Priority Decision-Making for the Regeneration of Traditional Village Landscapes Based on the IPA–EWM Method

Abstract

This study focuses on the traditional village landscape of the Yuan River Basin in Hunan Province, aiming to break through the limitations of subjectivity and one-sidedness in existing evaluation methods by coupling the importance-performance analysis (IPA) with the entropy weight method (EWM) to construct a comprehensive evaluation framework for tourists' satisfaction. This study provides an analysis framework that is both scientific and applicable. Based on 569 valid questionnaire data, the study first applies the EWM method to objectively weight 21 landscape factors and then uses the IPA method to analyze the perceived importance and satisfaction of tourists, thereby identifying the optimization level of each factor. The results show that cultural elements account for the highest proportion of the overall weight. Specifically, the continuity of intangible cultural heritage, the adequacy of recreational facilities, and the village popularity and reputation which are classified into the "key improvement area" with IPA indices of 30.957, 27.174, and 20.141, respectively, revealing a significant gap between the current tourist experience and expectations. This study not only verifies the adaptability and sensitivity of the IPA–EWM method in the satisfaction evaluation of traditional village landscapes, but also provides strong support and decision-making basis for the protection of traditional village landscapes, the optimization of tourism services, and the dynamic inheritance of culture in China. Compared with the previous studies that only use IPA or subjective weighting methods, this method has stronger explanatory power of the results and shows important theoretical innovation and practice guidance in the context of highquality development of rural cultural tourism.

Graphical abstract

Keywords

IPA–EWM / Yuan River Basin Traditional Village / Tourist Satisfaction / Landscape Planning and Design / Priority Decision

Highlight

	· Integrates IPA and EWM to assess tourists' satisfaction with traditional village landscapes
	· Reveals intangible cultural heritage and village reputation as key tourists' satisfaction drivers
	· Proposes practical strategies to enhance traditional village landscape construction and rural tourism development

1 Background

Traditional villages are irreplaceable cultural heritage assets that carry both historical significance and contemporary value^[1]. They are not only the physical carriers of local culture transmission but also vividly reflect the historical evolution and cultural characteristics of specific regions through their unique architectural styles, settlement patterns, and natural landscapes. Against the backdrop of rapid rural tourism development, traditional villages have become core resources for attracting tourists^[2]. Through the integration of culture and tourism, such villages can not only stimulate local economy but also effectively optimize the employment structure and increase residents' incomes, thereby enhancing socio-cultural identity and promoting sustainable development^[3]. However, with the continuous development of rural tourism, tourists' expectations and demands for traditional village landscapes are constantly changing. Accurately evaluating tourist satisfaction, identifying key influencing factors, and proposing targeted priority decisions for landscape optimization have become urgent issues to be addressed.

Importance–performance analysis (IPA) is a strategic analytical tool proposed by John A. Martilla and John C. James in 1977^[4]. It is primarily used to analyze the perceived importance and actual performance of various attributes of a product or service, assisting enterprises in identifying areas requiring priority improvement and subsequently formulating optimization strategies. This method has not only been widely applied in fields such as healthcare^[5], catering^[6], accommodation^[7–^8], and tourism^[9] but has also played a key role in tourist satisfaction research. The IPA method typically uses questionnaires to collect tourists' satisfaction evaluation data, requiring respondents to evaluate relevant factors from importance and satisfaction of their tourist destinations. Importance usually reflects the degree of tourists' expectations or needs for a certain factor, while satisfaction reflects tourists' feelings or evaluations of that factor during their actual experience. Researchers construct an IPA matrix based on the mean values of importance and satisfaction for each factor, visually displaying the relationship between them across four quadrants.

In studies on priority decision-making for landscape regeneration in traditional villages, international scholars have mainly focused on the gap between visitor expectations and actual perceptions, as well as on identifying key attributes requiring improvement, providing theoretical and methodological support for applying IPA to explore satisfaction enhancement and resource optimization. For example, Abraham Pizam et al.^[10] pointed out that satisfaction is moderated by tourist expectations, while Metin Kozak^[11] and James Wong et al.^[12] verified the effectiveness of the IPA method in identifying critical attributes for improvement and optimizing resource allocation in both cross-cultural and urban tourism contexts. Ernest Azzopardi et al.^[13] conducted a systematic theoretical evaluation of the IPA method and proposed the potential of integrating multi-model application methods to improve the accuracy of identifying upgrading priorities. In China, scholars have extensively applied IPA in strategy research for the landscape upgrading of cultural heritage sites such as villages and old towns, and have gradually introduced models such as Long Short-Term Memory (LSTM), Kano Model (Kano), and Structural Equation Modeling (SEM)^[14–^20] to achieve scientific ranking and optimization of reconstrution schemes. These studies have assessed not only the reconstrution priorities of fundamental elements, including physical space and service facilities in traditional village cultural landscapes^[21–^26], but also explored the coupling relationships between development factors such as tourism exploitation and visitors' cultural identity, nostalgia, and landscape perception^[27–^29]. For instance, Wenbin Luo et al.^[23] identified the specific shortcomings focused on by tourists in rural tourism landscape quality evaluation through IPA analysis, and formulated reconstrution strategies combined with actual conditions.

However, two limitations remain common in existing research. First, although the IPA method can intuitively and efficiently present differences in visitors' subjective perceptions, its weighting process is vulnerable to subjective bias interference. Second, most studies are primarily based on qualitative analysis and lack a systematic quantitative weighting mechanism, making it difficult to fully reflect the structure of visitor demands and the relative importance of landscape factors^[13,^19]. For example, Pei Zhang et al.^[30] identified key improvement areas in the traditional villages of Bailuyuan using IPA, but failed to effectively address the subjective bias in importance weighting. Si Liu et al.^[31] introduced an AHP– IPA model in a waterfront-space study and built a systematic indicator system, yet the results were still limited by expert experience, particularly lacking in the refinement of cultural dimensions. Yunning Zhang et al.^[25] emphasized the significant effect of intangible cultural experience on tourist satisfaction in the Anyi Ancient Village Cluster, but focused on a single factor rather than systematically integrating the cognitive structure across multiple landscape elements. To address these shortcomings, the Entropy Weight Method (EWM), as an objective weighting tool, has been introduced into landscape and environmental studies. By calculating indicator weights through information entropy, EWM can effectively reduce the interference of human bias^[32]. EWM has been widely applied in green space system evaluation^[33], water environment analysis^[34–^35], visual landscape assessment^[36], and ecological conservation and sustainable development^[37–^38], demonstrating strong applicability and scientific rigor in multicriteria decision-making contexts.

In summary, the coupling of IPA and EWM can strengthen the logical completeness of satisfaction research and provide a efficient pathway for the precise identification of weak links in traditional village landscapes. Accordingly, this study aims to systematically analyze tourists' perceived importance and actual experience satisfaction regarding various landscape elements of traditional villages in the Yuan River Basin of Hunan Province using the IPA-EWM method. From four aspects—aesthetics, ecology, society, and culture—it identifies landscape elements in urgent need of upgrading, constructs a multi-dimensional and multi-level evaluation system, and proposes priority decisions for landscape improvement, providing a scientific and feasible decision-making reference for traditional village landscape protection and rural tourism development.

2 Study Area and Data Sources

2.1 Study Area

The Yuan River, also known as the Yuanshui, is an important tributary of the Dongting Lake system, flowing through Guizhou and Hunan Provinces. In Hunan, the river extends for 568 km with a basin area of 51,066 km², accounting for 24.11% of the province's total land area^[39]. The development of traditional villages in this basin can be traced back to the Southern Song Dynasty. These settlements are characterized by clusters of timber buildings and a distinctive spatial pattern of "adapting to the terrain" shaped by rivers and surrounding mountains. Infrastructure such as roads and bridges also preserve traditional construction techniques, reflecting the synergy of nature and humans. As a typical gathering area of traditional villages in southern China, the Yuan River Basin is known for the continuity and long historical accumulation of its cultural heritage. A total of 412 villages in the basin have been included in the Chinese Traditional Villages List, accounting for 58.5% of the total traditional villages in Hunan. These villages completely preserve cultural heritages such as ancient academies, ancestral halls, and wharves. Meanwhile, the village landscape system has the composite characteristics of ecological conservation, agricultural production, and residential functions, forming a multi-scale "mountain–water–field–residence" cultural landscape. Furthermore, as a rural tourism hotspot in Hunan, this basin is facing multiple challenges such as the continuation of traditional culture, infrastructure upgrades, and tourist experience optimization. The contradiction between its protection and development provides a typical sample for tourist satisfaction research.

2.2 Data Sources

The sample villages comprise eight villages: Shibadong, Zhumu, Niuxi, Jinyuan, Shanbei, Chongmudang, Suoyixi, and Gaoping, covering the main landscape types in the upper, middle, and lower reaches of the Yuan River Basin. To improve the seasonal adaptability and sample representativeness of the satisfaction assessment, this study conducted field investigations in three stages in 2024, simultaneously distributing questionnaires through a combination of offline and online methods.

1) 15–22 May, 2024: this stage mainly conducted field observation and offline questionnaires, focusing on vegetation landscapes and village greening during the spring farming season.

2) 10–18 June, 2024: this stage combined offline investigation with supplementary online questionnaires to observe riverine landscapes and the use of waterfront spaces during the plum-rain season.

3) 5–12 July, 2024: matching the summer tourist peak, this stage distributed both offline and online questionnaires and conducted in-village interviews to ensure that the survey context matched actual tourism experiences.

A five-point Likert scale was used to quantify landscape satisfaction, with scores of 5, 4, 3, 2, and 1 corresponding to "satisfied, " "relatively satisfied, " "generally satisfied, " "relatively dissatisfied, " and "dissatisfied, " respectively. A total of 580 questionnaires were distributed, including 360 offline and 220 online, of which 569 were valid, yielding an effective response rate of 98.1%^①. To avoid cognitive bias, the offline respondents received one-to-one verbal explanations supported by a plain-language guide, while the online survey included pop-up descriptions and visual examples for each indicator.

① The criteria for identifying invalid questionnaires are as follows: 1) completion rate is less than 80%; 2) obvious logical contradictions in answers; 3) selecting the same score for five or more consecutive scale items (regarded as random filling).

The criteria for valid respondents were as follows: age 18 or above, staying in the village for at least 30 min, having direct experience of the landscape, and the ability to complete the questionnaire independently. The average age of the sample was 37 yr, with respondents aged 21–40 accounting for the largest share. In terms of geographic origin, 45% were local residents from the Yuan River Basin, 40% were visitors from other cities in Hunan, and 15% were visitors from the other province, mainly from neighboring provinces. The occupational composition included students, service industry personnel, agricultural practitioners, and retirees; the gender ratio was 55% female and 45% male. The above sample structure exhibits strong diversity and adaptability, providing a reliable foundation for the subsequent comprehensive IPA–EWM analysis.

3 Research Methods

Figure 1 presents the analytical workflow of the study. First, by reviewing literature and combining the characteristics of traditional villages in the Yuan River Basin, a landscape evaluation indicator system was constructed; subsequently, field investigations and questionnaire surveys were conducted; after data collection, it was preprocessed and subjected to reliability and validity testing using SPSS; then, the EWM method was used to determine the objective indicator weights, and IPA method was applied to plot the importance–satisfaction four-quadrant matrix of the landscape factors, ultimately clarifying the improvement directions and priorities of each factor, providing a theoretical basis for traditional village landscape optimization.

3.1 Indicator Selection

Drawing on previous studies^[10–¹¹,²⁶,⁴⁰–^43], the research objectives, and the complexity of traditional villages, the study initially selected 40 landscape factors. After two rounds of expert consultation involving five cross-disciplinary specialists, the list was reduced to 30 items. It was then further refined through pilot investigation, corrected item–total correlation (CITC) analysis, and exploratory factor analysis (EFA). The tourist satisfaction evaluation indicators for the Yuan River Basin in Hunan were constructed into a three-tier indicator system of "target–landscape element–landscape factor", comprising four elements and 21 landscape factors in total (Table 1).

3.2 EWM Method

EWM determines indicator weights by measuring the degree of information dispersion of the indicators, reflecting their contribution to system uncertainty, thereby reducing subjective bias in manual weighting^[38].

1) Standardize the original data matrix in positive or negative form, with the standardized value denoted as x_ij, where i = 1, 2, 3, …,m represents the sample, and j = 1, 2, 3, …, n, with n represents the indicator.

2) Compute the proportion p_ij of sample i under indicator j:

(1)p_{ij}=\frac{x_{ij}}{\sum _{i=1}^m{x}_{ij}}

3) Calculate the information entropy e_j of indicator j based on Shannon entropy, where k is the normalization coefficient:

(2)e_j=-k\sum _{i=1}^m{p}_{ij}\ln p_{ij}

4) Derive the entropy redundancy d_j:

(3)d_j=1-e_j;

5) calculate the entropy weight w_j:

(4)w_j=\frac{d_j}{\sum _{j=1}^n{d}_j}.

3.3 Reliability Test

To ensure the scientificity and reliability of the questionnaire a reliability analysis was conducted in SPSS 24.0. The overall Cronbach's α coefficient was 0.801 (≥ 0.80), indicating strong internal consistency. A linear regression was then performed to examine the relationship between satisfaction and importance (Table 2). The results indicate a significant positive relationship between the two variables (I = 2.222 + 0.529P). The regression coefficient for satisfaction was 0.529, the t value was 4.185, and the relationship was significant at the 0.01 level (p = 0.001 < 0.01), indicating that satisfaction significantly influences perceived importance.

To examine the normality assumption of the regression residuals, the standardized residuals of the model were further tested using the questionnaire data on importance and satisfaction. The distribution of standardized residuals was broadly close to normal, centered around zero, and approximately symmetric (Fig. 2). In addition, the normal P–P plot of the standardized residuals showed that, although not all points fell exactly on the diagonal, the overall pattern closely followed the reference line, suggesting that the normality assumption was acceptable (Fig. 2).

3.4 Importance–Performance Analysis

The IPA index was calculated using Eq. (5):

(5)IPA=(I-P)/I\times 100,

where I represents importance and P represents satisfaction^②. Lower IPA values indicate higher levels of satisfaction.

② In this research, performance is evaluated with tourists' satisfaction.

To observe the impact of different indicators on satisfaction, this study divided the IPA values into 5 levels^[41]: very satisfied (no more than 5.00), relatively satisfied (5.01–10.00), moderately satisfied (10.01–20.00), dissatisfied (20.01–30.00), and very dissatisfied (no less than 30.01). Subsequently, taking importance as the horizontal axis, satisfaction as the vertical axis, and the mean of the two as the intersection point to create vertical coordinate axes, it is divided into four quadrants to form the IPA matrix chart: 1) Quadrant Ⅰ: both importance and satisfaction are high, belonging to the advantage maintenance area; 2) Quadrant Ⅱ: importance is low, but satisfaction is high, belonging to the status quo maintenance area; 3) Quadrant Ⅲ: both importance and performance are low, belonging to the low-priority development area; 4) Quadrant Ⅳ: importance is high but satisfaction is low, belonging to the key improvement area.

4 Results and Analysis

4.1 EWM Analysis

4.1.1 EWM Analysis of the Landscape Elements

The EWM values of the four landscape elements ranked as follows: culture (2.504) > ecology (2.302) > aesthetics (1.428) > society (1.352), indicating that tourists highly value cultural experiences during their travels, with slightly lower attention paid to other elements.

4.1.2 EWM Analysis of the Landscape Factors

The landscape factors with relatively high EWM values were A1 (0.766), B2 (0.751), B1 (0.673), D1 (0.650), and C4 (0.645), indicating that tourists attach great importance to environmental quality, cultural atmosphere, and recreational experience. Factors with relatively low weights included C3 (0.168), B6 (0.137), C6 (0.046), C2 (0.029), and D5 (0.013), showing there is still obvious potential for improvement in the completeness of facility services and the protection and transmission of cultural heritage in traditional villages.

4.2 IPA Analysis

4.2.1 IPA Analysis of the Landscape Elements

The IPA analysis results for the landscape elements (Table 3) show that the mean importance scores ranked as culture > ecology > society > aesthetics, while the mean satisfaction scores ranked as culture > ecology > aesthetics > society.

The mean importance–satisfaction gap (I −P) ranked as society > culture > ecology > aesthetics. All four dimensions showed positive gaps, indicating that the visitor expectations exceeded actual experience across the board. The results exhibit that traditional villages need to focus on improving the social environment, recreational facilities, tourism management, and service levels while strengthening cultural construction and ecological protection, to meet the diversified needs of tourists. At the same time, it is also necessary to enhancing the aesthetic quality of the village to provide tourists with a better rural tourism experience.

The mean IPA index ranked as society > culture > ecology > aesthetics, with an overall mean of 9.43, corresponding to the "relatively satisfied" level. Visitors were relatively satisfied with aesthetics, ecology, and culture, whereas society reached only the "moderately satisfied" level overall. Within the social dimension, visitors were very satisfied with the richness of recreational activities and relatively satisfied with residents' friendliness but dissatisfied with the adequacy of recreational facilities and with village popularity and reputation, making these factors key constraints on overall satisfaction.

4.2.2 IPA Analysis of the Landscape Factors

The IPA analysis of the landscape factors (Table 4) show that the landscape factors rated as "very satisfied" were A1, B1, B2, B3, B4, C4, D1, D2, D3, and D4. Only A1 was a negative value with a high absolute value, indicating its satisfaction is higher than importance, and tourists were very satisfied with this. This may be because the overall layout and planning of the village landscape are orderly, and the elements are harmonious and unified, leaving a deep impression on tourists. While the IPA indices of the remaining landscape factors were all positive, although their absolute values were small, meaning that tourists' satisfaction with these factors is already relatively high, but not reached a level matching their importance. Therefore, under limited resources, priority should be given to improving those factors with larger absolute IPA index values.

Four factors—A2, A3, C1, and C5—fell into the "relatively satisfied" category, suggesting generally high satisfaction and importance perceived by the tourists, which are aspects worth continuing to maintain and optimize in village tourism development. A4, B5, B6, and C3 were classified as "moderately satisfied." The higher IPA indices of these factors indicate that their perceived importance is relatively high, while satisfaction is relatively low, making them aspects that need key attention and improvement in village landscape management and planning. By contrast, C2 and C6 were rated as "dissatisfied, " and D5 as "very dissatisfied." These factors are very important to tourists, but the tourists' satisfaction is low, which may affect their overall impression of the village and their willingness to visit again.

4.2.3 Quadrant Analysis of the IPA Matrix

Using the mean values of importance and satisfaction (4.25, 3.84) as the intersection point, the 21 factors were mapped into four quadrants (Fig. 3).

1) Quadrant Ⅰ (strength maintaining area) included A4, B1, B2, C1, C4, D1, D2, and D4. For these factors, their advantages in both importance and satisfaction and should be maintained while seeking further refinement to consolidate tourist satisfaction and loyalty.

2) Quadrant Ⅱ (status quo maintenance area) included B3. This factor was neither overly important nor showed an obvious performance deficit, the status quo can be maintained, although continued monitoring for potential development opportunities is still advisable to prevent future decline.

3) Quadrant Ⅲ (low-priority development area) included A1, A2, A3, B4, B5, B6, C2, C5, and D3. These factors had relatively high importance but relatively low satisfaction, suggesting that improvement is needed to better match tourists' expectations and demands.

4) Quadrant Ⅳ (key improvement area) included C3, C6, and D5. These factors combined high importance with very low satisfaction and should therefore be treated as the primary targets for intervention. Their underlying problems require careful diagnosis and prompt action in order to enhance tourist satisfaction and improve village competitiveness.

5 Discussions

Compared with single IPA analysis or methods relying entirely on subjective weighting, the IPAEWM framework integrates perceived importance with objective weighting and thus provides a more scientific and precise assessment of tourist satisfaction. The results show that cultural factors received the highest EWM value (2.504), highlighting the central role of cultural experience in visitors' overall perceptions. In particular, the continuity of intangible cultural heritage showed a high importance score (4.91) but a relatively low satisfaction score (3.39), resulting in an IPA index as high as 30.957, which falls into the very dissatisfactory category This indirectly corroborates the sensitivity and effectiveness of the IPA–EWM method in identifying key weak links. The findings further support the view of Fengqun Wei et al.^[44] that deficiencies in cultural transmission significantly affect visitor perception, highlighting the urgency for its improvement. In addition, by combining field questionnaires with statistical modeling, this study provides more actionable evidence for interpreting spatial indicators such as landscape coordination and spatial aggregation/dispersion, complementing the nostalgia perception research path based on online texts adopted by Ziling Wang et al.^[27].

In the importance–satisfaction four-quadrant matrix, factors located in the Quadarant Ⅳ, such as the continuity of intangible cultural heritage, adequacy of recreational facilities, and village popularity and reputation, carry high expectations from tourists but fail to satisfy them, urgently requiring priority intervention. This finding echoes the study by Yunning Zhang et al.^[25] on the Anyi Ancient Village Cluster, which similarly pointed out that intangible cultural experiences significantly affect tourist satisfaction, yet implementation is often insufficient in practice. In Quadrant Ⅲ, factors such as landscape coordination, spatial aggregation/dispersion, and local materials score relatively low in both importance and satisfaction, suggesting that further efforts are needed to improve spatial aesthetics and cultural imagery expression in villages. Additionally, the quadrant division of some factors further shows the need for a coupled analysis. For example, local materials in Quadrant Ⅲ have a relatively high satisfaction score (3.64) but slightly lower importance (3.81), suggesting that interpretation should integrate EWM weights and actual mean values for comprehensive judgment to avoid misjudgments caused by a single criterion. Therefore, the IPA–EWM approach demonstrates stronger adaptability and explanatory power in balancing subjective expectations and objective evaluations, providing theoretical support and practical guidance for the optimization of traditional village landscape construction.

The IPA–EWM-based evaluation on tourists' satisfaction indicates deficiencies to varying degrees in the protection and transmission of intangible cultural heritage, the adequacy of recreational facilities, village popularity and reputation, and aesthetic quality, all of which require coordinated improvement measures. First, a sound mechanism for the protection and transmission of intangible cultural heritage should be established. Regional cultural activities such as Meishan Nuo Opera, Xinhua folk songs, and Meishan paper-cutting can be organized to enhance visitors' awareness of and participation in traditional culture. This would not only strengthen the continuity of intangible heritage, but also enrich visitors' cultural experiences and improve overall satisfaction^[43]. Second, functional spaces such as rest areas, viewing platforms, and cultural exhibition halls should be rationally planned and added to improve the utility and convenience of facilities. In addition, existing facilities should also be properly maintained and updated to ensure efficient operation and service quality, thereby improving the visitor experience. Village popularity and reputation should be enhanced through fully utilization of diversified platforms such as social media and tourism websites by publishing high-quality graphic and video content to expand influence and attract potential visitors. On this basis, there is also a need to continuously optimize the tourism service system by strengthening practitioner training, rational route design, and upgrading catering and accommodation services, so as to establish a positive service image and enhance tourist loyalty and revisit intention. In addition, while respecting and preserving the original features of the village, aesthetic elements should be further optimized by improving landscape planning and design, particularly in terms of order, coordination, and cleanliness. Increasing vegetation coverage and landscape diversity would further improve visual quality and environmental comfort, thereby enhancing the aesthetic experience of visitors. To ensure the continuous and effective implementation of the above measures, it is recommended to establish a dynamic monitoring mechanism for tourist satisfaction, regularly conduct questionnaire surveys and interviews, comprehensively grasp tourist feedback, and adjust and optimize tourism products and services in a timely manner, thereby promoting the scientific, systematic, and sustainable development of traditional village tourism^[45–^46].

6 Conclusions

This study evaluated tourist satisfaction with traditional village landscapes in the Yuan River Basin of Hunan Province through the IPA and EWM methods, drawing the following conclusions:

1) The traditional village landscapes in this basin are generally relatively satisfying, with a mean IPA index of 9.43. However, society has the highest IPA index (13.636), indicating the large gap between visitor expectations and actual perceptions and thus needs improvement.

2) The analysis of landscape elements shows that culture rank first in both importance (4.51) and satisfaction (4.09), highlighting tourists' high recognition of cultural value. Among landscape factors, air quality and diversity of folk culture receive relatively high weights, reflecting their key role in shaping tourist satisfaction;

In summary, this study provides optimization directions for the landscape construction of traditional villages in the Yuan River Basin, particularly in social elements (adequacy of recreational facilities, village popularity and reputation) and cultural elements (continuity of intangible cultural heritage). However, this study is limited to the Yuan River Basin in Hunan Province, and the applicability of the outcome in other regions remains to be tested. Meanwhile, although the IPA–EWM method combines the advantages of objective weighting and subjective evaluation, it may still be affected by region, season, and tourist group characteristics in practical applications, unable to comprehensively reflect the actual situation of tourist satisfaction with traditional village landscapes in the basin. Furthermore, when constructing the IPA matrix chart, this study has not fully considered the internal connections and mutual influences among the indicators, leading to certain limitations in judging improvement directions and priorities. Future research could conduct deeper explorations of tourist satisfaction regarding the above aspects.

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