20 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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 Asian Business Research Journal, 2025, 10(10): 20-27 21 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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. Asian Business Research Journal, 2025, 10(10): 20-27 22 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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 Asian Business Research Journal, 2025, 10(10): 20-27 23 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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. Asian Business Research Journal, 2025, 10(10): 20-27 24 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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 Asian Business Research Journal, 2025, 10(10): 20-27 25 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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). Asian Business Research Journal, 2025, 10(10): 20-27 26 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA 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. References Ã, T. L., & Hsu, C. H. C. (2006). Predicting behavioral intention of choosing a travel destination. Tourism Management, 27(4), 589–599. https://doi.org/10.1016/j.tourman.2005.02.003 Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. https://doi.org/10.1016/0749-5978(91)90020-T https://doi.org/10.1016/j.tourman.2005.02.003 Asian Business Research Journal, 2025, 10(10): 20-27 27 © 2025 by the authors; licensee Eastern Centre of Science and Education, USA Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological Bulletin, 82(2), 261–277. https://doi.org/10.1037/h0076477 Ban, H. J., & Kim, H. S. (2020). Applying the modified health belief model (HBM) to Korean medical tourism. International Journal of Environmental Research and Public Health, 17(10), 3646. https://doi.org/10.3390/ijerph17103646 Basil Chibuike, N., Ireneus Chukwudi, N., & Rahim Ajao, G. (2021). Antecedents of tourists’ behavioural intentions: Perspectives of expectations confirmation model—A study of select tourism sites in South-East Nigeria. International Journal of Education, Culture and Society, 6(5), 176–182. https://doi.org/10.11648/j.ijecs.20210605.13 C. C., S., & Prathap, S. K. (2020). Continuance adoption of mobile-based payments in COVID-19 context: An integrated framework of health belief model and expectation confirmation model. International Journal of Pervasive Computing and Communications, 16(4), 351–369. https://doi.org/10.1108/IJPCC-06-2020-0069 Chelliah, S., Khan, M. J., & Atabakhshi Kashi, A. B. (2021). Antecedents of perceived beneficial destination image: A study on Middle-Eastern medical tourists visiting Iran. International Journal of Pharmaceutical and Healthcare Marketing, 15(1), 43–63. https://doi.org/10.1108/IJPHM-07-2018-0039 Chin, W. W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Lawrence Erlbaum Associates. Chiu, W., Cho, H., & Chi, C. G. (2020). Consumers’ continuance intention to use fitness and health apps: An integration of the expectation– confirmation model and investment model. Information Technology & People, 34(3), 978–998. https://doi.org/10.1108/ITP-09- 2019-0463 Damanik, J. (2022). Effects of perceived value, expectation, visitor management, and visitor satisfaction on revisit intention to Borobudur Temple, Indonesia. Journal of Heritage Tourism, 17(3), 1–16. https://doi.org/10.1080/1743873X.2021.1950164 Hair, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2023). A primer on partial least squares structural equation modeling (PLS-SEM) (4th ed.). SAGE Publications, Inc. Glendon, I. (1998). Application of leisure motivation scale to tourism. Annals of Tourism Research, 25(1), 169–184. https://doi.org/10.1016/S0160-7383(97)00066-X Guangxi Daily. (2023, November 1). Guangxi Zhuang Autonomous Region People’s Government: Accelerating investment attraction, cultivating diversified forms of industry, and promoting industrial integration: Guangxi presses the “fast-forward button” for the development of recreation industry. Guangxi Daily. http://www.gxzf.gov.cn/gxyw/t16961193.shtml Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckmann (Eds.), Action control: From cognition to behavior (pp. 11–39). Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-69746-3_2 Jeong, Y., Kim, S. K., & Yu, J. G. (2019). Determinants of behavioral intentions in the context of sport tourism with the aim of sustaining sporting destinations. Sustainability, 11(11), 3073. https://doi.org/10.3390/su11113073 Joo, Y., Seok, H., & Nam, Y. (2020). The moderating effect of social media use on sustainable rural tourism: A theory of planned behavior model. Sustainability, 12(10), 4095. https://doi.org/10.3390/su12104095 Lee, S., Jeong, E., & Qu, K. (2020). Exploring theme park visitors’ experience on satisfaction and revisit intention: A utilization of experience economy model. Journal of Quality Assurance in Hospitality & Tourism, 21(6), 1–24. https://doi.org/10.1080/1528008X.2019.1691702 Lemke, F., Clark, M., & Wilson, H. (2011). Customer experience quality: An exploration in business and consumer contexts using repertory grid technique. Journal of the Academy of Marketing Science, 39(6), 846–869. https://doi.org/10.1007/s11747-010-0219-0 Libre, A., Manalo, A., & Laksito, G. S. (2022). Factors influencing Philippine tourists’ revisit intention: The role and effect of destination image, tourist experience, perceived value, and tourist satisfaction. International Journal of Quantitative Research and Modeling, 3(1), 1–12. https://doi.org/10.46336/ijqrm.v3i1.260 Liu, L., Zhou, Y., & Sun, X. (2023). The impact of the wellness tourism experience on tourist well-being: The mediating role of tourist satisfaction. Sustainability, 15(3), 1872. https://doi.org/10.3390/su15031872 Mehmetoglu, M., & Engen, M. (2011). Pine and Gilmore’s concept of experience economy and its dimensions: An empirical examination in tourism. Journal of Quality Assurance in Hospitality & Tourism, 12(4), 237–255. https://doi.org/10.1080/1528008X.2011.541847 Moon, H., & Han, H. (2018). Destination attributes influencing Chinese travelers’ perceptions of experience quality and intentions for island tourism: A case of Jeju Island. Tourism Management Perspectives, 28(April), 71–82. https://doi.org/10.1016/j.tmp.2018.08.002 Moon, H., & Han, H. (2019). Tourist experience quality and loyalty to an island destination: The moderating impact of destination image. Journal of Travel & Tourism Marketing, 36(1), 43–59. https://doi.org/10.1080/10548408.2018.1494083 Oliver, R. L. (1980). A cognitive model of the antecedents and consequences of satisfaction decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.2307/3150499 Oliver, R. L., & Swan, J. E. (1989). Equity and disconfirmation perceptions as influences on merchant and product satisfaction. Journal of Consumer Research, 16(3), 372–383. https://doi.org/10.1086/209223 Pine, B. J., & Gilmore, J. H. (1998). Welcome to the experience economy. Harvard Business Review, 76(4), 97–105. Pine, B. J., & Gilmore, J. H. (2013). The experience economy: Past, present, and future. In J. Sundbo & F. Sørensen (Eds.), Handbook on the experience economy (pp. 21–44). Edward Elgar Publishing. https://doi.org/10.4337/9781781004227.00007 Rosenstock, I. M. (1974). Historical origins of the health belief model. Health Education Monographs, 2(4), 328–335. https://doi.org/10.1177/109019817400200403 Rosenstock, I. M., Strecher, V. J., & Becker, M. H. (1988). Social learning theory and the health belief model. Health Education Quarterly, 15(2), 175–183. https://doi.org/10.1177/109019818801500203 Seow, A. N., Foroughi, B., & Choong, Y. O. (2024). Tourists’ satisfaction, experience, and revisit intention for wellness tourism: E-word-of- mouth as the mediator. SAGE Open, 14(3), 1–16. https://doi.org/10.1177/21582440241274049 Siddiqui, S., & Hamid, S. (2023). Applying the extended theory of planned behaviour (TPB) to predict behavioural intention in the context of transformational tourism: A case of Nizamuddin Shrine. International Journal of Religious Tourism and Pilgrimage, 11(2), 24–40. https://arrow.tudublin.ie/ijrtp/vol11/iss2/4 Suhartanto, D., Brien, A., Primiana, I., Wibisono, N., & Triyuni, N. N. (2020). Tourist loyalty in creative tourism: The role of experience quality, value, satisfaction, and motivation. Current Issues in Tourism, 23(7), 867–879. https://doi.org/10.1080/13683500.2019.1568400 Torabi, Z., Shalbafian, A. A., Allam, Z., Ghaderi, Z., Murgante, B., & Khavarian-Garmsir, A. R. (2022). Enhancing memorable experiences, tourist satisfaction, and revisit intention through smart tourism technologies. Sustainability, 14(3), 1–18. https://doi.org/10.3390/su14031212 Zeng, L., & Li, R. Y. M. (2021). Tourist satisfaction, willingness to revisit and recommend, and mountain Kangyang tourism spots sustainability: A structural equation modelling approach. Sustainability, 13(19), 10620. https://doi.org/10.3390/su131910620 Zhao, J., & An, Y. (2021). Behavioural intention of forest therapy tourism in China: Based on health belief model and the theory of planned behaviour. Current Issues in Tourism, 25(11), 1–8. https://doi.org/10.1080/13683500.2021.1886256 https://doi.org/10.1037/h0076477 https://doi.org/10.3390/ijerph17103646 https://doi.org/10.11648/j.ijecs.20210605.13 https://doi.org/10.1108/IJPCC-06-2020-0069 https://doi.org/10.1108/IJPHM-07-2018-0039 https://doi.org/10.1108/ITP-09-2019-0463 https://doi.org/10.1108/ITP-09-2019-0463 https://doi.org/10.1080/1743873X.2021.1950164 http://www.gxzf.gov.cn/gxyw/t16961193.shtml https://doi.org/10.3390/su11113073 https://doi.org/10.1080/1528008X.2019.1691702 https://doi.org/10.1007/s11747-010-0219-0 https://doi.org/10.46336/ijqrm.v3i1.260 https://doi.org/10.3390/su15031872 https://doi.org/10.1080/1528008X.2011.541847 https://doi.org/10.1016/j.tmp.2018.08.002 https://doi.org/10.1080/10548408.2018.1494083 https://doi.org/10.2307/3150499 https://doi.org/10.4337/9781781004227.00007 https://doi.org/10.1177/21582440241274049 https://doi.org/10.1080/13683500.2019.1568400 https://doi.org/10.3390/su131910620 https://doi.org/10.1080/13683500.2021.1886256