Factors Influencing Tourist Satisfaction at a Biosphere Reserve: The Case of Ca Mau Cape, Vietnam

Motivating sustainable economic development is one of the essential functions of biosphere reserves. Ecotourism and responsible tourism are effective tools that enable biosphere reserves to fulfill this function. Tourist satisfaction acts as a crucial catalyst for tourism at biosphere reserve destinations. This study aims to analyze the factors within the biosphere reserve that influence tourist satisfaction. The results of a multivariate regression analysis based on data from 150 observations indicate that multiple factors simultaneously impact tourist satisfaction. These factors include security and safety, tourism facilities, infrastructure, service pricing, tourism resources, and service staff. Based on the research findings, practical implications are proposed to enhance destination quality and improve tourist satisfaction.

Introduction

Tourism is one of the fastest-growing industries in terms of income and employment. Therefore, developing tourism in biosphere reserves represents a viable option to support local communities (Bires & Raj, 2020). When local communities benefit more equitably from tourism, they are likely to develop a greater sense of awareness and responsibility towards conserving resources within the biosphere reserve (Mustapha & Azman, 2013). A biosphere reserve is a prestigious designation awarded by the United Nations Educational, Scientific and Cultural Organization (UNESCO) to areas of significant geographical value and conservation importance (Sánchez-Hernández & Maldonado-Briegas, 2023). A critical function of biosphere reserves is to integrate biodiversity conservation with the sustainable development of local communities (Maet al., 2009). Unlike most other sectors, tourism can make a positive contribution to conservation efforts (Buckley, 2010). When managed appropriately, tourism can serve as an important means to ensure sustainable development for local communities (Nechifor, 2014). Thus, the functions of biosphere reserves and tourism have many overlapping aspects. Sustainable tourism, in general, and specifically within biosphere reserves, cannot be realized without ensuring tourist satisfaction (Asmelash & Kumar, 2019). Tourist satisfaction reflects the emotional states of visitors based on their experiences (Tung & Ritchie, 2011). Satisfaction arises when tourists’ experiences exceed their expectations for the trip; conversely, dissatisfaction occurs when service or product performance does not meet expectations (Sadehet al., 2012).

The Ca Mau Cape Biosphere Reserve covers an area of 371,506 hectares across the districts of U Minh, Tran Van Thoi, Phu Tan, Nam Can, and Ngoc Hien in Ca Mau Province. With significant values for conservation, environmental protection, and economic development, the Ca Mau Cape Biosphere Reserve was recognized as a World Biosphere Reserve by UNESCO in 2009. Typical ecosystems within this reserve include coastal mangrove forests, cajuput forests on peatlands, marine islands, and agricultural ecosystems. In line with the implementation plan of the Provincial Party Committee’s resolution on tourism development until 2020 and orientation towards 2030, Ca Mau Cape Biosphere Reserve encompasses three out of four key areas for tourism development in the province: Ca Mau Cape National Park, U Minh Ha National Park, and Da Bac Island; the other area outside the biosphere reserve is Ca Mau City (People’s Committee of Ca Mau province, 2017).

Tourist satisfaction is a crucial factor for success in the tourism industry in general and for specific tourist destinations (Nhan & Dua, 2019; Sadehet al., 2012). Research on factors influencing tourist satisfaction has been documented extensively (Royet al., 2016; Sadehet al., 2012; Shahrivar, 2012; Suanmali, 2014). However, this topic remains under-researched within the context of biosphere reserves. The primary objective of this study is to analyze the factors influencing tourist satisfaction at Ca Mau Cape Biosphere Reserve in Vietnam. The research findings will contribute to expanding knowledge about tourist satisfaction at biosphere reserve destinations. Additionally, practical implications will be proposed to enhance destination quality and improve tourist satisfaction.

Literature Review and Hypothesis Formation

Biosphere reserves are designated by UNESCO for areas of significant importance in promoting sustainable development while conserving biodiversity and cultural heritage. Their functions include conservation, development, and logistical support (including education and research) (Crozat, 2013; Habibahet al., 2013). Sustainable tourism is increasingly recognized as a critical component of biosphere reserves, aiming to enhance local economies while minimizing negative environmental impacts (Wilkinson & Coles, 2024). The success of tourism development in these reserves heavily depends on management structures. Community participation in management is essential to legitimize activities conducted in these areas (Crozat, 2013). An effective development model within biosphere reserves combines local knowledge with collaboration among various stakeholders to ensure equitable distribution of tourism benefits (Hoppstadius, 2019). This approach not only enhances the social structure of the involved community but also supports local economic growth. While biosphere reserves possess significant tourism potential, they face challenges such as land-use conflicts and climate change (Crozat, 2013; Wilkinsonet al., 2023). Strategies for successful tourism development in biosphere reserves include developing educational tourism models, integrating tourism with conservation efforts, enhancing biodiversity conservation initiatives, and incorporating technological advancements and alternative energy sources (Crozat, 2013; Habibahet al., 2013; Wilkinsonet al., 2023).

Tourist satisfaction is a crucial factor for sustainable tourism development as it directly influences the longevity and success of tourism initiatives. Moreover, it affects the long-term viability of tourist destinations. The level of tourist satisfaction correlates with how well a destination meets their expectations throughout their travel experiences. When tourists’ experiences meet or exceed their expectations, high satisfaction is achieved; conversely, dissatisfaction arises when service or product performance falls short (Baker & Crompton, 2000). High levels of satisfaction can lead to repeat visits, positive word-of-mouth referrals, and increased economic benefits for local communities (Kwortnik & Thompson, 2009).

Given the close relationship between biosphere reserves and national parks, nature reserves, and landscape protection areas, studies examining factors influencing tourist satisfaction in these contexts are relevant. A study by Leet al. (2022) on factors affecting tourist satisfaction with ecotourism services at U Minh Thuong National Park in Kien Giang Province found that satisfaction was influenced by factors such as tourism resources, infrastructure, accommodation responsiveness, service pricing, and service style. Research on factors affecting tourist satisfaction regarding service quality at Xeo Quyt Landscape Protection Area in Dong Thap Province revealed that understanding, reliability, tangible aspects, and service pricing positively impacted tourist satisfaction (Thai & Dang, 2019). Lee and Bae (2006) found that natural resources, cultural resources, and facilities in national parks significantly influence tourist satisfaction. A study conducted at Cat Tien National Park indicated that transportation means, security and safety, guides, and accommodation facilities affect tourist satisfaction (Tran & Bui, 2017).

Based on the reviewed literature, the hypotheses for this study are proposed as follows:

Hypothesis 1 (H₁): Tourism resources at Ca Mau Cape Biosphere Reserve positively influence tourist satisfaction.

Hypothesis 2 (H₂): Infrastructure at Ca Mau Cape Biosphere Reserve positively influences tourist satisfaction.

Hypothesis 3 (H₃): Tourism facilities at Ca Mau Cape Biosphere Reserve positively influence tourist satisfaction.

Hypothesis 4 (H₄): Security and safety at Ca Mau Cape Biosphere Reserve positively influence tourist satisfaction.

Hypothesis 5 (H₅): Service pricing at Ca Mau Cape Biosphere Reserve positively influences tourist satisfaction.

Hypothesis 6 (H₆): Service staff at Ca Mau Cape Biosphere Reserve positively influences tourist satisfaction.

The model for this study is illustrated in Fig. 1.

Fig. 1. Research model.

Method

The model of this study comprises six independent constructs and one dependent construct, as illustrated in Fig. 1. All constructs are derived from relevant previous research. The tourism resources construct includes four variables adapted from the study by Leet al. (2022). The infrastructure factor consists of four variables borrowed from the studies by Leet al. (2022) and Tran and Bui (2017). The tourism facilities construct is measured by four variables modeled after the research by Leet al. (2022) and Tran and Bui (2017). The latent variable of security and safety is represented by five observable variables developed from the study by Tran and Bui (2017). The external construct service pricing comprises five observable variables formed from the research by Leet al. (2022). The service staff measurement includes four observable variables referenced from the studies by Leet al. (2022), Tran and Bui (2017), and Thai and Dang (2019). In addition to these independent measures, the study also includes one dependent measure (tourist satisfaction), which consists of four observable variables inherited from Oliver (1997).

The data collection tool used in this study is a questionnaire structured into three sections. Section 1 contains questions regarding the demographic characteristics of respondents. Section 2 addresses questions about the quality of destination attributes, while Section 3 measures tourist satisfaction. Nominal and ordinal scales are utilized for measurements in Section 1. Questions in Sections 2 and 3 are measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Prior to conducting the official survey, the structure and content of the questionnaire were reviewed by three tourism experts.

The study employs 26 observable variables to measure independent aspects, necessitating a minimum sample size of 130 respondents (Hairet al., 2010). This research surveyed 150 tourists visiting the study area. Participants were selected through convenience sampling, and they self-administered the survey. The data collection took place during September and October 2024. The survey data were analyzed using descriptive statistics, reliability testing of measurement scales, exploratory factor analysis, and multivariate linear regression analysis.

Results

Overview of Sample Demographic Characteristics

The sample structure of this study is presented through various dimensions. In terms of gender, females represent a higher proportion than males, with counts and percentages of 84 (56%) and 66 (44%), respectively. Regarding age, tourists aged 17 to 24 and those aged 25 to 34 constitute the largest groups, with 82 (54.7%) and 38 (25.3%) individuals, respectively. Tourists aged 35 to over 55 total 30, accounting for 20%. In terms of occupation, students, businesspeople, and traders dominate the sample structure, with counts of 58 (38.7%) and 46 (30.7%), respectively. The number and percentage of tourists who are farmers and workers in state jobs are nearly equivalent, with 23 (15.3%) and 22 (14.7%) individuals, respectively. The remaining respondents include one retiree, constituting 0.7%. The majority of tourists participating in the study are first-time visitors to the biosphere reserve, totaling 95 individuals (63.3%). Tourists who have visited two to three times amount to 27 (18%), while the remaining 28 individuals (18.7%) have visited more than three times.

Reliability Testing of Measurement Scales

This study employs multiple variables to measure each construct. To ensure consistency in measurement, a reliability test of the scales was conducted. The most commonly used method for assessing the consistency of a scale is Cronbach’s alpha. Two indices were utilized to measure the reliability of the scales: Cronbach’s alpha and the corrected item-total correlation. The recommended thresholds for these two indices are greater than 0.7 for Cronbach’s alpha and greater than 0.3 for the corrected item-total correlation (Hairet al., 2010). The results presented in Table I indicate that Cronbach’s alpha for the six independent scales and one dependent scale ranges from 0.822 to 0.900, while the corrected item-total correlation coefficients for the variables range from 0.551 to 0.808. Thus, these scales demonstrate high consistency and ensure good reliability.

Table I. Results of Reliability Analysis of Measurement Scales

Scale	Number of variables	Variables excluded	Corrected item-total correlation	Cronbach’s alpha
Tourism resources	4	None	0.665–0.744	0.860
Infrastructure	4	None	0.644–0.690	0.836
Tourism facilities	4	None	0.620–0.745	0.837
Security and safety	5	None	0.670–0.808	0.896
Service pricing	5	None	0.683–0.776	0.888
Service staff	4	None	0.551–0.689	0.822
Tourist satisfaction	4	None	0.761–0.785	0.900

Exploratory Factor Analysis

The validity of the measurement scales is demonstrated through both convergent and discriminant validity. A commonly used technique for testing the validity of latent variables is exploratory factor analysis (EFA). Several criteria are proposed to assess the suitability of the data for factor analysis, including the Kaiser-Meyer-Olkin (KMO) measure of sampling adequacy, the p-value from Bartlett’s test of sphericity, and the cumulative percentage of total variance explained. The recommended values for these criteria are KMO > 0.5, p-value < 0.05, and total variance explained >50% (Hairet al., 2010). The results of the data analysis indicate that the data are suitable for exploratory factor analysis, with a KMO value of 0.925 for the independent scales, a p-value from Bartlett’s test of 0.000, and a total variance explained by six factors of 70.995%. Similarly, the KMO value for the dependent scale is 0.845, with a p-value from Bartlett’s test also below 0.001 and a total variance explained by one factor of 76.982%. Using principal components extraction with eigenvalues greater than 1 and Varimax rotation, along with factor loadings ≥0.5, the data analysis yielded six independent factors and one dependent factor, as shown in Table II.

Table II. Results of Exploratory Factor Analysis

Number	Factor name	Observed variables	Eigenvalues	Factor loadings
1	Service pricing	Sp1, Sp2, Sp3, Sp4, Sp5	11.301	0.669–0.749
2	Security and safety	Ss1, Ss2, Ss3, Ss4, Ss5	1.912	0.589–0.817
3	Tourism facilities	Tf1, Tf2, Tf3, Tf4	1.583	0.633–0.827
4	Infrastructure	In1, In2, In3, In4	1.505	0.637–0.818
5	Service staff	St1, St2, St3, St4	1.147	0.636–0.787
6	Tourism resources	Ts1, Ts2, Ts3, Ts4	1.010	0.678–0.811
7	Tourist satisfaction	S1, S2, S3, S4	3.079	0.867–0.883

Multivariate Linear Regression Analysis

Multivariate linear regression analysis was utilized to predict the impact of independent variables on the dependent variable. The results of the data analysis indicate that the coefficient of determination (R²) for tourist satisfaction is 0.535, suggesting that 53.5% of the variance in tourist satisfaction can be explained by the independent variables. The Durbin-Watson value is 1.707, indicating that there is no first-order autocorrelation present in the residuals. The p-value from the ANOVA analysis is below 0.001, implying that the independent factors are significantly correlated with the dependent factor. The variance inflation factor (VIF) is equal to 1, suggesting that there is no multicollinearity among the independent variables (Hairet al., 2010). These analyses confirm that the market data are suitable for regression analysis.

Discussion and Implications

The alignment between the research model and market data is well established through the reliability assessment of the measurement scales and exploratory factor analysis. The scales demonstrated strong reliability, the data were suitable for factor analysis, and the results confirmed both convergent and discriminant validity. Furthermore, the data were appropriate for multivariate regression analysis, supporting the research hypotheses. Hypothesis 1 (H₁) is accepted (p < 0.001, β = 0.249), indicating that tourism resources positively influence tourist satisfaction. Infrastructure also contributes positively to tourist satisfaction (p < 0.001, β = 0.322); thus, Hypothesis 2 (H₂) is accepted. Hypothesis 3 (H₃) is accepted as well, with tourism facilities having a positive impact on tourist satisfaction (p < 0.001, β = 0.331). Tourist satisfaction is influenced by security and safety, leading to the acceptance of Hypothesis 4 (H₄) (p < 0.001, β = 0.340). Hypothesis 5 (H₅) cannot be rejected; it is accepted (p < 0.001, β = 0.309), showing that service pricing positively affects tourist satisfaction. With p < 0.001 and β = 0.220, the service staff has a positive relationship with tourist satisfaction, resulting in the acceptance of Hypothesis 6 (H₆).

Based on the standardized regression coefficients (β) from Table III, it is evident that security and safety, tourism facilities, infrastructure, service pricing, tourism resources, and service staff all positively influence tourist satisfaction.

Table III. Results of Multivariate Linear Regression Analysis

Factor	Hypothesis	B	β	t	Sig.	VIF	Decision
Constant		−4.609.E−0.17		0.000	1.000
Service pricing	H5	0.309	0.309	5.417	<0.001	1.000	Accepted
Security and safety	H4	0.340	0.340	5.964	<0.001	1.000	Accepted
Tourism facilities	H3	0.331	0.331	5.802	<0.001	1.000	Accepted
Infrastructure	H2	0.322	0.322	5.654	<0.001	1.000	Accepted
Service staff	H6	0.220	0.220	3.816	<0.001	1.000	Accepted
Tourism Resources	H1	0.249	0.249	4.367	<0.001	1.000	Accepted

Security and safety are critical determinants of tourist satisfaction as they directly affect visitors’ experiences and overall perceptions of the destination. In this study, security encompasses food safety management, handling aggressive vendors, managing begging situations, and preventing theft. A destination perceived as safe for health and property will leave a more favorable impression on tourists. This finding aligns with previous studies by Tran and Bui (2017). These emphasize the need for regular security personnel presence, surveillance systems, and customer support services at the destination.

The adequacy and quality of tourism facilities play a pivotal role in shaping tourists’ experiences. Well-maintained and accessible facilities provide comfort during their stay and significantly enhance tourist satisfaction (Riwuet al., 2024). This study highlights that factors such as the cleanliness of dining establishments, safety and comfort of transportation means, variety in accommodation types, and availability of amenities are crucial for improving visitor experiences. These findings corroborate research by Lee and Bae (2006) and Tran and Bui (2017). To enhance the visitor experience, the destination needs to ensure standards of cleanliness, safety, comfort, variety and good maintenance.

Along with tourism facilities, infrastructure plays an important role in creating convenience for tourists in many tourism activities. This factor not only creates a favorable environment for travel and living but also enhances tourist satisfaction. A positive relationship between infrastructure and tourist satisfaction was found in this study. This result supports the study of Leet al. (2022). In this study, infrastructure includes the availability of electricity and water, as well as the quality of roads, parking lots, and toilets. Each of these factors plays an important role in meeting the needs and expectations of tourists and affects their satisfaction. Therefore, not to lack electricity and water and to have a quality road system, parking lots and toilets are necessary for the destination.

Service pricing significantly shapes tourist satisfaction as emotional responses are influenced by comparisons between price levels and perceived service value. Factors contributing to service pricing include reasonable admission fees for attractions, dining costs, accommodation rates, transportation fares, and additional service charges, all of which positively affect tourist satisfaction. This is consistent with findings from Leet al. (2022) and Thai and Dang (2019). Therefore, a pricing strategy based on value perception while simultaneously enhancing service quality is recommended.

Tourism resources are pivotal in determining tourist satisfaction levels. The interplay between natural beauty and cultural richness directly influences how tourists perceive their experiences. Research indicates that the uniqueness and preservation of natural phenomena and cultural heritage correlate positively with tourist satisfaction (Nguyenet al., 2021; Sahroni & Alkemega, 2024). This study affirms that rich ecosystems, environmental cleanliness, diverse attractions, and local culinary appeal all contribute positively to tourist satisfaction, echoing findings from Lee and Bae (2006) and Leet al. (2022). Thus, proactive measures to protect natural and cultural resources are essential for maintaining their appeal.

Service staff frequently interact with tourists, and these interactions can significantly influence the tourist experience. Positive experiences in these interactions can lead to favorable perceptions of the destination and overall satisfaction, encouraging repeat visits and recommendations (Perićet al., 2018; Sahroni & Alkemega, 2024). The friendliness, openness, warmth, politeness, and professionalism of service staff had a positive influence on tourist satisfaction in this study. Several studies (Leet al., 2022; Tran & Bui, 2017) also confirmed that the service attitude of staff and the performance of tour guides influence tourist satisfaction. Regular training programs focusing on customer service skills, traveler psychology, the art of communication, and problem-solving can help employees effectively meet traveler expectations.

Conclusion

The topic of factors influencing tourist satisfaction with biosphere reserve destinations remains underexplored. Biosphere reserves not only serve conservation purposes but also support tourism activities and local economic development. They provide significant experiences and impacts on tourists’ perceptions. Tourist satisfaction with the Ca Mau Cape Biosphere Reserve is positively influenced by various factors, including security and safety, tourism infrastructure, service pricing, tourism resources, and staff performance. Each of these factors plays a crucial role in shaping the overall experience of visitors. To enhance tourist satisfaction, management of the biosphere reserve should focus on establishing security and safety, improving tourism infrastructure, upgrading facilities, ensuring reasonable service pricing, protecting tourism resources, and training service staff. In addition to contributing theoretical and practical insights, this study also has some limitations. Firstly, it relies on familiar measurement scales. Secondly, the sample structure does not adequately represent various demographic groups. To validate the results of this study and expand knowledge about tourist satisfaction with biosphere reserve destinations in different contexts, further research on this topic is necessary.