Influencing Factors on Customers’ Decision to Visit Agritourism Farms: A Case Study in Viet Nam

Abstract

The research analyzes the factors affecting the tourists’ decision to visit agricultural farms in Thai Nguyen province. Three hundred tourists from 10 agritourism farms were surveyed, and the results showed that there are five factors affecting the decision of tourists in order of importance, including: “Motivation to travel” has the greatest influence with the coefficient β = 0.290, followed by “Destination image” with the coefficient β = 0.288, the factor “Destination information” with the coefficient β = 0.247 and the group “Other motives” with the coefficient β = 0.229. The factor group, “Infrastructure of the destination”, has the lowest influence with the coefficient β = 0.166. Therefore, solutions to promote the development of agricultural tourism in Thai Nguyen need to pay attention and guide people to cultivate organically, preserve the environment and natural landscape, and create unique experiences for visitors. Authorities, local people, and travel agencies need to have seminars, develop specific agricultural tourism products, and apply information technology in advertising destination images to tourists.

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Hai, N. , Song, N. , Thuy, V. , Huan, N. , Luong, N. and Quang, H. (2022) Influencing Factors on Customers’ Decision to Visit Agritourism Farms: A Case Study in Viet Nam. Agricultural Sciences, 13, 566-579. doi: 10.4236/as.2022.134038.

1. Introduction

Viet Nam has a long history of agricultural production in South East Asia and the world. Along with the development of the tourism industry in general, agritourism has been recently interested and developed [1]. Agritourism can be considered as a combination of two industries: agriculture and tourism [2]. Although there are many definitions of agritourism in the literature, there is no consensus on a single meaning, which still leads to the following general conceptions: Agritourism is a type of tourism that creates mainly tourist products of agricultural activities. Agricultural tourism creates conditions for visitors to contact and experience rural life through activities associated with agriculture, traditional craft villages, rural landscapes, customs and cultural heritage. Through tourism, farmers use tourism to promote their agricultural products and improve their agricultural income. Agricultural tourism contributes to creating jobs, increasing stable income for rural residents, creating sustainable development in the future [3] [4] [5].

Thai Nguyen is the second largest tea-producing region in Viet Nam, with many areas that grow fruit trees, and the province also has many famous agricultural specialties such as “Bao Thai Dinh Hoa” rice, “Tan Cuong” tea, “Dai Tu” tea. This place can completely develop agricultural tourism to create many tourist products that attract and prolong the stay for visitors. In 2019, the number of tourists to Thai Nguyen reached 2.2 million; the revenue reached more than 400 billion VND [6]. Thai Nguyen province strives to reach 2.5 million visitors/year by 2025 and 4 million/year by 2030, emphasizing developing agricultural tourism [7]. In recent years, Thai Nguyen’s agricultural tourism has developed and attracted a significant number of visitors, who come to focus on famous tea and fruit growing areas such as Dinh Hoa, Dai Tu, Vo Nhai, Tan Cuong [8]. Studying the factors affecting tourist destination choice helps farms find some solutions to attract tourists [9].

There are three groups of factors affecting the choice of tourist destination: The first factor is the source of information: It can be the opinion or experience of friends, family, colleagues; word of mouth information; advertising (through the mass media: TV, newspapers, social networks, websites...); The second factor is the visitor’s assessment of the destination, for example, the brand image of the destination; price; or other tangible factors (resources, traffic, services...); The third factor is the motivation of tourists; curiosity; experiencing things that are different from everyday life (e.g., escape from regular residence, away from daily stress, meeting new people, participating in extreme sports activities, trying challenge yourself [10] [11].

Research in Italy on the choice of agricultural farm destinations showed that tourists are interested in the size of the farm, the website, and the price [12]. In addition, other recent studies have also mentioned that if the farm has a website providing detailed and updated information, it will attract many tourists [13] [14]. Some tourists are very interested in agritourism combined with enjoying local cuisine and preserving the natural landscape [15]; many tourists care about the environment at the farms [16]. Ferencova (2012) said that the image of a travel agency such as reputation and image of the travel agency/company, the experience of using the service of the travel agency, the advice and suggestions of acquaintances about the travel agency, using the dependent services of the travel agency, using the dependent services of the travel agency at the destination, the outstanding advantages of the travel agency compared to other companies is a decisive factor to the choice of destination of tourists [17]. 1) Developing guidelines for community-based Tourism, the first step as a basis for the development of community-based tourism laws later; 2) Policies related to the implementation of planning, development of key community tourist areas and destinations; 3) Policies related to the development of community-based tourism associated with the protection of natural and cultural environments; 4) Policies related to tourism management; coordinating monitoring of community tourism resource points; policies related to the local community in community tourism development; 5) Policies related to human resource development; promotion work; developing community-based tourism products [18].

Thus, most of the studies on the factors affecting the tourist destination choice of tourists refer to internal factors (personal factors) and external factors (environmental factors). However, each study only mentions a group of factors or certain factors depending on the research purpose and context. Research showed that there has been no official study for tourists’ choice of destination when visiting agricultural farms in Viet Nam in general and in Thai Nguyen province. Therefore, the results of this study aimed to clarify more clearly the needs of tourists, which factors determine the choice of destinations, which are agricultural farms, thereby proposing some solutions to promote agritourism development in Thai Nguyen province.

2. Methodology

2.1. Data Collection

Data on total agritourism farms area, types of crops grown on farms, forms of tourism business on the farm were collected in Thai Nguyen province. The data is compiled from the statistical report of Thai Nguyen province 2020, from the project summary report: “Developing a sustainable ecological agricultural model associated with Agro-tourism in Thai Nguyen province”.

2.2. Primary Data Collection Method

2.2.1. Research Site Collection: Select Ten Agritourism Farm in Thai Nguyen Province, Viet Nam

02 farms which specialize in tea production combined with tourism in Hoang Nong commune, Dai Tu district. 04 farms which specialize in tea production combined with tourism in Tan Cuong commune, Thai Nguyen city. 02 farms specialize in fruit tree production combined with tourism in Phu Thuong commune, Vo Nhai district. 02 medicinal herbs farm, combined with tourism in Phu Dinh commune, Dinh Hoa district.

2.2.2. Questionnaire and Research Sample

Firstly, the questionnaire was developed based on discussions with agro-tourism experts (5 experts). The questionnaire was used in a trial survey with 10 tourists, and it was corrected before being released to the official survey.

Secondly, Research sample selection: The sample size applied in the study was based on the requirements of Exploratory Factor Analysis (EFA) and multivariate regression, specifically: According to Hair et al. (1998), the minimum sample size for exploratory factor analysis is 5 times the total number of observed variables: n = 5 * 18 = 90 (18 is the number of independent variables). For multivariable regression analysis, the minimum sample size to be achieved is calculated by the formula n = 50 + 8 * 4 = 82 number of tourists [18]. In order to ensure reliability, the study randomly selected tourists to visit agricultural farms in 2020, 30 people each; the total number of tourists interviewed was 300 people. The study uses a 5-point Likert scale to evaluate. The scale of factors affecting the decision to choose a destination consists of 18 observed variables, and the scale of the dependent variable on deciding the destination consists of 4 variables evaluated by the Cronbach Alpha reliability coefficient and Exploratory factor analysis—EFA, which used the varimax rotation for principal components analysis.

2.3. Data Analysis

The data were analyzed using multivariable linear regression analysis, specifically as follows:

*Assessment of scale’s reliability: The scale’s reliability is tested through Cronbach’s alpha coefficient and the total correlation coefficient (Corrected Item − Total Correlation). Use the Cronbach’s Alpha reliability coefficient method before the EFA factor analysis to eliminate inappropriate variables because these garbage variables can create dummy factors. Cronbach’s Alpha reliability coefficient only indicates whether the measures are related or not; but it does not indicate which observed variables should be removed and which should be kept. Then, the calculation of the correlation coefficient between the total variable will help to eliminate those observed variables that do not contribute much to the results to be measured.

Observable variables with a total correlation of less than 0.4 are considered garbage variables and will be excluded. Data is reliable when Cronbach’s Alpha coefficient is in the range (0.6 - 0.95) [19] [20] [21].

*Exploratory factor analysis: The variables are only accepted when the KMO (Kaiser − Meyer − OlKIN) relevance coefficient is in the range (0.5 - 1), and its load weights in other factors are less than 0.35 or the distance between 2 load weights (Factor Loading) with the same variable in 2 different factors greater than 0.3. If the load weight is >0.3, the sample size should be at least 350; if the sample size is about 100, the load weight should be >0.55; and if the sample size is about 50, the load weight should be >0.75. For this study, the chosen load weight is >0.5 because the minimum sample size is 300 samples. In addition, the scale is only accepted when Total Variance Explained > 50%; Bartlett’s coefficient with sig significance < 0.05 to ensure that the factors are correlated with each other; The Eigenvalue coefficient has a value of ≥1 to ensure that the groups of factors are different [22].

*Multivariate regression analysis: The multivariable regression model used to analyze the factors affecting the tourist’s decision to choose a destination has the form: Yi = β0 + β1X1 + β2X2 + β3X3 + ... + βnXn. In which: Yi: is the dependent variable; βo is a constant; X1; X2; ... Xn: Are the independent variables showing the factors affecting the tourist’s decision to choose a destination; β1, β2, ... βn: Are the regression coefficients expressing the impact of the factors X1, X2, ... Xn on the dependent variable Yi.

3. Results

Basic Features of Research Area

The characteristics of some agricultural farms associated with tourism in Thai Nguyen are presented in Table 1.

Table 1 showed that Thai Nguyen province currently has six communes with agritourism farms. Tan Cuong commune has the most agritourism farms, with 9 farms. All people on the farm have their main income from tea production; tourism has only been started since 2017. Dai Tu district has two communes with agritourism farms, namely Hoang Nong and La Bang; in total, the two communes have 13 agritourism farms, these farms also mainly produce tea. The terrain of Hoang Nong and La Bang communes is close to the side of the Tam Dao mountain range, the climate is extremely cool, so it attracts many tourists. Vo Nhai district has Phu Thuong commune with farms specializing in growing fruit trees; custard apple is famously delicious and has a brand name in the market. In Dinh Hoa district, there are farms specializing in growing medicinal plants; tourists coming to Dinh Hoa, in addition to sightseeing, buying herbs, and relaxing, can also visit the ATK historic site, a famous place where President Ho Chi Minh lived and worked from 1945-1954. Previously, households on

Table 1. Quantity and characteristics of some agritourism farms in Thai Nguyen.

Source: Direct survey data [6] [23].

tourist farms only had their income mainly from agricultural products, but from 2017 onwards, the farms have additional income from experience activities services. Moreover, products are sold to tourists. Agritourism can boost the rural economy as farmers have gradually diversified their production activities into other sectors. The tourism sector supplements additional income from agricultural activities [24] [25]. The development of agritourism will create benefits in terms of economic and environmental, and socio-cultural benefits. Research data from 873 US farms showed that agritourism farms approach sustainability to a greater extent than other farms, generating many environmental, cultural, and social benefits [26].

In order to assess which factors determine the choice of destination of tourists, 300 tourists were randomly selected from tourists of 10 farms. The characteristics of visitors are presented in Table 2.

The number of male visitors to the experience areas accounted for 61.33%, while the female only accounted for 38.67%. Visitors from Thai Nguyen still make up the majority (due to the Covid pandemic in 2020), accounting for 66.0%. In addition, there are guests from Hanoi and neighboring provinces a small number of visitors from Saigon.

Table 2. Characteristics of tourists visiting agritourism farms.

The scale to study the factors affecting the decision to choose a tourist destination includes 18 observed variables, including 4 dependent variables, using a 5-level Likert scale through calculating the Cronbach Alpha coefficient and exploratory factor analysis; the results are presented in Table 3. The factors affecting

Table 3. Results of reliability analysis (Cronbach’s Alpha) of factors affecting the tourists’ decision to visit agricultural farms.

Source: Direct survey data.

the destination choice of tourists are built based on previous studies [24] [25] [27] and reference consult a group of experts on agritourism.

When testing the scale’s reliability by Cronbach’s Alpha coefficient, if a measurement variable has a correlation coefficient of the total variable Corrected Item − Total Correlation ≥ 0.3, then that variable meets the requirements [17]. The analysis of the total variable correlation coefficient showed that, out of 18 independent variables and 04 dependent variables, there were 16 variables with the total variable correlation coefficient greater than 0.3, meeting the requirements of reliability, consistent with the total variable. For the next analysis, 02 variables had a total correlation coefficient less than 0.3 (T5 = 0.234, T6 = 0.298). Therefore, remove the variable; “know the destination information through the introduction program of Thai Nguyen province” and turn “Through TV station” out of the input factor group and rerun the model to ensure that the variables have the total variable correlation coefficient greater than 0.3.

The results of testing the scale’s reliability using Cronbach’s Alpha coefficient showed that all the factor groups have Cronbach’s Alpha coefficients in the range from 0.683 - 0.732, proving that the research data is reliable.

*Exploratory factor analysis EFA with independent variables

Data in Table 4 showed the results of testing the suitability of factor analysis, showing that the coefficient of KMO (Kaiser − Meyer − Olkin) = 0.698, satisfying the condition 0.5 < KMO < 1. Thus, the factor analysis discovery factor is suitable for real data. In addition, Barlett’s test with the value sig = 0.000 < 0.05 showed that the actual data is consistent with the EFA analysis and the observed variables are linearly correlated with the representative factor. The results of the assessment of the influence of factors on tourists’ decisions are shown in Table 5.

The results of Table 5 showed that the total extracted variance of the independent variable is 60.017 > 50% (satisfactory EFA analysis). This data shows that 60.017% of the change of the outcome factor is due to the factors (variables) given in the model; that is, the observed variables in this study explained 60.017% of the variation of the decision. The choice of destination is agricultural farms.

The results of Table 6 rotation matrix determining the load weights showed that from 04 groups of factors with 16 observed variables are arranged

Table 4. Results of KMO and Bartlett’s Test of factors affecting tourists’ decision to choose destinations.

(Source: Analytical results).

Table 5. Total explanatory variance, load weight of rotation matrix for factors affecting tourists’ decision to choose destination.

(Source: Analytical results).

Table 6. The results of the load weight of the rotation matrix of factors affecting the destination choice of tourists.

(Source: Analytical results).

Table 7. Reliability analysis results—Cronbach’s Alpha newly formed factor group.

(Source: Analytical results).

into 05 groups without the original order. Load coefficients of all variables are >0.5. Group of travel motives forms 02 groups: Group 1 denotes DC including variables (DC1, DC2, DC3), and Group DCK includes variables (DK3, DK4 and DK5), destination infrastructure group (HT). Destination Information Group (TT) and Destination Image Group (HA).

Test the reliability of the scale for two newly formed groups of factors. The analysis of the total correlation coefficient showed that the variables with a total correlation coefficient greater than 0.3 met the requirements of reliability. The results of testing the scale’s reliability by Cronbach’s Alpha coefficient all have Cronbach’s Alpha coefficients in the range of 0.6 - 0.95 (Table 7).

*Multivariate regression analysis to determine the influence of factors

The results of multivariable regression analysis in Table 8 show that: Sig coefficient. = 0.00 is less than the significance level α = 1%, so the regression model is significant; the independent variables affect the dependent variable Y. The adjusted R2 value = 0.564 indicates that the independent variables in the dependent variable Y. The model can explain 56.4% of the variation of the dependent variable (decision to choose a destination) and shows 56.4% of the influence of the factors on the decision to choose a destination. To be explained by 05 factors and 16 observed variables included in the research model. Analysis of variance results for Sig. value = 0 shows that the multivariable linear regression model is suitable for this study. Besides, the Durbin Watson coefficient (d) has the value = 1.960, ranging from 1 to 3, showing that the model does not have autocorrelation. Variance Inflation Factor (VIF) of all variables included in the model is less than 2, so the research model does not have multicollinearity. Also, sig. T-test, the regression coefficients of the independent variables are all less than 0.05, so these independent variables are significant to explain the dependent variable (decision of destination choice), and no variable is excluded from the model.

The coefficients (β) in Table 8 all have positive signs, showing that 05 groups of factors have a linear relationship in the same direction with the decision to choose a destination of tourists. The coefficient (β) indicates the importance of the groups of factors in the research model. The group of factors “Motivation to travel” has the most important significance, meaning the greatest influence with the coefficient β = 0.298; this study is similar to the study of Dwi Suhartanto (2020) [28], followed by the factor groups “Destination image” with the coefficient β = 0.288, the factor “Destination information” with the coefficient β = 0.247 and the group “Other motives” with the coefficient β = 0.229. The factor group, “Infrastructure of the destination”, has the lowest influence with the coefficient β = 0.166.

From the normalized regression coefficient, we can determine the regression equation of the form:

Y = 0.609 + 0.298 * DC + 0.288 * HA + 0.247 * TT + 0.166 * CS + 0.229 * DCK

The study also showed that in five groups of factors, the group of motivation to travel with the reason of wanting to explore rural life, wanting to mingle with

Table 8. Results of linear regression analysis of factors affecting tourists’ decision to choose destinations.

(Source: Analytical results).

the natural scenery and wanting to enjoy local products) is the factor that has the most influence on the decision of the tourists. The survey results of 217 tourists while on agricultural tourism in Iran also gave similar results [29]. The second factor that plays an important role in the image of the destination because agricultural farms have not unique natural scenery associated with places with beautiful scenery but also have unique experiences such as: picking tea, taking care of fruit trees, medicinal herbs, buying herbs. This study also coincides with the study of Mataveli and Gil (2018), showing that when traveling on agricultural farms, tourists want to learn a different way of life and want to participate directly in agricultural activities [30].

4. Results and Recommendations

Research results showed that agritourism has been developed in several agricultural farms in the Thai Nguyen province, especially farms with income from crop products such as tea, fruit trees, and combined medicinal herbs.

The study initially identified five groups of factors affecting tourists’ decision to choose a tourist destination, in which the factor of travel motivation (such as wanting to explore rural life, wanting to explore the countryside, being in harmony with the natural scenery and wanting to enjoy local products) is the factor that has the most influence on tourists’ decision to choose a destination, which is agricultural farms. The second factor that plays an important role in the destination’s image is that agricultural farms do not have unique natural scenery, associated with places with beautiful scenery, but have unique experiences such as picking tea, taking care of fruit trees, and medicinal herbs. Therefore, to promote sustainable agricultural tourism in Thai Nguyen, it is necessary to pay attention to the development of organic agriculture, preserve the environment, protect the natural landscape, and create unique experiences for tourists.

Destination information is very important to visitors, but because agritourism has just started to develop in Thai Nguyen in recent years, the province’s investment and promotion campaigns for agricultural tourism are still not diversified. Most tourists know about Thai Nguyen’s tourist camps through social networking sites through fan pages, so the government, local people, and travel agencies need to build new products. Agricultural tourism products with specific characteristics, applying information technology in advertising destination images for tourists.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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