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Asian Business Research Journal 
Vol. 10, No. 10, 20-27, 2025 
ISSN: 2576-6759 
DOI: 10.55220/2576-6759.593 
© 2025 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Exploring Factors Affecting Wellness Tourists' Behavioral Intention in Guangxi 

 
Yun Zheng1    
Izdihar B. Baharin @ Md. Daud2 
Dongju Wang3 
Jun Lei4

 
 

 
 
 

1School of Humanities and Management, Youjiang Medical University for Nationalities, Baise, China, SEGI 
University of Malaysia. 
2UNIKL Business School, Kuala Lumpur University, Malaysia. 
3College of Basic Medical Sciences, Hainan Medical University, Haikou, China. 
4Sports Management Department, Guangxi College of Sports Education, Nanning, China. 
Email: zhengyun0208@qq.com  
Email: izdihar70@gmail.com  
Email: hy0115004@muhn.edu.cn  
Email: jun.lei@stu.nida.ac.th   
( Corresponding Author) 
 

 
Abstract 

This study investigates factors influencing wellness tourists’ post-travel behavioral intentions in 
Guangxi, China. Using a quantitative, cross-sectional design, data were collected from 124 valid 
questionnaires distributed to tourists who had visited wellness resorts or centers within the past 
three years. Data were analyzed using SPSS and Smart PLS 4.0. Results show that experience 
quality, perceived wellness value, and tourist satisfaction have significant positive effects on 
behavioral intention, with satisfaction being the strongest predictor. Perceived wellness value 
more strongly influenced satisfaction than experience quality. Satisfaction also mediated the 
effects of experience quality and perceived wellness value on behavioral intention. The findings 
highlight satisfaction and perceived wellness value as key drivers of tourists’ loyalty and support 
strategies for sustainable wellness tourism in Guangxi. 

 
Keywords: Wellness Tourism, Behavioral Intention, Tourist Satisfaction, Experience Quality, Perceived Wellness Value. 

 
1. Introduction 

Guangxi has emerged as a prominent wellness tourism destination, leveraging its unique ecological and 
cultural endowments to drive industry growth. Endowed with lush forests, mineral hot springs, and a reputation 
for longevity (exemplified by Bama), the region has built a solid foundation through strategic planning and 
resource integration. Policy support includes top-tier designs like the Guangxi Elderly Health Tourism 
Development Plan (2022-2025) and the launch of 20 premium wellness routes, such as Guilin’s landscape health 
retreats and Hezhou’s forest spa experiences (GuangxiDaily, 2023). In 2024, forest wellness and ecological tourism 
alone generated over 230 billion yuan in comprehensive revenue, with 43 new brand bases accredited, reflecting 
robust market expansion. Regional collaboration has also strengthened, with 148.6 billion yuan in signed 
investments from key markets like the Guangdong-Hong Kong-Macao Greater Bay Area. Tourist satisfaction and 
revisit intention are pivotal to sustainable development, as research confirms a direct correlation between 
experience quality and long-term loyalty. Guangxi’s wellness tourism, despite its rich resources like longevity 
villages and ethnic medical heritage, faces notable deficiencies in perceived wellness value and tourist experience 
(China's Reform and Development, 2023), hindering its quality upgrade. Tourists are unable to perceive concrete 
health gains, weakening their willingness to pay. 
 

2. Literature Review  
2.1. Underpinning Theory 

The present study focuses on exploring the factors influencing the behavioral intention of wellness tourists in 
Guangxi, with a core emphasis on the relationships between key variables. Theory of Planned Behavior and 
Expectation-Confirmation Theory serve as foundational support to elaborate on these relationships. 
 

2.1.1. Theory of Planned Behavior (TPB) 
TPB is an expansion of reasoned action theory (TRA) (Ajzen, 1991; Ajzen & Fishbein, 1975). According to the 

TPB, people will act in a certain way if they have the means, opportunities, and skills to carry out the behavior and 
if they feel the activity will lead to particular outcomes that they value (Ã & Hsu, 2006; Icek Ajzen, 1985). When 
applied to wellness tourism, TPB provides a robust framework for understanding and predicting tourists’ 
behavioral intentions and decision-making processes (Joo et al., 2020; Siddiqui & Hamid, 2023; Zhao & An, 2021). 

mailto:zhengyun0208@qq.com
mailto:izdihar70@gmail.com
mailto:hy0115004@muhn.edu.cn
mailto:jun.lei@stu.nida.ac.th
https://doi.org/10.55220/2576-6759.593


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2.1.2. The Expectation–Confirmation Theory (ECT) 
The Expectation–Confirmation Theory (ECT) (Oliver, 1980) in the study of consumer behavior states that 

customers go through an assessment process before deciding whether or not to repurchase. Prior to making a 
purchase, customers establish preliminary expectations on a certain service or good with their past interactions and 
current knowledge (Oliver, 1980). After utilizing the product or service, customers evaluate its performance and 
contrast it with what they had anticipated (Oliver, 1980). The degree of satisfaction and chance of repeat business 
for the customers ultimately depend on how well the perceived performance matches their initial expectations 
(Chiu et al., 2020; Oliver, 1980). According to the ECT, the continuation intention is preceded by three constructs: 
contentment, perceived utility, and expectation confirmation. When the perceived performance meets or beyond 
initial expectations, a sense of satisfaction arises, which in turn generates an inclination to continue using the 
service. When the actual performance falls short of the expectations, users become dissatisfied and stop using the 
product (C.C & Prathap, 2020). This theory is well-known in the field of customer satisfaction and is seen crucial in 
shaping consumers' behavioral intention (Basil Chibuike et al., 2021; Jeong et al., 2019). Tourists' satisfaction with 
their wellness value and experience in Guangxi may act as a mediator between their initial expectations and their 
subsequent behavioral intentions. Higher satisfaction levels enhance the likelihood of positive behavioral intention. 
 

2.2. Dependent Variable: Factors of Tourist’s Behavioral Intention 
Behavioral intention is a foundational concept in understanding consumer behavior, characterized as a key 

indicator of loyalty and future action tendencies. Oliver and Swan (1989) identify it as a critical loyalty trait 
reflecting customers’ intended behaviors (Oliver & Swan, 1989), while Ajzen and Fishbein (1975) define it broadly 
as the potential to engage in a specific behavior (Ajzen & Fishbein, 1975). It encapsulates individuals’ tendencies, 
experiences, and feelings toward products or services, representing a planned commitment to carry out actions 
(Glendon, 1998). 

Tourist satisfaction, perceived wellness value, and multi-dimensional experiences during travel are the main 
factors influencing behavioral intention. It has been established that visitors’ satisfaction is a significant 
precondition that directly influences visitors' propensity to return and refer (Zeng & Li, 2021). Since wellness 
tourists have relatively clear health-related goals, whether tourists feel they have become healthier and whether 
they perceive that the time and money they have invested have yielded health-related returns determine their 
subsequent consumption behaviors (Chelliah et al., 2021). Experiences had a major influence on their level of 
pleasure and inclination to return. determining the contribution of visitors' experiences to the development of 
happy and returning patrons (Lee et al., 2020). These indicators collectively help people gain a comprehensive 
understanding of how tourists transform their positive experiences into future actions and influence, thereby 
providing support for the sustainable development and promotion of wellness tourism. However, in existing 
studies on the factors affecting tourism behavioral intention, there are still some research gaps regarding how 
tourist satisfaction, perceived wellness value, and travel experiences influence tourists' future behaviors. 
 

2.3. Tourist Satisfaction 
Tourist satisfaction is a comprehensive state of emotional activation and cognitive evaluation formed based on 

the “expectation-perception” comparison. It is tourists’ emotional responses and overall evaluation of the entire 
tourism process (products, services, experiences), triggered by positive disconfirmation, directly driving positive 
behaviors such as repurchasing and recommending, and holding core value in measuring industry success in 
scenarios such as wellness tourism. In wellness tourism, tourist satisfaction is even a key indicator for measuring 
the success and sustainability of destinations, directly related to sustained profit growth (Liu et al., 2023), 
highlighting its practical value in industry operations. Previous studies have conducted multi-dimensional 
explorations on the influencing factors, mechanism of action, and characteristics of wellness tourism scenarios 
related to tourist satisfaction, confirming that satisfaction serves as a core indicator of success in wellness tourism, 
directly linked to tourists' willingness to revisit and recommend destinations (Libre et al., 2022; Seow et al., 2024; 
Torabi et al., 2022) . 
 

2.4. Experience Quality 
Pine and Gilmore (1998) introduced the conceptual model of experience economy. It outlines various 

experience categories, including aesthetic, entertaining, educational, and escape experiences (Lee et al., 2020; 
Mehmetoglu & Engen, 2011; Pine & Gilmore, 1998, 2013). It help to measures how well a place has been 
experienced overall over a predetermined amount of time (Lemke et al., 2011) and the way visitors engage with a 
destination's landscape (Moon & Han, 2018).  

Experiences accompany the psychological process of a person’s thoughts and feelings under the influence of an 
environment. In this light, the tourist experience is formed via the process of internalizing interactions at a 
destination, creating responses. Thus, Moon & Han (2019) considers the tourists’ tour experiences as their overall 
encounters at an destination, and the outcomes as tourists’ subjective responses to the tour experiences at the 
destination (Moon & Han, 2019). 
 

2.5. Perceived Wellness Value  
Perceived value arises from a relative comparison between the sacrifices customers make and the benefits 

gained from consumed products or services. As a multifaceted concept, it includes functional value, social value, 
epistemic value, and a sense of well-being. The conceptual foundation of perceived value lies in equity theory, 
which posits this as the proportion between the provider’s outcomes and the consumer’s inputs (Suhartanto et al., 
2020).  

In the tourism industry, perceived value refers to the visitor's overall assessment of the place based on the 
advantages they receive through travelling (Damanik, 2022). It is subjective and influenced by individual 
preferences, expectations, and experiences. When it comes to wellness tourism, Perceived wellness value is the 
overall assessment by tourists of the benefits and worth of wellness-related services and experiences they receive. 



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Perceived benefits are those that result from adopting preventative measures to lessen the severity or susceptibility 
of a certain disease (Ban & Kim, 2020; Rosenstock, 1974; Rosenstock et al., 1988). Wellness tourists weigh both the 
costs incurred and the expected outcomes of their experiences. Thus, perceived wellness value reflects their 
assessment of how much their tourism activities will positively affect mental and physical health. When tourists 
perceive greater value in wellness tourism activities that enhance or maintain well-being, this leads to higher 
satisfaction levels (Seow et al., 2024). 
 

2.6. Research Framework and Hypothesis Development 
Based on the Expectation-Confirmation Theory, this study explores the factors influencing the satisfaction and 

subsequent behavioral intentions of wellness tourists in Guangxi, and constructs a research framework involving 
tourist experience quality, perceived wellness value, tourist satisfaction, and post-travel behavioral intentions of 
tourists. Within this framework, the study hypothesizes that tourist experience quality, perceived wellness value, 
and tourist satisfaction are identified as three direct affective factors affecting tourists' post-consumption behavioral 
intentions; meanwhile, tourist experience quality and perceived wellness value are two direct factors influencing 
tourist satisfaction and tourists' post-travel behavioral intentions. Additionally, tourist satisfaction also plays a 
mediating role between tourist experience quality, perceived wellness value, and tourists' post-consumption 
behavioral intentions.  

Based on relevant theories and existing research findings, this study proposes the following research 
framework and research hypotheses, as specifically illustrated in Figure 1. 
 

 
Figure 1. Research Framework and Hypotheses. 

 
The research model shows that this study has seven research hypotheses in total, which are as follows: 

H1: There is a significant relationship between experience quality and wellness tourists’ behavioral intention. 
H2: There is a significant relationship between perceived wellness value and wellness tourists’ behavioral intention. 
H3: There is a significant relationship between tourists’ satisfaction and wellness tourists’ behavioral intention. 
H4: There is a significant relationship between experience quality and wellness tourists’ satisfaction. 
H5: There is a significant relationship between perceived wellness value and wellness tourists’ satisfaction. 
H6: Tourists’ satisfaction mediates the relationship between experience quality and wellness tourists’ behavioral intention. 
H7: Tourists’ satisfaction mediates the relationship between perceived wellness value and wellness tourists’ behavioral 

intention. 
 

3. Methodology 
3.1. Research Design 

This study is quantitative research that adopts the cross-sectional method, aiming to explore the relationship 
between dependent variables and independent variables, and analyze the mediating effect therein. Data from 
respondents were collected through a self-administered structured questionnaire, which was specifically divided 
into two categories: one based on the demographic characteristics of the respondents; the other derived from the 
respondents' answers to the structured questions in the questionnaire. These data were used to test the research 
hypotheses, confirm the relationships between variables, and identify the factors influencing tourists' post-travel 
behavioral intentions. This study employed a seven-point Likert scale for measurement, with the scoring range 
being: 1 = Strongly Disagree, 2 = Somewhat Disagree, 3 = Disagree, 4 = Neutral, 5 = Agree, 6 = Somewhat 
Agree, and 7 = Strongly Agree. The data collection period lasted approximately one month. Table 1 presents the 
measurement methods of the variables used in this study. 
 

3.2. Research Sampling 
Guangxi is home to numerous wellness tourism destinations, including wellness resorts and centers, 

distributed across the province. However, due to constraints in research funding and time, a sampling approach 
was adopted to select geographically and demographically representative samples for the study. Specifically, 
purposive sampling was employed to select the target group samples for this research. 

Since the majority of tourists in Guangxi are domestic tourists from within China, the questionnaire for this 
study was developed in Chinese. The questionnaire was translated by a professional translation company to ensure 
the accuracy and rigor of the Chinese version. Tourists visiting Guangxi have diverse travel purposes; for instance, 
some come merely for urban sightseeing. To ensure that the questionnaire respondents are tourists with health as 
their primary travel purpose, the questionnaire was only distributed to those who have visited wellness resorts or 
wellness centers in Guangxi within the past three years. Meanwhile, before respondents filled out the 
questionnaire, researchers explained the purpose of the questionnaire and interpreted the definitions of key terms 



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listed in it to ensure that respondents had a full understanding. This measure was intended to guarantee the 
validity of the collected data. G*Power is configured for a multiple regression with 7 predictors in order to 
calculate the appropriate sample size. The test employed a medium effect size of (f2 = 0.15), a power of 0.80, and an 
alpha of 0.05. Since most social science studies estimate 80 percent to be the minimum acceptable power (Gefen, 
Rigdon, & Straub, 2011). G*Power calculations led to a required sample size of 103. 
 

3.3. Data Analysis Method 
The information gathered from completed surveys underwent several analytical procedures. First, data 

preparation was conducted. Second, descriptive analysis was performed. Third, both the measurement model and 
the structural model were analyzed. Data analysis for hypothesis testing is conducted in alignment with the 
research questions formulated. All collected information and data must be sufficiently robust to enable proper 
analysis, thereby ensuring the generation of valid results and conclusions. For the present study, the Statistical 
Package for the Social Sciences (SPSS) software was utilized for data preparation and descriptive analysis. 
Additionally, Structural Equation Modeling (SEM) and Partial Least Squares (PLS) were employed for data 
analysis and processing. The present study conducts its data analysis for measurement model and the structural 
model by using Smart-PLS version 4.0, following the guidelines and procedures outlined by Hair et al. (2023). The 
analysis begins with an assessment of the measurement model, which examines the reliability and validity of the 
measurement items. Subsequent to this, the structural model is evaluated to determine the nature of the 
relationships between the latent variables as hypothesized in the conceptual model. 
 

4. Results  
This study collected a total of 138 questionnaires. Fourteen questionnaires where respondents selected the 

same score for all questions were removed. These questionnaires reflected situations where respondents did not 
fully understand the content or answered randomly, which would affect the reliability and validity of data analysis. 
After excluding such questionnaires, the study finally retained 124 valid questionnaires, with a questionnaire 
validity rate of 89.86%. 
 

4.1. Demographic Characteristics 
  

Table 1. Demographic Characteristics of Respondents. 

Variable Demographic Frequency Percentage (%) 

Gender female 50 40.32 
 

male 74 59.68 

Age 18 under 2 1.60 
 

18-30 10 8.10 
 

31-40 13 10.50 
 

41-50 84 67.74 
 

51-60 11 8.87 
 

60above 4 3.20 

Degree Junior High School 1 0.80 
 

High School 13 10.50 
 

Vocational College 10 8.10 
 

Undergraduate 90 72.60 
 

Masters and above 7 5.60 
 

Others 3 2.40 

Occupation Public Servant 3 2.40 
 

Company Employee 87 70.20 
 

Self-employed 2 1.60 
 

freelancers 2 1.60 
 

Military Personnel 4 3.20 
 

Homemaker 4 3.20 
 

Student 3 2.40 
 

Teacher 3 2.40 
 

Retired 12 9.70 
 

Others 4 3.20 

Income 30,001 or less 8 6.50 
 

30,001-50,000 15 12.10 
 

50,001 -70,000 88 71.00 
 

70,001-90,000 3 2.40 
 

90,001-110,000 1 0.80 
 

110,001 and above 9 7.30 
 

Total 124 100 

 

4.2. Measurement Model 
Table 2 and table 3 present the results of indicator reliability, internal consistency reliability, convergent 

validity, and discriminant validity for the first-order and second-order constructs. 



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For convergent validity which evaluates the degree of agreement among indicators measuring the same 
variable, the AVE values for all variables ranged from 0.524 to 0.728. All values exceeded the minimum threshold 
of 0.5, demonstrating that the model meets the criteria for convergent validity. 
 

Table 2. Measurement Model for the First Order Constructs. 

First Order  
Construct 

Items Outer Loading Cronbach's 
Alpha 

Composite 
Reliability 

AVE 

EQ AES1 0.894  0.929  0.933  0.524   
AES2 0.912  

   
 

AES3 0.893  
   

 
AMU1 0.889  

   

 
AMU2 0.877  

   

 
AMU3 0.880  

   

 
EDU1 0.829  

   

 
EDU2 0.886  

   

 
EDU3 0.862  

   

 
EDU4 0.827  

   

 
ESC1 0.807  

   

 
ESC2 0.770  

   

 
ESC3 0.836  

   

 
ESC4 0.732  

   

PWV PWV1 0.695  0.905  0.910  0.639  
 

PWV2 0.758  
   

 
PWV3 0.868  

   

 
PWV4 0.827  

   

 
PWV5 0.808  

   

 
PWV6 0.827  

   

 
PWV7 0.801  

   

TS TS1 0.789  0.869  0.874  0.720  
 

TS2 0.867  
   

 
TS3 0.818  

   

 
TS4 0.914  

   

BI BI1 0.781  0.906  0.906  0.681  
 

BI2 0.829  
   

 
BI3 0.830  

   

 
BI4 0.844  

   

 
BI5 0.842  

   

 
BI6 0.823  

   

Note: EQ: Experience Quality PWV: Perceived Wellness Value TS: Tourists’ Satisfaction  
BI: Behavioral Intention 

 
Indicator reliability was assessed by examining the factor loadings of each item on its corresponding variable. 

For all first-order and second-order constructs in this study, the factor loadings of all items exceeded the 
conventional threshold of 0.7, ranging from 0.732 to 0.914, with the exception of item PWV1, which had an outer 
loading of 0.695. This deviation was deemed acceptable, however, as the Average Variance Extracted (AVE) value 
for the construct PWV1 exceeded the critical threshold of 0.5. 
 

Table 3. Measurement Model for the Second Order Constructs. 

Second Order 
Construct 

Items Outer Loading Cronbach's 
Alpha 

Composite 
Reliability 

AVE 

EQ AES 0.805 0.929 0.915 0.728  
AMU 0.894 

   
 

EDU 0.855 
   

 
ESC 0.857 

   

Note: EQ: Experience Quality AES: Aesthetic AMU: Amusement EDU: Education ESC: Escape 

 
Table 4. Discriminant Validity Assessment. 

Construct AES AMU BI EDU ESC PWV TS BI 

EQ 
    

(0.777) 0.777 0.739 0.764 
AES (0.767) 

       

AMU 0.767 (0.828) 
      

BI 0.655 0.763 (0.893) 
     

EDU 0.599 0.795 0.633 (0.743) 
    

ESC 0.679 0.828 0.671 0.743 (0.714) 
   

PWV 0.680 0.714 0.793 0.652 0.714 (0.795) 
  

TS 0.559 0.728 0.893 0.683 0.645 0.795 
  

Note: EQ: Experience Quality PWV: Perceived Wellness Value TS: Tourists’ Satisfaction 
BI: Behavioral Intention AES: Aesthetic AMU: Amusement EDU: Education ESC: Escape 

 



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Regarding internal consistency reliability, the Composite Reliability (CR) and Cronbach’s alpha values for each 
variable were all high, ranging from 0.869 to 0.933. Both metrics surpassed the minimum acceptable threshold of 
0.70, confirming that the model exhibits good internal consistency and reliability. 

The final step in assessing the measurement model was to test discriminant validity, which was evaluated using 
the Heterotrait-Monotrait (HTMT) ratio. Discriminant validity measures the distinctiveness between different 
variables, and an HTMT ratio below 0.90 is generally considered acceptable (F.Hair et al., 2023). In this study, the 
HTMT ratios ranged from 0.559 to 0.893, indicating that the items measuring different constructs are sufficiently 
distinct and that the model has established discriminant validity. 
 

4.3. Structural Model 
Variance Internal Factor (VIF) measurement showed that there is no potential collinearity problem in this 

study because all the variables have a VIF value lower than 3 (Hair et al., 2023).  
Effect size analysis (f2) is a method to measure whether there is a substantive impact of a particular exogenous 

variable on an endogenous variable. Cohen (1988) has set up the range value of the impact of f2 as 0.02 as a small 
effect, 0.15 as a medium and 0.35 as a large effect at the structural level. As shown in table 5, experience quality and 
perceived wellness value have a medium effect size on behavioral intention, while tourist’s satisfaction has a large 
effect size on behavioral intention. Both experience quality and perceived wellness value have large effect size on 
tourist’s satisfaction. 
 

Table 5. Structural Model Assessment.  
Endogenous variable 

 

 
Exogenous variable 

BI TS 
 

f2 VIF f2 VIF R2 

EQ 0.078  2.295  0.124  2.041  
 

PWV 0.057  2.540  0.245  2.041  
 

TS 0.365  2.262  
  

0.558 

BI 
    

0.705 

Note: EQ: Experience Quality PWV: Perceived Wellness Value TS: Tourists’ Satisfaction  
BI: Behavioral Intention 

 
The Coefficient of Determination (R2) was used to measure the goodness of fit of the model. The R2 for the 

tourist’s satisfaction was 0.558, meaning that 55.8 percent of the variance in the tourist’s satisfaction can be 
explained by experience quality and perceived wellness value. The R2 for the behavioral intention was 0.705 after 
the mediating effect of tourist’s satisfaction, meaning that 70.5 percent of the variance in the behavioral intention 
can be explained. As there are a various set of rules on the acceptable R2, this study follows the guideline by Chin 
(1998). R2 values of 0.67, 0.33 and 0.19 are considered as substantial, moderate and weak (Chin, 1998).. In this 
study. The R2 for tourists’ satisfaction (0.558) and behavioral intention (0.705) means the model have a moderate 
explanatory power for tourists’ satisfaction and a substantial explanatory power for behavioral intention, which 
meet the requirement in the social science research. 
 

Table 6. Summary of Hypothesis Results. 

Hypothesis Path Coefficient STDEV T statistics P values Decision 

H1: EQ -> BI 0.231 0.085 2.729 0.006 Supported 
H2: PWV -> BI 0.206 0.096 2.136 0.033 Supported 
H3: TS -> BI 0.494 0.095 5.220 <0.000 Supported 
H4: EQ -> TS 0.335 0.089 3.763 <0.000 Supported 
H5: PWV -> TS 0.470 0.096 4.893 <0.000 Supported 
H6: EQ -> TS -> BI 0.165 0.060 2.776 0.006 Supported 
H7: PWV -> TS -> BI 0.232 0.060 3.862 <0.000 Supported 

Note: EQ: Experience Quality PWV: Perceived Wellness Value TS: Tourists’ Satisfaction  
BI: Behavioral Intention 

 
Path analysis was used to examine the developed hypotheses and bootstrapping analysis was used to validate 

the theoretical model that was developed using smart PLS. Based on table 6, experience quality (H1: t-values = 
2.729, p < 0.01), perceived wellness value (H2: t-values = 2.136, p < 0.05) and tourists’ satisfaction (H3: t-values = 
5.220, p < 0.000) showed significant direct positive relationship with behavioral intention. Tourists’ satisfaction 
played the most important role in tourists’ post-travel behavioral intention. Perceived wellness value (H5: t-values 
= 4.893, p < 0.000) played the more important role in tourists’ satisfaction than experience quality (H4: t-values = 
3.763, p < 0.000). There are significant mediating effects of tourists’ satisfaction on experience quality (H6: t-values 
= 2.776, p < 0.01), perceived wellness value (H7: t-values = 3.862, p < 0.000) and tourists’ post-travel behavioral 
intention (H6 to H7). 
 



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Figure 2. The Bootstrapping Algorithm Results. 

 

5. Discussion and Conclusion 
The findings of this study provide strong empirical support for the proposed relationships between experience 

quality, perceived wellness value, tourist satisfaction, and behavioral intention among wellness tourists in Guangxi. 
Path analysis using Smart PLS revealed that all hypothesized relationships were statistically significant, 
demonstrating the robustness of the theoretical model. 

First, the results confirmed that experience quality (H1) and perceived wellness value (H2) significantly and 
positively influence tourists’ behavioral intention. This finding aligns with previous studies emphasizing that high-
quality tourism experiences and perceived value are critical predictors of tourists’ future behavioral tendencies, 
such as revisiting and recommending a destination (Libre et al., 2022; Seow et al., 2024; Torabi et al., 2022). 
Wellness tourists, in particular, tend to seek authentic, health-enhancing, and emotionally enriching experiences. 
When their sensory and psychological needs are met through high experience quality, they are more likely to form 
positive behavioral intentions. 

Second, tourists’ satisfaction (H3) showed the strongest direct effect on behavioral intention (t = 5.220, p < 
0.000), suggesting that satisfaction is the most influential factor in determining tourists’ post-travel behavior. This 
finding is consistent with the Expectation-Confirmation Theory (ECT) and Theory of Planned Behavior (TPB), 
which posit that satisfaction acts as a central determinant of future behavioral intention. Satisfied wellness tourists 
are more likely to revisit the destination, engage in word-of-mouth promotion, and recommend the wellness 
experience to others. This emphasizes that wellness tourism managers in Guangxi should prioritize maintaining 
high satisfaction levels through superior service quality, personalized care, and emotional engagement. 

Third, the results also revealed that perceived wellness value (H5) has a stronger effect on tourists’ satisfaction 
than experience quality (H4). This indicates that tourists’ overall satisfaction stems not only from tangible service 
quality but also from the perceived value they derive in terms of physical rejuvenation, mental relaxation, and 
emotional well-being. In the context of wellness tourism, value perceptions—such as the feeling of improved 
health, inner peace, and self-restoration—are more powerful in shaping satisfaction than mere sensory or aesthetic 
experiences. This highlights the necessity for wellness destinations to enhance perceived wellness value by 
integrating authentic, culturally distinctive, and holistic wellness programs. 

Moreover, the mediating role of tourists’ satisfaction (H6 and H7) was confirmed between both experience 
quality and perceived wellness value on behavioral intention. The mediation results suggest that while experience 
quality and wellness value have direct impacts on behavioral intention, their influence is largely transmitted 
through satisfaction. This partial mediation implies that satisfaction serves as a psychological bridge connecting 
tourists’ experiences and their behavioral outcomes. When tourists perceive the experience as enjoyable and 
beneficial to their health, satisfaction increases, thereby strengthening their intention to revisit or recommend the 
destination. 

Overall, the model demonstrates that enhancing tourists’ satisfaction, perceived value and experience quality is 
crucial for stimulating long-term loyalty among wellness tourists. For Guangxi’s wellness tourism industry, this 
means focusing on continuous quality improvement, emotional engagement, and personalized service design to 
sustain visitor retention and positive word-of-mouth. 
 

6. Future Research Suggestions 

Future studies could adopt longitudinal or experimental approaches to track changes in satisfaction and 
behavioral intention over time, providing stronger evidence of causality. Additional constructs such as destination 
trust, emotional attachment, or spiritual well-being could be integrated into the model to provide a more holistic 
understanding of wellness tourists’ behavioral patterns. 
 

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https://doi.org/10.1016/0749-5978(91)90020-T 

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https://doi.org/10.3390/ijerph17103646
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https://doi.org/10.1080/13683500.2021.1886256

