Frontiers in Business, Economics and Management ISSN: 2766-824X | Vol. 14, No. 3, 2024 244 A Survey Study on the Satisfaction of Rural Tourists in Nanchong City, Sichuan Province Baosen Wang1, *, Nuttapong Jotikasthira1, a 1Program in Creative Industries Management Rattanakosin International College of Creative Entrepreneurship, Rajamangal University of Technology Rattanakosin, Nakhon Pathom 73170, Thailand *Corresponding author Email: 1463633768@qq.com, anuttapong.jot@rmutr.ac.th Abstract: This study employs field survey methods to investigate the satisfaction of tourists visiting rural areas in Nanchong City. After conducting reliability and validity tests on the collected data, the results showed that the questionnaire data met the standards for reliability and structural fit. The hypotheses set earlier were then verified, finding that individual differences among tourists have a certain impact on tourist satisfaction. Furthermore, the positive effect of perceived value on tourist satisfaction was also validated. Therefore, the factors affecting tourist satisfaction mainly stem from the inadequacies in the tourism development of Nanchong City, including the need for optimization of infrastructure and environment, relatively small development scale, single development mode, and the need for improvement in service levels. Keywords: Rural tourism; Nanchong City; Satisfaction. 1. Introduction Rural tourism is a form of tourism with villages as the backdrop, rich in natural and cultural resources, and is an important way for urban residents to experience rural life and culture. Since the development of rural tourism in Nanchong City, it has been promoting the development of related industries and cultural industries. Some industries have gradually expanded their scale, forming an orderly industry structure. In the tourism industry of Nanchong City, the catering industry features farm stays, the accommodation industry has dabbled in homestay construction, though with not significant effects, and leisure and entertainment construction has been the main focus, during which a series of facilities and places for play were established, combining rural style and modern trends, making many rural tourism spots popular destinations for Nanchong City and surrounding areas during holidays. Meanwhile, the scale of the construction of industries supporting tourism in Nanchong City is balanced, which is conducive to improving the overall satisfaction of tourists. Since the development of rural tourism in Nanchong City began, it has been promoting the development of related industries and cultural industries. Some industries gradually expanded in scale, forming an orderly industry structure. In Nanchong City's tourism industry, the catering business features farm stays, the accommodation sector has ventured into the construction of homestays, though with not very notable effects, but leisure and entertainment construction has been the main focus. During this process, a series of facilities and places for play that combine rural style with modern trends were established, many rural tourism spots have become popular choices for Nanchong City and surrounding citizens for holiday outings. At the same time, the balanced construction scale of Nanchong City's tourism supporting industries helps to improve the overall satisfaction of tourists. This paper, based on the actual situation of rural tourism in Nanchong City, China, studies the satisfaction model proposed by previous researchers (TDTS model), and proposes hypotheses to investigate the satisfaction of tourists in rural tourism in Nanchong City. According to the tourist loyalty model, the destination's scenery affects tourist satisfaction, and the tourist experience affects tourist satisfaction. Therefore, this paper proposes the following research hypotheses: H1a: The natural scenery of the destination directly and significantly affects tourist satisfaction H1b: The cultural connotation of the destination directly and significantly affects tourist satisfaction H1c: The uniqueness of the destination directly and significantly affects tourist satisfaction H1d: The richness of the destination directly and significantly affects tourist satisfaction H2a: Infrastructure construction directly and significantly affects tourist satisfaction H2b: The connotation of service quality directly and significantly affects tourist satisfaction H2c: The quality of supporting facilities directly and significantly affects tourist satisfaction 2. Theoretical Foundation American scholar Pizam (1978) proposed the tourist expectation-perception difference theory, which became the main theoretical basis for tourist satisfaction. Gronroos (1984) was among the first scholars to propose a quantitative method to build a satisfaction evaluation model, dividing dimensions into the level of attraction use, its role in display, and its impression in the public eye. The American Customer Satisfaction Model (ACSI), the Swedish Customer Satisfaction Barometer (SCCB), and the European Customer Satisfaction Index (ECSI), along with the fuzzy comprehensive evaluation analysis and grey relational theory, are the mainstream international customer satisfaction models today. Lian Yi et al. (2004), based on the six elements of the tourism industry (food, accommodation, transportation, travel, shopping, entertainment), built a causal model for the tourist destination customer satisfaction index (TDCSI) and a set of indicators for evaluating tourist destination customer satisfaction. Wang Xia et al. (2006), based on improvements to mainstream customer satisfaction models and combining 245 the characteristics of the tourism industry, constructed the Tourist Destination Satisfaction Index (TDSI) model, and validated the model using LISREL statistical software with tourists in Guilin as a case study. They divided the factors affecting satisfaction into tourist loyalty, tourist expectations, perceived quality, perceived value, verified the hypothesis of the reverse effect of tourist expectations on satisfaction, and demonstrated the model's high target reliability. The tourist loyalty model usually is based on the customer loyalty model proposed by Chi and Qu (2008), adjusted and improved in combination with the consumption characteristics of tourism products, including motivation, satisfaction-driven model; image-satisfaction driven model; and a tourist loyalty model based on tourist satisfaction and place attachment. Yoon and Uysal (2005) divided tourism motivation into push and pull factors, incorporating the intention to revisit and the tendency to recommend into the measurement of loyalty, and introduced the destination image into the model of scenic area tourist loyalty, dividing tourist satisfaction into attribute satisfaction and overall satisfaction. In 2012, Girish and Chris used the intention to revisit and recommend as two dimensions to construct and perfect a customer loyalty model based on place attachment. Jo et al. (2014), based on existing research, believed that service quality and perceived value would affect tourist satisfaction and on this basis, constructed a tourist loyalty model (see Figure 1). Figure 1. Tourist Loyalty Model This paper summarizes the achievements and related theories of domestic and foreign scholars in the field of rural tourism tourist satisfaction research, finding that although there is a large amount of research on tourist satisfaction in the field of tourism, discussions on tourist satisfaction in the development of rural tourism are relatively scarce. Therefore, this paper chooses the tourist loyalty model to analyze in-depth the development status of rural tourism in Nanchong City, to explore the factors affecting tourist satisfaction in Nanchong City, and thereby to find the crux of the issues affecting the development of rural tourism in Nanchong City. 3. Research Methods 3.1. Research Design This paper refers to the research indicator system of Jiang Bo and Zheng Honghua, dividing the research indicators into six aspects: "food, accommodation, transportation, travel, shopping, entertainment". According to related literature and the actual situation in Nanchong City, it was found that there were no relevant tourist characteristic souvenirs in shopping, thus the "shopping" factor was eliminated. Combined with the previously mentioned tourist loyalty model, this paper will construct the measurement indicators needed for the study from four aspects: destination scenery, tourist experience, tourist satisfaction, and tourist loyalty, as shown in Table 1. 3.2. Research Subjects The subjects of this survey are tourists visiting rural areas in Nanchong City, covering all age groups. The survey aimed to balance the sample selection ratio across different age groups. The questionnaire was distributed online, through on- site distribution in scenic areas, and by sending links via WeChat, QQ, and SMS. Respondents could complete the questionnaire using a computer, mobile phone, or orally on- site. The survey was conducted from July 7, 2023, to September 20, 2023. A total of 479 questionnaires were distributed, with 442 collected. After excluding incomplete and patterned responses, 408 valid responses remained, yielding a valid recovery rate of 92.3%. 3.3. Research Tools The study employed a questionnaire survey method, with the questionnaire being self-compiled. The reliability of the questionnaire was tested using Cronbach's α, which showed that most observed variables had a Cronbach's α above 0.7, with some reaching 0.802, indicating high reliability. The validity of the questionnaire was tested using the KMO and Bartlett's test of sphericity, which resulted in a KMO value of 0.807. The significance level of Bartlett's test was Sig=0.000 (less than 0.01), indicating good validity. 246 Table 1. Measurement Indicators in the Tourist Loyalty Model Level 1 Variables Level 2 Variables Evaluation Criteria Tourist Loyalty Model Destination Landscape Destination Natural Scenery (D1) Destination's Natural Scenery and Landscape Destination Cultural Connotation (D2) Destination's Cultural and Historical Architecture Destination Uniqueness (D3) Unique Products and Services of the Destination Destination Richness (D4) Activities Conducted at the Destination Tourist Experience Quality of Infrastructure Construction (D5) Destination's Environmental Protection, Public Facilities, and Signage Service Quality (D6) Transportation, Dining, and Accommodation Quality of Supporting Facilities (D7) Conditions of Homestays, Dining, and Entertainment Venues Tourist Satisfaction Satisfaction with Destination Landscape (D8) Satisfaction with the Destination's Landscape Satisfaction with Service (D9) Services Enjoyed in Dining, Accommodation, and Entertainment Activities Satisfaction with Price (D10) Satisfaction with Prices of Dining, Accommodation, and Entertainment Activities Tourist Loyalty Tourist Retention Rate (E1) - Tourist Revisit Intentions Tourist Recommendation Level (E2) - Tourist Recommendations to Friends and Family Tourist Advocacy Level (E3) - Tourist Promotion through Media 3.4. Data Collection and Analysis The collected data were analyzed and processed. Initially, the data were entered into Excel 2023, then transferred to SPSS 25.0 for organization and analysis. The survey data included categorical and continuous variables. For categorical data, based on research needs, they were divided into two categories: 0 and 1. For continuous data, dimensionless methods were applied. The methods of data analysis included descriptive analysis, which described the basic situation of tourist satisfaction; variance analysis, which explored the differences among various variables within satisfaction; and regression analysis, which analyzed the factors affecting tourist satisfaction and unearthed relevant influencing factors. 4. Results and Discussion The selection ratio of various age groups in the sample was balanced, indicating that Nanchong City's scenic spots are popular among tourists of different ages. To explore the factors affecting rural tourist satisfaction, this paper conducted a regression analysis of rural tourist satisfaction. Since the measurement variables related to rural tourist satisfaction are numerous and not of the same dimension, statistical analysis methods were used to reduce the dimensions of rural tourist satisfaction to meet the research requirements. 4.1. Variable Processing in Rural Tourist Satisfaction The index weights of various variables in rural tourist satisfaction were calculated using principal component analysis. This method transforms multiple correlated variables into a set of uncorrelated variables through coordinate transformation and calculates eigenvalues, variance contribution rates, and cumulative contribution rates to determine the number of common factors and their representation of the original data information. Based on the component matrix and eigenvalues, the linear combination of common factors was calculated, and the weights of each indicator were calculated using normalization methods. The weights of each indicator in rural tourist satisfaction were determined to be 0.246, 0.352, and 0.402, respectively. The measurement model of rural tourist satisfaction is established, and the measurement model of rural tourist satisfaction is assumed as follows. mmnnknp xxxxxxxxxY   ......................... 221122112211 Where .......21 ，， , ......21 ，， and ......21 ，， are the weights of different indicators, kxxx ...21 ，， , nxxx ...21 ，， and mxxx ...21 ，， are the variables on the weights of the corresponding indicators. Based on the weights of rural tourist satisfaction indicators, such as destination landscape satisfaction (0.246), service satisfaction (0.352), and price satisfaction (0.402), the rural tourist satisfaction measurement model was calculated: 321 402.0352.0246.0 xxxYp  Using the rural tourist satisfaction measurement model, the total index of rural tourist satisfaction was calculated, and a regression analysis was conducted to explore the factors affecting rural tourist satisfaction. 4.2. Regression Analysis of Tourist Satisfaction 4.2.1. Variable Selection Considering the many factors affecting rural tourist satisfaction, this study selected destination natural scenery, destination uniqueness, destination cultural connotation, destination richness, infrastructure quality, service quality, and supporting facility quality as independent variables. The study focused on the impact of these factors on rural tourist satisfaction, with destination landscape satisfaction, service satisfaction, and price satisfaction as dependent variables. To enhance the objectivity and accuracy of the study and reduce research errors, demographic variables such as gender and household registration were treated as control variables affecting rural satisfaction. 247 4.2.2. Model Selection Rural tourist satisfaction, composed of destination landscape satisfaction, service satisfaction, and price satisfaction, was used as the dependent variable. Changes in independent variables such as destination natural scenery, destination uniqueness, destination cultural connotation, destination richness, and infrastructure quality would result in changes in the dependent variable of rural tourist satisfaction. A linear relationship exists between independent variables like destination natural scenery, destination uniqueness, destination cultural connotation, destination richness, infrastructure quality, and the dependent variable of rural tourist satisfaction. Therefore, a multiple linear regression model was chosen:   nnxxxY ......22110 Where n ......, 21 represents regression coefficients, 0 is the constant term, and  is the error term. Additionally, given multiple influencing factors considered in this study, a stepwise regression analysis method was used. This method systematically excludes insignificant independent variables to ensure the best fitting results. 4.2.3. Regression of the Model Based on survey data, a stepwise hierarchical regression was used to statistically analyze each hypothesized independent variable (Tables 3-6). To ensure the accuracy of the regression results, collinearity diagnostics were performed on Models 1, 2, 3, and 4, calculating the Variance Inflation Factor (VIF). A VIF greater than 10 suggests potential collinearity issues. However, the VIF values in each regression equation were found to be less than 4, indicating no severe multicollinearity issues among independent variables in this study. Control variables (gender and household registration) were then entered into Regression Model 1, finding no significant correlation between tourist gender, household registration, and rural tourist satisfaction. When variables from the destination landscape, such as destination natural scenery, destination uniqueness, destination cultural connotation, and destination richness, were entered into Regression Model 2, it was found that, except for destination uniqueness significantly affecting rural tourist satisfaction, other factors did not significantly affect rural tourist satisfaction. Further, when variables related to tourist experience, such as infrastructure quality, service quality, and supporting facility quality, were entered into Regression Model 3, it was found that infrastructure quality and service quality in the tourist experience significantly affected rural tourist satisfaction, while other variables did not significantly affect rural tourist satisfaction (Table 2). Table 2. Regression Analysis of Rural Tourist Satisfaction Variables Model 1 Model 2 Model 3 Constant 0.0126*** 0.089*** 0.078*** Gender 0.024 0.025 0.031 Household Registration Distribution 0.043 0.031 0.024 Destination Natural Scenery 0 0.101** 0.051 Destination Uniqueness 0 0.066 0.152** Destination Cultural Connotation 0 0.072 0.073 Destination Richness 0 0.053 0.067 Quality of Infrastructure Construction 0 0 0.137* Service Quality 0 0 0.097* Quality of Supporting Facilities 0 0 0.068 N 408 Note: *sig<0.05, **sig<0.01, ***sig<0.001 The regression analysis reveals that within the aspects of destination landscape, the uniqueness of the destination has a direct and significant impact on tourist satisfaction, confirming hypothesis H1c, while other variables do not have a direct significant impact on tourist satisfaction, indicating that hypotheses H1a、H1b and H1d are not supported. In terms of tourist experience, both the quality of infrastructure construction and service quality have a significant impact on tourist satisfaction, supporting hypotheses H2a and H2b, whereas the quality of supporting facilities does not have a significant impact, indicating that hypothesis H2c is not supported. 5. Conclusion and Future Directions This paper, based on the actual situation in Nanchong City, modifies the existing theoretical framework and establishes an evaluation index system for assessing tourist satisfaction. By adjusting the model for rationality through structural equation modeling and conducting hypothesis testing, the data collected were subjected to reliability and validity tests, showing that the questionnaire data's credibility and structural fit meet the standards. The verification of previously established hypotheses partially confirmed that individual differences among tourists have an impact on tourist satisfaction, and perceived value has a direct positive effect on tourist satisfaction. Therefore, factors affecting tourist satisfaction mainly include the inadequacies in the tourism development of Nanchong City, such as the need for optimization of infrastructure and environment, relatively small development scale, single development mode, service level in need of improvement, and the need for better handling 248 methods under the pandemic situation. However, this study only conducted a questionnaire survey on tourist satisfaction without employing other auxiliary research methods, such as interviews with related enterprises, villagers, and village committees, to understand the evaluations and suggestions of individuals and groups related to tourism on the development of rural tourism in the area. 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