Integrating AHP-SBE for Evaluating Visitor Satisfaction in Traditional Village Tourism Landscapes

Abstract

Traditional villages, as repositories of cultural heritage and natural landscapes, have gained increasing prominence in the tourism industry. However, balancing authenticity preservation with visitor satisfaction remains a critical challenge. This study employs a combined Analytic Hierarchy Process (AHP)–Scenic Beauty Estimation (SBE) approach under the theoretical framework of 4E theory (Entertainment, Education, Aesthetics, and Escapism) to comprehensively evaluate visitor satisfaction in traditional village tourism landscapes. Eight nationally designated tourism-oriented traditional villages in Anhua County, China were selected as case studies. Findings from the AHP analysis reveal that aesthetic and escapism experiences are the most influential dimensions in shaping visitor satisfaction, while entertainment and educational experiences, though secondary, remain integral to a well-rounded tourism framework. The SBE evaluation corroborates these results, highlighting that seasonal characteristics, stress relief, and cultural landscape diversity significantly enhance visitor experiences. Conversely, deficiencies were observed in social interactivity, satisfaction with educational experiences, and fulfillment of aesthetic needs, indicating areas for improvement. A strong positive correlation between AHP and SBE scores (Pearson correlation coefficient = 0.867, p < 0.01) underscores the alignment between expert-driven evaluations and visitor perceptions. These insights suggest that integrating expert-based hierarchical analysis with empirical visitor assessments provides a more robust and multidimensional framework for sustainable tourism management. Recommendations include enhancing social interactivity, optimizing educational components, enriching aesthetic experiences, and ensuring the preservation of vernacular landscapes to foster sustainable, experience-driven rural tourism development.

1. Introduction

With the accelerated expansion of the global tourism industry, traditional villages, as invaluable cultural and natural assets, have garnered increasing attention from domestic and international visitors [1,2]. These villages not only serve as repositories of profound historical and cultural heritage, but also boast distinctive vernacular landscapes and rich folk traditions, offering tourists an immersive and multifaceted travel experience. Historically, tourism in traditional villages has been driven by visitors’ aspirations for aesthetic appreciation, leisurely recreation, and cultural enrichment, primarily through sightseeing and passive engagement [3]. However, as market competition intensifies and tourist preferences evolve, there is a discernible shift toward health-oriented tourism, sociocultural exploration, experiential learning, and immersive interactions with local traditions [4].

The 4E theory presents a human-centered economic model that prioritizes experiential engagement and consumer participation. This theoretical framework underscores the psychological and emotional dimensions of consumption, positing that meaningful tourism experiences extend beyond passive observation to active involvement. The core principles of the 4E theory can be distilled as follows: (1) Economic Viability of Landscapes—The theory underscores that practical projects serve as the primary vehicles for experiential tourism development; (2) Emotional Engagement in Tourism Experiences—In traditional village tourism, experience-oriented marketing must transcend functional attributes, fostering deep emotional resonance between visitors and landscapes; (3) Personalization of Experience—The theory encapsulates entertainment, education, aesthetics, and escapism, aligning seamlessly with the environmental culture, agrarian practices, and vernacular traditions of traditional villages. This framework enables individuals to project subjective emotions onto animate and inanimate elements, imbuing them with perceptual, cognitive, and affective significance, thereby enriching the aesthetic and sensory experience. The integration of the 4E theory into traditional village tourism landscapes allows for thematic engagement across its four experiential pillars, effectively catering to modern tourists’ psychological expectations. Furthermore, this experience-driven approach revitalizes rural communities, enhances residential conditions, elevates architectural quality, and facilitates the restoration of critical infrastructure.

The Analytic Hierarchy Process (AHP), pioneered by Thomas Saaty in the early 1970s, has emerged as a widely utilized multi-criteria decision-making tool in tourism planning and landscape evaluation. Internationally, AHP has been extensively applied to the assessment of railway landscapes [5], historical urban landscapes [6], environmental sustainability metrics [7], lithic cultural landscapes [8], historical districts, urban communities [9], and highway landscapes. In China, AHP has been instrumental in evaluating agritourism product packaging, rural e-commerce development, nature reserves, forest wellness tourism, rural ecological livability, vernacular landscape quality, rural tourism development, and cultural squares in villages. To mitigate the methodological limitations of singular evaluation techniques, AHP has frequently been integrated with other analytical models, including GIS-AHP [10,11], PB-AHP [12], AHP-FCE [13,14], AHP-Entropy [15,16], AHP-PCA [17], AHP-SD [18], SBE-AHP, AHP-PROMETHEE-GIS [19], AHP-TOPSIS [20], AHP-TOPSIS-POE [21], SWOT-AHP [22,23], and AHP-IPA [24].

Although prior research has explored integrated AHP-SBE methodologies, their applications have remained predominantly within general rural contexts, with limited exploration in the domain of traditional village tourism landscapes. Moreover, studies incorporating the 4E theory into AHP-SBE analyses remain scarce. To address this gap, the present study selects eight nationally designated tourism-oriented traditional villages within Anhua County, located in the Zishui River Basin, China, as case studies. By employing the AHP-SBE method under the theoretical framework of the 4E model, this research conducts a comprehensive assessment of visitor satisfaction in traditional village tourism landscapes. Specifically, the study investigates the discrepancies between visitor expectations and actual experiences across four core dimensions: entertainment, education, aesthetics, and escapism. The findings aim to provide scientific insights and empirical guidance for the sustainable development, preservation, and management of traditional village tourism landscapes, ensuring their long-term viability and enhanced visitor appeal.

2. Methodology and Materials

2.1. Study Area

Anhua County, situated under the administrative jurisdiction of Yiyang City, Hunan Province, China, occupies a strategic position in the north-central region of Hunan, nestled within the northern foothills of the Xuefeng Mountains along the middle reaches of the Zishui River (Figure 1). As a predominantly mountainous region, Anhua County is characterized by a subtropical monsoon climate, fostering fertile soils that are highly conducive to agricultural activities. The Zishui River, which bisects the county, is fed by 170 tributaries, each exceeding 5 km in length, contributing to a diverse and complex hydrological network that supports both ecological stability and agrarian productivity.

Currently, 14 traditional villages within Anhua County remain well-preserved, serving as integral components of China’s rural heritage. This study selects eight nationally designated tourism-oriented traditional villages, as officially recognized by the Ministry of Housing and Urban-Rural Development and the Ministry of Culture and Tourism of China, across the first to sixth batches of designated historical sites. The selected villages include Huangshaping Old Street, Tangjiaguan, Maluxi, Dongshi, Gaocheng, Huanghuaxi, Tianzishan, and Meishan. These villages exemplify the unique historical and cultural legacy of Anhua’s ancient settlements, encapsulating vernacular architecture, diverse rural landscapes, and rich intangible cultural heritage. Their centuries-old craftsmanship and deeply rooted folk traditions render them invaluable for both heritage conservation and sustainable tourism development.

2.2. Evaluating Traditional Village Tourism Landscapes Using the AHP Method

2.2.1. Development of the Evaluation Index System

To construct a systematic and objective evaluation framework for traditional village tourism landscapes, this study integrates extensive literature review, empirical field investigations in Anhua County, and practical regional development considerations. The 4E theory serves as the conceptual foundation, providing a multi-faceted experiential lens through which tourism landscapes are assessed.

To refine the evaluation criteria, the Delphi method was employed, enabling expert consultation and iterative consensus-building to ensure multi-dimensional validity and robust screening of evaluation indicators [25]. The final evaluation index system is structured into four primary dimensions, which are further delineated into 16 specific indicators across multiple assessment levels. The hierarchical structure is presented in Table 1.

2.2.2. Calculating Relative Weights

Between July and December 2024, an on-site survey was conducted across eight nationally designated tourism-oriented traditional villages in Anhua County. Photographs and video documentation were collected to evaluate the four key experiential dimensions: entertainment, education, aesthetics, and escapism.

A survey questionnaire titled “Weight Determination of Traditional Village Tourism Landscape Satisfaction Evaluation Index System Based on the 4E Theory” was designed and distributed using the Delphi method. The survey was disseminated via email, Wenjuanxing (a professional online survey platform), and in-depth interviews to 18 experts specializing in urban and rural planning, environmental design, landscape architecture, and tourism management. Experts assigned weights to evaluation indicators through pairwise comparisons, constructing a judgment matrix. The final weights were calculated using AHP mathematical formulas, employing Excel and SPSS 24.0 software for consistency testing.

• (1). Constructing the Judgment Matrix

$A=\left[\begin{array}{ccccc}{a}_{11}& a_{12}& \cdots & \cdots & a_{1n}\\ a_{21}& a_{22}& \cdots & \cdots & a_{2n}\\ \cdots & \cdots & a_{ij}& \cdots & \cdots \\ \cdots & \cdots & \cdots & \cdots & \cdots \\ a_{n1}& a_{n2}& \cdots & \cdots & a_{nn}\end{array}\right]$

where $a_{ij}$ denotes the relative importance of criterion $A_i$ in comparison to criterion $A_j$. If Ai holds greater significance than $A_j$ within the hierarchical evaluation framework, then $a_{ij}$> 1. Conversely, if both criteria are deemed equally important, their comparative value is expressed as $a_{ij}$= 1.

• (2). Determining the Importance of Matrix Elements (Table 2)

• (3). Calculating Indicator Weight Vectors

Step 1: Normalization of the Judgment Matrix.

The judgment matrix is normalized using the formula:

$b_{ij}={\displaystyle \frac{a_{ij}}{{\sum }_{i=1}^n{a}_{ij}}}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}(i,j=1,2,\cdots n)$

where a_ij represents the data located in the ith row and jth column, reflecting the relative importance of criterion A_i in comparison to A_j. Similarly, in the normalized matrix A, the corresponding element b_ij denotes the normalized value of a_ij at the same position, ensuring consistency in the comparative assessment.

Step 2: Summing the Normalized Matrix Elements.

$\overline{w_i}=\sum _{j=1}^n{b}_{ij}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}(i,j=1,2,\cdots n)$

Step 3: Normalization of Weight Vectors.

$w_i={\displaystyle \frac{\overline{w_i}}{{\sum }_{i=1}^n\overline{w_i}}}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}\hspace{0.33em}(i=1,2,\cdots n)$

where $w_i$ is the weight of the ith indicator.

Step 4: Calculating the Maximum Eigenvalue of the Judgment Matrix.

${\lambda {\displaystyle \frac{1}{n}}\sum _{i=1}^n{\displaystyle \frac{(Aw{)}_i}{w_i}}}_{max}$

where n represents the order of the matrix, indicating the number of evaluation criteria under consideration. The parameter w_i denotes the weight assigned to the ith indicator, reflecting its relative significance within the hierarchical structure. Additionally, λ_max represents the largest eigenvalue of the judgment matrix A.

Consistency Testing.

The Consistency Index (CI) is calculated as follows:

$CI={\displaystyle \frac{{\lambda }_{max}}{n-1}}$

By utilizing the order of the matrix (n), the Random Index (RI) value can be determined (Table 3), facilitating the computation of the Consistency Ratio (CR), expressed as CR = CI/RI, where CI represents the Consistency Index. If the CR value is less than 0.1 (CR < 0.1), the consistency check is considered acceptable, indicating that the judgment matrix exhibits a satisfactory level of logical consistency.

2.3. Scenic Beauty Estimation (SBE) Method

A comprehensive dataset comprising 6374 high-resolution landscape photographs was meticulously collected and systematically documented across eight nationally designated tourism-oriented traditional villages in Anhua County. These images serve as the empirical foundation for the quantitative analysis of scenic beauty perceptions. To enhance the accuracy, reliability, and representativeness of the dataset, a rigorous selection protocol was implemented. Non-representative images were systematically excluded, ensuring that only those most accurately depicting the landscape’s characteristics were retained.

Photographic capture adhered to strict methodological controls to maintain spatial consistency and objective representation. Images were taken from the four cardinal directions (east, south, west, and north) at a fixed height of 1.6 m, employing a high-resolution digital camera to ensure uniform perspective and optimal image clarity. Each photograph was assigned a sequential numerical identifier, facilitating precise tracking and systematic evaluation across participant groups.

To assess scenic beauty perception, participants were instructed to evaluate and rate the photographs based on visual appeal. Each image was displayed for 30 s, adhering to a standardized rating scale, where: −3 = Extremely Poor, −2 = Very Poor, −1 = Poor, 0 = Neutral, 1 = Good, 2 = Very Good, and 3 = Excellent. To minimize subjective bias arising from individual aesthetic preferences, a statistical standardization process was applied to the collected evaluation data. Standardization is a mathematical normalization technique designed to adjust scoring discrepancies among different evaluators, ensuring comparability across assessments and mitigating potential biases introduced by variability in personal aesthetic judgment [26].

The standardization formula applied is as follows:

$$\begin{gathered} Z_{\mathrm{ij}}=(R_{\mathrm{ij}}-\overline{R_j})/S_j \\ {Z}_i=\sum _j{Z}_{\mathrm{ij}}/N_j \end{gathered}$$

where $Z_{ij}$ represents the standardized score assigned by the jth evaluator for the ith landscape, $R_{ij}$ denotes the raw score assigned by the jth evaluator for the ith landscape, R_j signifies the mean score across all ratings given by the jth evaluator, S_j represents the standard deviation of all ratings assigned by the jth evaluator, and Z_i is the final standardized score for the ith landscape.

3. Results

3.1. Reliability and Validity Analysis

To ascertain the robustness, internal consistency, and methodological soundness of the 16 landscape evaluation indicators, a comprehensive reliability and validity assessment was conducted. The statistical analysis was performed using SPSS 24.0, ensuring rigorous verification of data accuracy and structural validity. First, Cronbach’s α coefficient was computed to evaluate the internal reliability of the questionnaire dataset, yielding a value of 0.756 (>0.7). This result indicates an acceptable threshold of reliability, affirming that the study exhibits a high degree of internal consistency across the evaluation constructs. Second, the Kaiser-Meyer-Olkin test for factor adequacy produced satisfactory values, confirming that the dataset was well-suited for factor extraction and advanced statistical analysis. Furthermore, all assessment items were deemed methodologically appropriate, with no variables exhibiting statistical anomalies or necessitating elimination. Additionally, Bartlett’s test of sphericity yielded a significance level (p) of 0.000, demonstrating that the 16 landscape evaluation indicators conform to a multivariate normal distribution under ideal conditions. Given these results, no modifications to the dataset were required, thereby ensuring its integrity, statistical reliability, and suitability for subsequent inferential analyses.

3.2. AHP Value Analysis

The statistical findings presented in Table 4 reveal a high degree of consistency in the weight distribution of evaluation criteria and comprehensive scores within the Traditional Village Tourism Landscape Satisfaction Assessment Based on the 4E Theory. This consistency underscores the existence of shared determinants influencing visitor satisfaction across different experiential dimensions.

At the criteria level, the weight distribution follows the ranking: Aesthetic Experience (0.4658) > Escapism Experience (0.2771) > Entertainment Experience (0.1611) > Educational Experience (0.096).

These results indicate that aesthetic experience holds the greatest significance for tourists visiting traditional villages, followed by escapism, suggesting a strong inclination toward immersive and restorative experiences that facilitate temporary detachment from daily routines. Nonetheless, the findings also emphasize the necessity of integrating entertainment and educational experiences into the evaluation framework to ensure a holistic and enriching visitor experience.

At the indicator level, the most influential factors within each experience dimension are as follows: (1) Entertainment Experience: Immersive Atmosphere Creation (0.4839), Engagement of Young Visitors (0.2795), Diversity of Scenic Attractions (0.1680); (2) Educational Experience: Knowledge Acquisition Effectiveness (0.4658), Knowledge Stimulation and Guidance (0.2771), Mastery of Techniques and Methods (0.1611); (3) Aesthetic Experience: Seasonal and Regional Characteristics (0.4949), Diversity of Cultural Landscapes (0.2755), Fulfillment of Aesthetic Needs (0.1650); (4) Escapism Experience: Stress Relief and Personal Liberation (0.4925), Degree of Escape from Reality (0.3081), Environmental Contrast (0.1058).

Additionally, certain indicators exhibited relatively lower weight contributions, including Social Interactivity (0.0685), Mastery of Techniques and Methods (0.1611), Satisfaction with Educational Experience (0.0960), and Fulfillment of Aesthetic Needs (0.1650). This suggests that while these elements contribute to the overall tourism experience, they are perceived as less critical in shaping tourists’ overall satisfaction.

The AHP analysis demonstrates that visitors to traditional village tourism landscapes prioritize aesthetic and escapism experiences over entertainment and educational aspects. The findings suggest that traditional villages should emphasize enhancing their aesthetic appeal and creating immersive environments that allow visitors to detach from their daily lives. Aesthetic enhancement strategies should focus on seasonal beauty, regional uniqueness, and diverse cultural landscapes to maximize visitor engagement. Escapism-oriented tourism development should emphasize stress relief, immersive rural environments, and distinctive contrasts from urban life to fulfill visitors’ psychological needs. Entertainment and educational experiences, while relatively less significant, remain integral components of overall satisfaction and should be incorporated thoughtfully into tourism development strategies. The consistency check results (CR < 0.10 for all categories) confirm the validity and robustness of the evaluation model, ensuring that the findings provide a scientifically sound basis for the development and management of traditional village tourism landscapes.

3.3. Analysis of SBE Value

As depicted in Figure 2, the Z-score results derived from the SBE method elucidate a hierarchical ranking of factors influencing visitor satisfaction in traditional village tourism landscapes. The results are categorized into highly ranked and lower-ranked indicators as follows: Top-Ranked Factors: Seasonal and Regional Characteristics (0.72376), Stress Relief and Personal Liberation (0.54162), Diversity of Cultural Landscapes (0.46283), Degree of Escape from Reality (0.40684); Lower-Ranked Factors: Transcendence and Serenity (0.02674), Mastery of Techniques and Methods (−0.22365), Fulfillment of Aesthetic Needs (−0.32165), Social Interactivity (−0.95635), Satisfaction with Educational Experience (−1.85422). The findings underscore the paramount significance of seasonal variations and regional distinctiveness in shaping tourists’ aesthetic perceptions and overall satisfaction.

Visitors exhibit heightened aesthetic appreciation when landscapes possess vivid seasonal attributes or culturally distinctive elements, reinforcing the crucial role of dynamic environmental aesthetics in tourism appeal.

Moreover, contemporary travelers increasingly prioritize psychological relaxation and emotional well-being in their tourism experiences. The elevated ranking of stress relief and personal liberation highlights the function of traditional village landscapes as sanctuaries for emotional rejuvenation and cognitive restoration. Additionally, the diversity of cultural landscapes emerges as a critical determinant of visitor satisfaction, as multifaceted and immersive cultural environments significantly enrich touristic engagement and aesthetic depth, fostering memorable and fulfilling travel experiences. Furthermore, the desire for escapism remains a fundamental driver of rural tourism motivation. Traditional villages that successfully establish a stark contrast with urban environments, thereby facilitating a profound sense of detachment from modern life, are more likely to resonate with visitors seeking temporary reprieve from urban pressures.

Conversely, the five lowest-ranked indicators reveal potential deficiencies in the traditional village tourism experience, warranting further examination and strategic improvement: (1) Transcendence and Serenity: The low ranking suggests that factors such as excessive commercialization, overcrowding, environmental degradation, and shifts in local lifestyles may be eroding the tranquility and seclusion that visitors inherently associate with authentic rural tourism; (2) Mastery of Techniques and Methods: Experiences requiring tourists to acquire specialized skills or technical proficiency may diminish engagement levels, as overly complex activities can be less appealing to casual visitors; (3) Fulfillment of Aesthetic Needs: A failure to align with visitors’ aesthetic expectations could lead to perceived visual and artistic deficiencies within the landscape. This highlights the necessity for curated, visually cohesive environments that resonate with tourists’ aesthetic and emotional sensibilities; (4) Social Interactivity: Excessive social engagement during the tourism experience may distract visitors from fully immersing themselves in the landscape, subsequently diminishing the depth of aesthetic appreciation and personal reflection; (5) Satisfaction with Educational Experience: The relatively low reception of educational content among tourists implies that traditional villages should reassess the integration of educational elements within the visitor experience. Overly didactic or structured approaches may fail to align with the experiential preferences of modern tourists, who often prioritize interactive, sensory-rich, and emotionally engaging experiences over passive knowledge acquisition.

The discrepancies observed between high- and low-ranking indicators suggest that while traditional villages excel in offering aesthetically rich and escapism-driven experiences, challenges remain in balancing interactivity, educational value, and visitor engagement. To elevate the overall tourism experience, strategic improvements should focus on: (1) Preserving Tranquility and Authenticity—Implementing controlled visitor flows, minimizing commercial overdevelopment, and enhancing environmental conservation measures to reinforce the village’s intrinsic serenity; (2) Enhancing Experiential Learning—Redesigning educational programs to incorporate interactive, hands-on cultural activities, ensuring a more engaging and accessible knowledge-sharing approach; (3) Optimizing Social Interaction—Creating structured yet organic opportunities for visitor engagement without compromising immersive and aesthetic appreciation.

3.4. Correlation Analysis Between AHP and SBE Values

3.4.1. Correlation Analysis

A linear regression analysis, performed using SPSS 24.0, yielded the following regression equation:

AHP = 0.062 + 0.055SBEAHP = 0.062 + 0.055SBE

This model indicates a statistically significant positive relationship between SBE scores and AHP scores, suggesting that variations in SBE values exert a considerable influence on AHP outcomes. Specifically, for every one-unit increase in the SBE score, the AHP score exhibits an average increment of 0.055 units. The high coefficient value reinforces the assertion that AHP-derived evaluation scores substantially shape subjective assessments captured through the SBE method.

In this model, the t-value of SBE is 2.728, implying a substantial influence of the independent variable (SBE) on the dependent variable (AHP). Typically, a larger absolute t-value signifies a greater impact of the predictor variable. Furthermore, the p-value associated with SBE is 0.016 (<0.05), confirming that SBE has a statistically significant effect on AHP scores. The detailed results of the regression analysis are summarized in Table 5.

As illustrated in Table 6, the Pearson correlation coefficient (r) between AHP and SBE is 0.867, indicating a strong positive correlation between these two assessment frameworks. Moreover, the significance level (p = 0.000 < 0.05) further corroborates the substantial impact of AHP scores on SBE outcomes.

Despite the relatively small sample size (N = 16), the high correlation coefficient remains statistically significant, suggesting that even within a limited dataset, AHP and SBE exhibit a strong associative relationship. Although these two methodologies adopt distinct analytical paradigms, their high correlation in evaluating tourist satisfaction within traditional village tourism landscapes highlights their mutual complementarity. This finding underscores their potential to be integrated into a more robust, multidimensional assessment framework, thereby offering greater analytical precision and a more holistic approach to decision-making in rural tourism landscape evaluation.

These findings reinforce the potential for integrating AHP and SBE methodologies to create a comprehensive and multidimensional assessment framework for traditional village tourism landscapes. The strong correlation between these two methods suggests that while AHP provides a structured, hierarchical evaluation based on expert-driven criteria, SBE offers empirical insights derived from visitor perceptions. Their synergistic application can enhance decision-making accuracy, facilitate more nuanced interpretations of tourist satisfaction, and ultimately contribute to the sustainable management and strategic development of rural tourism destinations.

3.4.2. Divergence in Individual Evaluations Between AHP and SBE Scores

The AHP score for the fulfillment of tourists’ aesthetic needs was calculated as 0.1650, ranking ninth among all assessed factors. This positioning indicates that, within the hierarchical evaluation framework, this factor holds a relatively lower degree of importance. Given that AHP is a structured decision-making tool employed to determine the relative significance of multiple evaluation criteria, this ranking reflects expert consensus that aesthetic fulfillment is not among the most critical determinants influencing overall tourist satisfaction.

Conversely, the SBE score for aesthetic fulfillment was recorded as −0.32165, placing it 14th (third lowest) among all evaluated indicators. This suggests that tourists generally expressed a lower level of satisfaction regarding the aesthetic appeal of traditional village landscapes. Furthermore, the negative score explicitly implies aesthetic deficiencies, signaling areas that may require targeted landscape enhancements and visual refinements.

The visitor rating scale ranged from 2 (very good) to −2 (very poor), revealing considerable variability in individual assessments. This divergence can be attributed to multiple interrelated factors, including the following: Personal preferences and subjective aesthetic standards, cultural backgrounds and prior experiential exposure, and contextual influences at the time of evaluation (e.g., mood, weather conditions, and ambient environmental factors).

Aesthetic perception is highly subjective, as individualized aesthetic sensibilities and cognitive interpretations shape the way visitors evaluate and emotionally respond to landscapes. As a result, tourists may form widely divergent opinions about the same traditional village environment. Moreover, cultural background exerts a profound influence on aesthetic expectations—visitors from different cultural traditions and artistic paradigms may demonstrate varied preferences and interpretations of vernacular landscapes. Similarly, situational and environmental variables at the time of evaluation (e.g., atmospheric conditions, emotional disposition, or immediate comparisons with other scenic destinations) can further modulate aesthetic judgments and contribute to discrepancies in scoring.

By juxtaposing AHP and SBE scores, a more nuanced and multidimensional understanding of aesthetic fulfillment within the evaluation framework can be achieved. While the AHP score encapsulates expert-derived assessments regarding the relative importance of aesthetic fulfillment, the SBE score provides empirical insights derived directly from visitor perceptions and experiential realities.

This comparative analysis is particularly valuable in mitigating subjectivity in the evaluation of tourist satisfaction with traditional village landscapes. AHP scores are derived from a structured, expert-driven decision-making framework, whereas SBE scores represent the collective assessments of a broad tourist demographic. By integrating these two complementary methodologies, a more balanced, objective, and data-driven evaluation model can be established, thereby informing evidence-based landscape improvements and optimizing tourism management strategies for enhanced visitor experiences.

4. Discussion

4.1. Enhancing Social Interactivity in Entertainment Experiences

As a public service-oriented tourism asset, traditional village landscapes play a pivotal role in recreation, leisure, and cultural engagement [27,28]. The entertainment dimension of traditional village tourism encompasses a diverse range of agritourism experiences, including agricultural sightseeing (e.g., canola flower viewing), horticultural activities (e.g., paddy cultivation, rice harvesting, and organic vegetable farming), as well as family-oriented tourism, agricultural exhibitions, and interactive farming experiences. Within the planning and design of traditional village tourism landscapes, it is imperative to safeguard and perpetuate the cultural heritage associated with agricultural labor [29]. By seamlessly integrating agrarian activities with visitor engagement, an immersive and participatory tourism environment can be cultivated, thereby enhancing the cultural significance and experiential value of traditional village landscapes.

The social interactivity component within entertainment experiences holds a comprehensive weight of 0.011035 with a Z-score of −0.95635, classifying it within the low evaluation tier. This suggests that social engagement within entertainment experiences is currently underdeveloped and requires strategic enhancement. To amplify social interactivity, three key strategic interventions are proposed: (1) Incorporating Interactive Elements: The integration of engaging activities, such as interactive games, Q&A sessions, and structured group discussions can significantly stimulate visitor interest and encourage active participation; These activities foster a dynamic and immersive atmosphere, motivating tourists to exchange ideas, share experiences, and engage in meaningful interactions; (2) Promoting Collaborative Participation: Team-based activities and cooperative projects provide opportunities for visitors to work towards shared objectives, thereby reinforcing mutual understanding and trust; Such initiatives serve as catalysts for fostering deeper interpersonal connections, further enhancing social engagement within the tourism experience; (3) Leveraging Social Media Platforms: The establishment of online communities, along with the regular dissemination of event updates, interactive discussions, and digital engagement initiatives, can broaden audience participation; This strategy not only expands the spatial and temporal reach of social interactions but also enhances accessibility and convenience, enabling a more dynamic and sustained engagement.

By synthesizing these strategic measures, a vibrant and socially engaging tourism environment can be cultivated, allowing visitors to experience a more enriching and interactive social atmosphere within traditional village tourism landscapes. A notable case study exemplifying the integration of social interactivity into landscape design is the “Xiangju Commune” project in Jianjian, Chongming District, Shanghai. This initiative successfully preserved existing rice paddies and water bodies while simultaneously reconfiguring the landscape into a QR code-shaped rice field maze. This innovative approach not only enhanced the aesthetic and historical appeal of the rice fields but also improved irrigation efficiency. To achieve the desired textural and structural effect, bamboo clamping plates were utilized for pathway demarcation, while rice plants were strategically bundled to ensure structural integrity. Once the maze had served its purpose, the temporary structures were dismantled, allowing the farmland to be fully restored for the subsequent planting season.

4.2. Enhancing Aesthetic Experience and Fulfilling Tourists’ Aesthetic Need

The aesthetic experience in traditional village tourism landscapes encompasses both tangible cultural heritage and natural scenery, forming an integral component of visitor engagement. Material culture, as a quintessential representation of local heritage, offers a distinctive, immersive experience, evoking a sense of novelty and deep cultural appreciation among tourists [3,30]. The comprehensive weight of tourist aesthetic fulfillment was calculated as 0.076857, with a Z-score of −0.32165, categorizing it at a lower evaluation level. These findings underscore the necessity of enhancing the aesthetic appeal of traditional villages through strategic preservation, thoughtful restoration, and innovative design approaches. To elevate the aesthetic experience, it is imperative to adhere strictly to principles of heritage conservation, ensuring that historical buildings remain structurally intact while simultaneously preserving the authenticity of the rural landscape [31]. Restoration efforts should prioritize architectural integrity, retaining cultural distinctiveness, and incorporating contextually appropriate adaptations that harmonize with the historical fabric of the village environment [32].

4.2.1. Key Strategies for Architectural and Landscape Preservation

• (1). Architectural Conservation and Contextual Innovation

Traditional architectural styles, color palettes, and material textures should be meticulously preserved while allowing for selective, contextually sensitive adaptations that enhance both functionality and aesthetic coherence [33].

• (2). Rural Landscape and Vernacular Design

The design of village residential areas and communal spaces should prioritize the preservation of local cultural identity, ensuring that vernacular elements are thoughtfully integrated. Honoring traditional customs and indigenous craftsmanship can further strengthen the authenticity and visual harmony of the landscape [34].

• (3). Cultural Interpretation and Experiential Engagement

A deep understanding of vernacular culture is essential for effective planning, restoration, and landscape revitalization. Spatial planning should ensure a harmonious interplay between historical architecture, rural landscapes, and contemporary functional enhancements, allowing for a seamless fusion of tradition and modernity [35].

4.2.2. Case Study: Jingping Ancient Village, Zhongfang County, Huaihua City

A compelling illustration of aesthetic conservation principles can be observed in the Jingping Ancient Village restoration project. Many of its historic residential courtyards and architectural structures have suffered significant wear and deterioration, necessitating urgent preservation efforts. The village has adopted a heritage-centric restoration approach, employing blue bricks and granite slabs to recreate an authentic historical streetscape. Furthermore, a range of cultural landscape elements has been strategically incorporated, including planting beds and green walls on deteriorated ruins, enhancing visual cohesion; seating areas along village alleys, fostering interactive and contemplative spaces; and rural-themed installations, reinforcing the cultural identity of the village. Conservation efforts should extend beyond visual restoration to encompass the preservation of intangible cultural heritage, including local customs, traditional crafts, and community lifestyles. Any modifications for tourism should be carefully assessed to enhance the village’s authenticity while safeguarding its cultural identity.

4.2.3. Landscape Enhancement Through Structured Site Planning

From a natural landscape perspective, structured site planning plays a crucial role in facilitating engaging visitor experiences [36]. The integration of thematic travel routes, such as a cycling path traversing the entire village, can enhance nature-based and agrarian tourism. Notable design elements include wisteria-covered corridors and wooden boardwalks, fostering an immersive botanical ambiance; a sea of pink muhly grass at the main entrance, creating a striking visual gateway; Golden-hued rice terraces, reflecting seasonal vibrancy and agrarian authenticity; and a terraced field restaurant, seamlessly blending into the natural contours of the landscape.

4.2.4. A Multi-Functional, Interactive Aesthetic Framework

A holistic, interactive landscape framework can be established by organically integrating point, line, and surface elements, thereby supporting a diverse array of cultural and leisure activities, including wedding photography and artistic documentation, family-oriented rural tourism experiences, nature education programs emphasizing ecological conservation, thematic rice field cycling routes, art salons, culinary experiences, boutique dining services, and reading lounges and boutique homestays, enhancing cultural immersion.

This multi-layered approach fosters an experiential “learning through leisure” model, where tourists can engage in recreational, educational, and artistic activities within a meticulously curated aesthetic environment. Furthermore, the integration of an eco-style terraced restaurant within the existing rice terraces can offer unique sensory experiences, such as stargazing from outdoor camping areas, fostering a profound connection between visitors and the natural surroundings. By synthesizing heritage preservation, landscape design, and interactive engagement, this approach ensures that traditional villages evolve as dynamic, culturally rich, and aesthetically compelling tourism destinations, securing long-term sustainability and appeal.

5. Conclusions

This study systematically evaluated tourist satisfaction with traditional village tourism landscapes by integrating the AHP and SBE under the theoretical framework of 4E theory. The findings highlight that aesthetic and escapism experiences hold the greatest significance for visitors, while entertainment and educational experiences, though less influential, remain integral components of a holistic tourism framework.

The AHP analysis revealed that tourists prioritize aesthetic appeal and escapist qualities, emphasizing the need for immersive landscapes that facilitate temporary detachment from modern life. SBE analysis, on the other hand, demonstrated that seasonal variations, stress relief, and cultural landscape diversity are key determinants of visitor satisfaction. However, deficiencies were observed in areas such as social interactivity, satisfaction with educational experiences, and fulfillment of aesthetic needs, indicating opportunities for enhancement.

The strong correlation between AHP and SBE scores (Pearson correlation coefficient = 0.867, p < 0.01) suggests that expert-driven evaluations (AHP) align significantly with visitor perceptions (SBE), underscoring the mutual complementarity of these two methodologies. The comparative analysis further revealed notable divergences in individual aesthetic evaluations, likely influenced by cultural backgrounds, personal preferences, and contextual factors at the time of assessment.

To enhance traditional village tourism experiences, targeted strategies should focus on: (1) Preserving architectural authenticity and rural landscapes while implementing contextually sensitive restorations [37]; (2) Enhancing social interactivity by incorporating structured yet organic engagement opportunities; (3) Optimizing educational experiences to align with modern tourists’ preferences for interactive and hands-on learning; and (4) Enriching aesthetic experiences through seasonal landscaping, cultural integration, and curated visual enhancements.

This study has several limitations that should be addressed in future research. First, the small sample size in the SBE method may limit the reliability and generalizability of the findings. Future studies should include a larger and more diverse participant pool to enhance statistical robustness. Second, visitor behavior data, such as dwell time, movement patterns, and activity participation were not analyzed. Integrating GPS tracking, sensor-based monitoring, or behavioral surveys could provide deeper insights into visitor engagement. Third, seasonal variations were not explicitly considered despite their potential influence on visitor satisfaction. Future research should conduct longitudinal or comparative seasonal studies to assess how environmental changes affect tourist experiences. Fourth, this study does not examine the impact of cultural backgrounds on visitor perceptions. Future research should employ cross-cultural surveys, comparative studies, and qualitative methods (e.g., interviews and focus groups) to explore these variations. Lastly, the study primarily reflects the perspectives of industry experts and tourists, with limited representation of local residents. Future research should adopt participatory approaches, including in-depth interviews, ethnographic studies, and community engagement to ensure a balanced and sustainable tourism framework that preserves both local well-being and cultural integrity.

Author Contributions

Conceptualization, L.W.; methodology, L.W.; software, L.W. and J.Z.; validation, L.W. and J.Z.; formal analysis, L.W.; investigation, L.W.; resources, L.W.; data curation, L.W. and J.Z.; writing—original draft preparation, L.W. and J.Z.; writing—review and editing, M.W.; visualization, J.Z.; supervision, M.W.; project administration, L.W. and M.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of Specialised Committee on Science Ethics, Guangzhou University NO. [2025] 024 2025-03-03.

Informed Consent Statement

Informed consent was obtained from all the subjects involved in the study.

Data Availability Statement

The original contributions presented in this study are included in the article.

Conflicts of Interest

The authors declare no conflicts of interest.

Footnotes

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Figures and Tables

Figure 1. Location map of the study area.

Figure 2. Z-value of tourist satisfaction in traditional village tourism landscape.

Table 1

Evaluation index system for traditional village tourism landscapes.

Criteria Level	Indicator Level	Indicator Description
Entertainment Experience	Diversity of Scenic Attractions	Assesses the variety and richness of entertainment-oriented scenic elements in traditional village tourism.
	Immersive Experience	Evaluates the degree of visitor immersion through landscape design, interactive facilities, and ambiance creation.
	Engagement of Young Visitors	Measures participation rates and satisfaction levels among younger tourists to assess their attraction to entertainment experiences.
	Social Interactivity	Examines the level of visitor communication and interaction during entertainment experiences.
Educational Experience	Knowledge Stimulation	Determines whether exhibitions, guided tours, and interactive experiences successfully provoke visitors’ curiosity and interest in unfamiliar knowledge.
	Skill and Method Mastery	Evaluates the extent to which visitors acquire relevant skills and techniques through educational experiences, such as traditional craftsmanship or agricultural activities.
	Knowledge Retention	Measures the depth and breadth of knowledge gained through educational activities via questionnaires and testing.
	Satisfaction with Educational Experience	Assesses visitor satisfaction concerning the quality and efficacy of educational experiences.
Aesthetic Experience	Diversity of Cultural Landscapes	Evaluates the variety and richness of cultural landscapes in traditional village tourism.
	Seasonal and Regional Characteristics	Determines the uniqueness and attractiveness of cultural landscapes across different seasons and geographic settings.
	Fulfillment of Aesthetic Needs	Measures visitor satisfaction with the aesthetic appeal of cultural landscapes.
	Agricultural Participation and Rural Authenticity	Assesses whether participation in agrarian activities fulfills visitors’ intrinsic yearning for simplicity, nostalgia, and rural authenticity.
Escapism Experience	Escape from Reality	Evaluates whether visitors genuinely detach from their everyday lives during escapism-oriented tourism experiences.
	Stress Relief and Personal Liberation	Measures the efficacy of escapist experiences in reducing psychological stress and fostering emotional release.
	Environmental Contrast	Assesses the degree to which the escapism experience setting differs from visitors’ routine environments, contributing to mental detachment and relaxation.
	Transcendence and Serenity	Evaluates whether visitors attain a profound sense of tranquility, self-reflection, and transcendence through their escapist tourism experience.

Table 2

Relative importance scale.

Scale	Meaning
1	Two factors are equally important.
3	The first factor is slightly more important than the second.
5	The first factor is significantly more important than the second.
7	The first factor is strongly more important than the second.
9	The first factor is extremely more important than the second.
2, 4, 6, 8	Intermediate values between adjacent levels.
Reciprocal Values	When comparing factors in reverse, use the reciprocal of the original comparison value.

Table 3

Random index (RI).

Order (N)	1	2	3	4	5	6	7	8	9	10	11
RI	0	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49	1.51

Table 4

AHP weight analysis and z-scores for traditional village tourism landscape satisfaction.

Criterion Level	Weight	Indicator Level	Weight	Comprehensive Weight	Consistency Check	Z-Score
Entertainment Experience (B1)	0.1611	Diversity of Scenic Attractions (C1)	0.1680	0.027065	λmax = 4.0311CI = 0.0104CR = 0.0115	0.11327
		Immersive Atmosphere Creation (C2)	0.4839	0.077956		0.37659
		Engagement of Young Visitors (C3)	0.2795	0.045027		0.25574
		Social Interactivity (C4)	0.0685	0.011035		−0.95635
Educational Experience (B2)	0.096	Knowledge Stimulation and Guidance (C5)	0.2771	0.026602	λmax = 4.0310CI = 0.0103CR = 0.0115	0.09657
		Mastery of Techniques and Methods (C6)	0.1611	0.015466		−0.22365
		Knowledge Acquisition Effectiveness (C7)	0.4658	0.044717		0.23649
		Satisfaction with Educational Experience (C8)	0.0960	0.009216		−1.85422
Aesthetic Experience (B3)	0.4658	Diversity of Cultural Landscapes (C9)	0.2755	0.128328	λmax = 4.0206CI = 0.0069CR = 0.0076	0.46283
		Seasonal and Regional Characteristics (C10)	0.4949	0.230524		0.72376
		Fulfillment of Aesthetic Needs (C11)	0.1650	0.076857		−0.32165
		Agricultural Participation and Rural Authenticity (C12)	0.0646	0.030091		0.17265
Escapism Experience (B4)	0.2771	Degree of Escape from Reality (C13)	0.3081	0.085375	λmax = 4.0325CI = 0.0108CR = 0.0120	0.40684
		Stress Relief and Personal Liberation (C14)	0.4925	0.136472		0.54162
		Environmental Contrast (C15)	0.1058	0.029317		0.124731
		Transcendence and Serenity (C16)	0.0936	0.025937		0.02674

Table 5

Linear analysis between AHP and SBE.

Model	Unstandardized Coefficients		Standardized Coefficients	T	Significance
	B	Standard Error	Beta
(Constant)	0.062	0.012		4.961	0.000
SBE	0.055	0.020	0.589	2.728	0.016

Table 6

Correlation analysis between AHP and SBE.

Variables	AHP	SBE
AHP	Pearson Correlation	1
	Significance (Two-tailed)
	Sample Size (N)	16
SBE	Pearson Correlation	0.867 (p < 0.01)
	Significance (Two-tailed)	0.000
	Sample Size (N)	16

Note: Correlation is significant at the 0.01 level (two-tailed).

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