not considered in the traditional DEMATEL method. Therefore, Sait Gul [
61] developed the spherical fuzzy DEMATEL (SF-DEMATEL) approach, defined as an extension of DEMATEL, to support experts and increase the priority domain and independence in decision-making. In this research, SF-DEMATEL is utilized to calculate the criteria’s weight and the cause–effect relationships of these criteria, conducting nine steps as follows.
Step 1: Identifying related evaluating criteria
It is assumed that a group discussion has k decision-maker, who contributes to and decides on the project’s investment, and n criteria, which affect the assessment and decision.
Step 2: Creating direct influence matrices based on the expert’s evaluation
The linguistic terms of SF-DEMATEL [
61], as shown in
Table 2, are developed to illustrate the expert’s judgment on the influence criteria assessment, denoted as the score index (SI) value, using Equation (15).
According to the pairwise comparisons from experts, the direct influence matrix form (
) is constructed in Equation (16).
where
is the direct influence matrix,
is the spherical fuzzy value of the impact of criterion
ith to
jth by
decision-maker.
Step 3: Calculating the decision-makers weights
The decision-maker’s weights in the decision group reflect the importance of the participants and their experiences. Let
is provided as the spherical fuzzy value by
eth decision-makers, and the decision-maker’s weights (
) can be defined by Liu et al. [
62], as in Equation (17).
where
,
.
Step 4: Establishing aggregated direct influence matrix (
)
In this step, individual comparisons are collected from the decision-makers to synthesize all the evaluations. The aggregated direct influence matrix (
) is constructed through the SWAM process from Equation (6), as shown in Equation (18).
where
is the aggregated SF value of the impact of criterion
ith to
jth.
Step 5: Creating the initial direct influence matrix (X)
The SF value of each comparison contains three dimensions, including membership (
), non-membership (
), and hesitancy level (
). After separating these into three submatrices, the normalization of matrix (
D) will be performed to create the initial direct influence matrix (
X), as defined in Equation (19). The final matrix form in this stage is described as in Equation (20).
where
s is the normalization index.
Step 6: Defining the total influence matrix (T)
The submatrices of
T are transformed from the submatrices of
X by utilizing Equation (21). Then, these matrices are merged into the
T matrix shown in Equation (22).
where
T is the total influence matrix,
X is the direct influence matrix,
is the indirect influence matrix,
and is the SF value of the
T matrix corresponding to the impact from criterion
ith to
jth.Step 7: Computing the sum of spherical fuzzy column (
) and row ( )
The spherical fuzzy of row sum (
) and column sum (
) are calculated by Equations (23) and (24), respectively.
where
is the SF value of the
T matrix corresponding to the influence of criterion
ith on criterion
jth.
Step 8: Determining the value of prominence and relation
In this step, the values of prominence and relation are found through the defuzzification into real numbers, illustrated as score values by utilizing Equation (25).
The term “Prominence” describes the importance level of the criteria ith through the calculus of “”. The calculation of “” is called “Relation”, which divided these criteria into two groups, as shown. This identification of values in the group of cause and effect plays a significant role in evaluating the degree of effect and ranking the criteria.
: the impact of the criteria ith on other criteria, participating in the “cause” group.
: the criteria ith is affected by others, and is defined as the “effect” group.
The weight (
) of the
jth criteria is presented in Equation (26):
Step 9: Painting network relations map (NRM)
In the final step, the casual dependence relationship and the important level among criteria are displayed by the network relations map, in which prominence values illustrate on horizontal axes and relation values define the vertical axes. Specifically, the member of the cause group is drawn on the above, and the remaining member of the relation group expresses the graph below.
3.3. SF EDAS
Evaluation based on distance from average solution (EDAS) is another tool of the multi-criteria decision-making method, which typically solves optimal selection problems based on the preferences of alternatives or attributes. This technique has been applied in many ways, such as finding the most suitable location for solid waste disposition [
63], choosing a sustainable appropriate location for the third-party reverse logistics suppliers based on the combination of fuzzy Critic [
64], identifying optimal alternative energy sources to invest in as new renewable energy by using the fuzzy AHP-EDAS-FMEA approach [
65], finding optimal solutions for health emergencies systems by integrating spherical linguistics [
66], determining the most efficient strategy to improve business performance in the railway industry by applying for trapezoidal fuzzy number EDAS PIPRECIA method [
67], and selecting the best alternative teaching tools in terms of distance learning by incorporating spherical fuzzy AHP EDAS [
68]. This study applies spherical fuzzy EDAS to compute the priority of alternative destinations, following a sequence of steps.
Step 1: Creating SF decision matrix
The evaluation of the linguistics of alternatives among the expert criteria are converted into SF values, as shown in
Table 3. The SF decision matrix
is established by Equation (27):
Step 2: Determining the spherical fuzzy average solution
According to Equations (26) and (27), the SF average solution is found in Equation (28):
Step 3: Converting to the crisp decision matrix and the crisp average solution
Next, the crisp decision matrix and the crisp average are converted by the defuzzification process, as shown in Equation (14).
Step 4: Calculating the value of the positive distance and the negative distance from the average matrix
Based on Equations (27) and (28), the positive and negative distances from the average matrix are identified in Equations (29) and (30).
Step 5: Computing the value of weighted sum positive distance and negative distance
According to Equation (26), the weighted sum positive distance and the weighted sum negative distance are calculated in Equations (31) and (32).
Then, the normalization of weighted sum positive distance and negative distance are computed in Equations (33) and (34).
Step 6: Finding the appraisal score of alternatives
Finally, the alternative’s appraisal score is defined in Equation (35), in which the priority is based on a better score.
5. Discussion and Conclusions
At present, agritourism is becoming a promising alternative for the tourism industry. By both promoting and preserving the value of the natural environment, this model seems to be a key business strategy for sustaining tourism by attracting the interest of many countries worldwide. After the COVID-19 pandemic, ensuring the demand for sustainable tourism is essential. A sustainable tourist destination is an extremely important factor influencing tourists’ choice, and it simultaneously ensures the long-term business operation of the enterprise. Based on the diversity of the agricultural industry, many models combining tourism and agriculture have been implemented in Vietnam. However, efficient exploitation in current agritourism destinations is still limited, and sustainability operations have not been fully developed. Therefore, this study developed a combination of spherical fuzzy MCDM approaches to determine an appropriate destination for sustainable development in agritourism in Vietnam. The SF-DEMATEL technique determines the assessment criteria’s influence and causal relationships, and the SF-EDAS method ranks potential tourist destinations.
In the model’s development, ten assessment criteria, related to aspects such as the economy, environment, society, and natural resources, were suggested. The results from the SF-DEMATEL method illustrate that the most important assessment criteria are local living conditions (ASC10), local agriculture product (ASC3), and accessibility (ASC1). This shows that environmental factors are increasingly focused on agritourism development, which directly impacts natural conditions such as agricultural value, natural landscapes, and ecosystems [
26]. When natural resources are utilized, tourism activities are shown to have potential. Moreover, agritourism also improves the lives of local people, increasing their understanding, and generating additional income for farmers on their land [
76]. In addition, the advantages of various special agriculture products are an important factor, attracting tourists to visit, learn about and experience the area. However, ease of accessibility is also an indispensable criterion in the tourism development strategy. The variety of public transport, convenient transportation, and easy access to information and communication will increase satisfaction and can adapt to customer requirements [
70]. The model results show a causal dependence relationship between the evaluating factors. Local agricultural product (ASC3), scenic resource (ASC4), culture and custom (ASC8), local regulation and policy (ASC6), and waste management (ASC9) are the criteria in the cause group, and the remaining criteria, including accessibility (ASC1), land use (ASC5), awareness of local people (ASC7), and local living conditions (ASC10), belong to the effect group. Local agricultural product (ASC3), scenic resource (ASC4), and culture and custom (ASC8) were found to have a high significance level in the cause group, and changes or improvements in these criteria will greatly influence the criteria in the effect group. Therefore, natural resources play an important role in sustainable agritourism development [
18]. The scale and capacity of agricultural products will extensively impact attempts to expand tourism products. The more distinctive and high-quality the agricultural products are, the better their competitiveness in the market [
25]. Moreover, the rich natural beauty combined with a unique traditional culture, can be properly used as a highlight to influence the customer’s choice [
77]. Consequently, decision-makers should consider these factors when selecting sustainable agritourism locations.
After determining the criteria’s weights, potential alternatives were evaluated and selected performed using the SF-EDAS method. The model’s results indicate that Lam Dong (AD9) is the optimal agritourism destination for sustainable development. Lam Dong has many advantages in terms of natural and ecological conditions combined with a temperate climate, providing a chance for the long-term promotion of agricultural production. This area is now leading the country in terms of high-tech production applications, which are conducted to expand high-quality production activities and attract further investment. Based on those strengths, the combination of agriculture and tourism is an appropriate direction in sustainable development, bringing significant value to both sectors, contributing to product diversification, and improving the local economy. Lai Chau (AD2) and Dong Thap (AD7) are some other priority agritourism destinations in terms of sustainability, based on the results shown in the method.
In conclusion, this study presented a research framework for the assessment and selection of sustainable agritourism destinations. It is an original study, which used a fuzzy multi-criteria decision model combining SF-DEMATEL and SF-EDAS methods to make decisions on agritourism sites. The model’s outcome can undergo practical application in Vietnam, and the model’s stability was considered using a sensitivity analysis. The advantage of this work is its use of previous studies and experts’ opinions to determine the assessment criteria for tourist destinations, and the results show that the environment is a significant factor, matching the current green tourism trend. The proposed research can be a valuable guide when implementing a new type of Vietnam tourism, adapting to actual market needs in the global sustainable development context. Moreover, the proposed model will assist decision-makers and tourism developers in the exploitation of natural resource efficiency and the long-term sustainability of the other tourism projects. However, this study is still limited by the significant influence of qualitative expert judgment on the evaluation results and the scenario development process.