China’s urbanization process has accelerated markedly since the reform and opening up, and urbanization research has gradually deepened. The research results of small town construction, urbanization development policy, road selection and dynamic mechanism have emerged.
Hu Jiquan (2005) combed the development process of urbanization at home and abroad and obstacles that China’s new urbanization construction may encounter, and proposed a new urbanization development direction in line with China’s national conditions  ; Peng Hongbi (2010) believed that to achieve the goal of urban-rural integration, it is necessary to strengthen the use of intensive and ecological development models based on the Scientific Outlook on Development, and build new towns with diverse functions and reasonable systems  ; Qiu Baoxing (2012) analyzed the difficulties in the process of China’s new urbanization construction, and drew on the experience of foreign urbanization, and concluded that China must change the model of new urbanization construction  .
Premier Li Keqiang pointed out at the 2013 Central Urbanization Work Conference that urbanization is the inevitable process of modernization. Promoting the urbanization process is of great significance for solving three rural issues, promoting regional coordinated development, expanding domestic demand and promoting industrial upgrading, building a well-off society in an all-round way, and accelerating socialist modernization. As a result, China’s research on urbanization began to flourish. Among them, the research on the combination of new urbanization and tourism industry has steadily increased since 2013.
Zhang Chunyan (2014) took the example of Jingshan City, Hubei Province, and constructed a coupled evaluation model and evaluation index system between tourism industry and new urbanization. She believed that vigorously promoting the development of tourism and paying attention to the role of traditional industries are very important for new urbanization  ; Zhang Xinsheng (2016) summarized the typical models of China’s tourism-driven urbanization construction and analyzed the problems existing in each model, he concluded that China’s urbanization needs to get rid of the dependence on resources and focus on factors-driven combination advantages  ; Tang Hui (2018) analyzed the connotation and characteristics of Chinese tourism characteristic towns, pointed out the problems existing in the development of tourism characteristic towns at present and put forward the suggestions for improvement  .
The concept of “Characteristic Town” was first proposed by Li Qiang, governor of Zhejiang Province, when he visited Yunqi Town in 2014, which also set off a wave of building characteristic towns in China. In 2016, the National Development and Reform Commission, the Ministry of Housing and Urban-Rural Construction, The Ministry of Finance jointly issued “the notice on the cultivation of characteristic towns” (Jiancun  No. 147), the first national policy to support characteristic towns construction  . By August 2018, there were 403 characteristic towns on the list of National Characteristic Towns, the development of Chinese tourism characteristic towns was in full swing.
Tourists’ revisit intention is of great significance to the operation and sustainable development of Chinese tourism characteristic towns. By investigating the influencing factors of tourists’ revisit intention of characteristic towns, we can find out the existing shortages and improvement spaces of the town, and combine the resources to make the town develop better. From the research of Ding Fengqin (2015), it can be concluded that the revisiting behavior of tourists plays an important role in improving the economic benefits of tourism destinations, reducing the cost of tourism, maintaining the original source of tourists and the positive reputation of tourists.  Therefore, the tourists’ revisit intention has a diversified impact on business development level of tourism destinations, and is an important reference for improving the operation status of tourism destinations. Therefore, the tourists’ revisit intention is a problem that the town operators should continue to pay attention to. At present, scholars at home and abroad have made a certain degree of research on characteristic towns, but mainly focus on the qualitative discussion.
Liu Xibin (2015) analyzed the features of characteristic towns and put forward suggestions for orderly promotion of characteristic town construction from the perspective of government  ; Zhu Yingying (2016) pointed out the problems existing in the characteristic towns construction and proposed corresponding improvement measures  , Chen Lixu (2016) studied the cultural support of characteristic towns construction  ; Chao Pei, Ding Yuan (2017) explored the connotation of the characteristic towns  ; Yi Kaigang, Li Feiqin (2017) explored the development model of characteristic towns with Zhejiang characteristic towns for example, and put forward three kinds of spatial development models  ; Fan Bin (2018) put forward suggestions on the construction of sports characteristic towns from the perspective of Embeddedness Theory  .
Although there have been a certain number of quantitative studies on characteristic towns, most quantitative studies are concerned with the construction of characteristic towns development evaluation system. Wu Yizhou (2016) used the expert consultation method to construct a characteristic town’s development level indicator system and evaluation method  . It can be seen that the current domestic scholars’ research on tourists’ revisit intention in Chinese characteristic towns is basically blank.
Gankeng Hakka Town was selected as one of the first national-level cultural and tourism characteristic towns in 2017. It relies on long Hakka history and rich culture heritage. It develops tourism on the basis of retaining the aboriginal inhabited villages, and introduced the new concept of “IP Town” to create a development model of “cultural + tourism + urbanization”. This study takes the tourists’ revisit intention of the Chinese tourism characteristic town as the research object and takes Gankeng Hakka Town as a research case. It combines field investigation, questionnaire survey, reliability test and analysis of variance method on the basis of reading and summing up the previous research. Then this study constructs and analyzes the influencing factors model of tourists’ revisit intention of Chinese tourism characteristic towns by the structural equation analysis method, and verifies the model. Finally, it put forward reasonable countermeasures for the future development of Gankeng Hakka Town.
The theoretical significance of this study is trying to explore the factors affecting the tourists’ revisit intention in the Gankeng Hakka Town from the perspective of tourists, and the mechanism among each factor, which will provide theoretical basis for subsequent research. The practical significance of this study is to explore the factors affecting tourists’ revisit intention in Gankeng Hakka Town through a combination of field surveys and questionnaire surveys. According to the survey results, the managers of Gankeng Hakka Town can clarify the future development direction of the town and improve the existing shortages of the town. On the other hand, this study provides lessons for the construction of other tourist characteristic towns in China.
2. Theoretical Basis and Research Hypothesis
2.1. Theoretical Basis
There are different definitions of tourists’ perceived value and most of the definitions are mainly based on the definition of customer value in the field of marketing. This study agrees with Morrison’s definition that tourists’ perceived value is a psychological assessment of a tourism product by an assessment of the individual’s costs and benefits.
Service quality and satisfaction has always been the core content of early tourism marketing research. In recent years, scholars believe that consumer behavior can be better understood through perceived value. In the study of the relationship between perceived value and loyalty, Grisaffe and Kumar (1998) believe that perceived value can cause customers’ recommendation behavior and repurchase behavior to a certain extent  .
Most studies on the perceived value of tourists show that tourists’ perceived value has a significant effect on tourist satisfaction and behavioral tendencies. Petrick (2001) proposed a revival determinant model, which indicated that the influencing factors of revisit intention included recreational experience, satisfaction and perceived value  .
With regard to the definition of the concept of tourist satisfaction, domestic and foreign scholars have different views. In the 1970s, American scholar Pizam proposed that tourist satisfaction is the result of tourists’ expectations of destinations and experiences at destinations. If the result of the comparison is expected to make the tourists feel satisfied, the satisfaction is positive; otherwise, the satisfaction is negative  . Scholars have empirically analyzed tourist satisfaction based on different types of tourist destinations. Most of the research results show that tourists’ satisfaction can positively influence to tourists’ revisit intention.
This paper adopts the definition of tourist motivation by scholar Li Ronggui (2009), defines tourist motivation as the psychological motivation caused by tourism demand, influenced by social concepts and norms, maintaining individual tourist behaviors and guiding behaviors to tourism goals  . In recent years, many scholars have verified that there is a comprehensive effect between tourist motivation and revisit intention. Xu Guoquan (2008) take agricultural tourism as the research object, and find that the farming experience and the appreciation of scenery factors in tourist motivation have positive correlated impact  . Qu Ying and Li Tianyuan (2010) have summarized the domestic and foreign research on tourist loyalty in the past decade, and found that tourist motivation in the literature has been used as a driving factor for tourist loyalty  .
2.2. Research Hypothesis
The purpose of this paper is to try to construct a model of the influencing factors of tourists’ revisit intention in China’s tourism characteristic towns, and to analyze its mechanism of action. At the same time, this paper explores whether tourists’ perceived value, tourist satisfaction and tourist motivation can affect tourists’ revisit intention. If it can affect, is it direct or indirect? If it is an indirect influence, what is the intermediate variable?
H1: Tourist motivation has a direct and significant impact on tourists’ perceived value.
H2: Tourist motivation has a direct and significant impact on tourist satisfaction.
H3: Tourists’ perceived value has a direct and significant impact on tourist satisfaction.
H4: Tourist satisfaction has direct and significant impact on tourists’ revisit intention.
H5: Tourists’ perceived value has direct and significant impact on tourists’ revisit intention.
H6: Tourist motivation has a direct and significant impact on tourists’ revisit intention.
H7: Tourist motivation has a significant impact on tourists’ revisit intention through tourist satisfaction.
H8: Tourist motivation has a significant impact on tourists’ revisit intention through Tourists’ perceived value.
H9: Tourists’ perceived value has a significant impact on tourists’ revisit intention through tourist satisfaction.
H10: Tourist motivation has a significant impact on tourist satisfaction through Tourists’ perceived value.
The model of influencing factors of tourists’ revisit intention is shown in Figure 1.
3.1. The Questionnaire Design
The questionnaire consists three parts: the first part is about tourist satisfaction and tourists’ revisit intention; the second part is the basic information of tourists, including age, gender, occupation, education level and income level; the third part is the tourist motivation scale and the tourists’ perceived value scale. The measurement indicators of tourist motivation and tourists’ perceived value scale mainly refer to the research achievements of Guo Anxi and Huang Fucai   , combined with the specific situation of Gankeng Hakka Town, a total of 22 measurement items were designed, and the four measurement indicators were deleted through project analysis and factor analysis (Table 2).
The questionnaire used to measure the indicators using the 5-point Likert scale, 1 means very disagree, 2 means disagree, 3 means general, 4 means consent, 5 means Very much agree.
3.2. Data Source
The data of the questionnaire mainly comes from online and offline. From June to August 2018, a total of 250 questionnaires were distributed and 211 were collected, of which 200 were valid. The effective rate of the questionnaire was 94.8%, which met the sample requirements.
3.3. Sample Description
Among the 200 valid samples collected in this survey (Table 1), males accounted for 46%, female accounted for 54%, and the proportion of men and women was basically balanced. The young (15 - 24 years old) and middle-aged (25 - 44 years old) groups make up the majority of the samples, accounting for 38.5% and 35% respectively. The educational level of the samples is mainly junior college/bachelor degree, accounting for 62.5%. The samples are mainly composed of students, white-collar workers and service staffs, accounting for 27%, 22.5% and 16% respectively. The income composition of the sample is mainly composed
Figure 1. Model of the influencing factors of tourist revisit intention.
Table 1. Demographic characteristics description.
of groups below 6000 yuan, accounting for 68%.
3.4. Analysis Method
The research conducted a test on reliability and validity of samples by Kaiser-Meyer-Olkin (KMO), Bartlett Test of Sphericity and Exploratory Factor Analysis (EFA) on SPSS 23.0. Furthermore, Amos 17.0 is used for Confirmatory Factor Analysis (CFA) to test the factor loading of the model to test whether the relationships between the test factors and the items matches the designed model. Finally, this paper evaluated the overall fitting degree of the model, tested whether the estimated model is realistic, and modified the model according to each measurement index to improve the final model.
4.1. Exploratory Factor Analysis
The results of extreme grouping method project analysis show that the items in the scale can significantly distinguish different levels of subjects (independent sample T test, p < 0.05) and the correlation with the total score of the project was significant (correlation test, p < 0.05). The overall relevance of all items in the scale to the project was greater than 0.5, which met the standard greater than 0.4. The internal structure of the gauge is good.
This paper used SPSS23.0 to explore dimensions of tourist motivation and tourists’ perceived value in Gankeng Hakka Town.KMO analysis and Bartlett test results show that the KMO of tourist motivation dimension is 0.926, and the associated probability of Bartlett test is 0.00; while the KMO of tourists’ perceived value dimension is 0.878, Bartlett test with a companion probability of 0.00, this result proves that the selected sample is very suitable for factor analysis.
This study uses principal component analysis to extract factors, the maximum-variance algorithm rotation to extract the common factors, and the common factors are selected according to the principle that feature root is greater than or equal to 1. The factor is extracted based on the factor load of 0.4, and the indicators are eliminated according to the following criteria: first, all factors with a load of less than 0.4; second, factors and indicators containing only one indicator; third, the load reached 0.4 on two or more factors at the same time. The analysis results show that there were four indicators with a load of more than 0.4 in more than two factors, which should be eliminated (Friends have not Gone, Kill Time, Excitement, and Chase Trend).
4.2. Test of Measurement Equation
The measurement equation of influencing factors of tourists’ revisit intention in Gankeng Hakka Town includes four endogenous latent variables. Measurement equation test is used to test whether each measurement index of the equation can effectively reflect each potential variable and the data fitting degree of the whole measurement equation. The AMOS 17.0 was used to conduct confirmatory factor analysis on the equation. As it can be seen from Table 2, the load of each measurement index on latent variable is between 0.554 and 0.905, and most of the loads are above 0.80. The square multiple correlation coefficient (SMC) is between 0.306 and 0.818, and the average variance extraction (AVE) of potential variables is greater than 0.500, indicating that the equation has good convergence validity, and each indicator can better measure potential variables. Most of the Cronbach coefficients of each potential variable and measurement index are over 0.800, and the combined reliability (CR) is between 0.790 and 0.955, which indicates that the reliability of measurement index is high. It can be seen from Table 2 that the AVE square roots of latent variables on diagonal lines are mostly larger than the correlation coefficients between corresponding latent variables and other latent variables, so the measurement equation has good discriminant validity. The overall fitting of the measurement equation is good, which can be seen from x2 (df = 203) = 595.902, x2/df = 2.935, all within a reasonable range.
Table 2. The test results of reliability, validity, and confirmatory factor analysis of hypothetical model.
Note: ***indicates that the significance level is less than 0.001.
4.3. Test of Structural Equation
Structural equation mainly refers to the structural relationship among the latent variables. The theoretical equation in this paper is composed of one exogenous latent variable (Tourist Motivation) and three endogenous latent variables (Tourist Satisfaction, Tourists’ Perceived Value, and Tourists’ Revisit Intention). Based on modifying the structural equation, the fitting indexes before and after revision are obtained as shown in Table 3 that CMIN/DF is 1.231, RMR is 0.047, RMSEA is 0.034, CFI is 0.993 and RFI is 0.954, which means that the overall fitness evaluation index of the equation achieves the ideal value.
As shown in Table 4, the results of the Maximum Likelihood Estimation (MLE) of the structural model indicate that: ① H5 (Tourists’ perceived value has direct and significant impact on tourists’ revisit intention.), H6 (Tourist motivation has a direct and significant impact on tourists’ revisit intention.), and H8 (Tourist motivation has a significant impact on tourist’ willingness through Tourists’ perceived value.) are invalid; ② H1 (Tourist motivation has a direct
Table 3. The test results of model fitness.
Table 4. Result of structural model and effects of the factors on tourists’ revisit intention.
Note: ***indicates that the significance level is less than 0.001.
and significant impact on tourists’ perceived value.), H2 (Tourist motivation has a direct and significant impact on tourist satisfaction.), H3 (Tourists’ perceived value has a direct and significant impact on tourist satisfaction.), H4 (Tourist satisfaction has direct and significant impact on tourists’ revisit intention.), H7 (Tourist motivation has a significant impact on tourists’ revisit intention through tourist satisfaction.), H9 (Tourists’ perceived value has a significant impact on tourist’ willingness through tourist satisfaction.) and H10 (Tourist motivation has a significant impact on tourist satisfaction through Tourists’ perceived value.) are valid.
After deleting the insignificant paths, the final model (Figure 2) is obtained. After modification, the overall fitness index of the final model is shown in Table 3. The CMIN/DF is 1.143, RMR is 0.049, RMSEA is 0.027, GFI is 0.996 and RFI is 0.957, each index meets the critical conditions, and the overall fitness degree is good.
Table 4 shows the relationship between the paths between the latent variables. Tourist motivation and tourists’ perceived value have no direct impact on the
Figure 2. Final model of the influencing factors on tourists’ revisit intention.
tourists’ revisit intention, but they can indirectly influence the tourists’ revisit intention through tourist satisfaction. In this process, the tourist satisfaction plays the role of the intermediary.
Therefore, the total standardization effect of tourist motivation on tourists’ revisit intention is 0.387 through tourists’ satisfaction; the total standardization effect of tourists’ perceived value on tourists’ revisit intention through tourist satisfaction is 0.320; the total standardization effect of tourist motivation on tourist satisfaction through tourists’ perceived value is 0.132; tourist satisfaction has a direct impact on tourists’ revisit intention, and the corresponding standardization effect is 0.984.
5. Conclusions and Discussion
In this study, the structural equation analysis method was used to construct the influencing factors model of tourists’ revisit intention in Gankeng Hakka town. The mechanism of tourist motivation, tourists’ perceived value and tourist satisfaction on tourists’ revisit intention were discussed. The following conclusions were drawn:
1) Tourist motivation and tourists’ perceived value do not directly affect tourists’ revisit intention, but indirectly through the tourist satisfaction. Tourist satisfaction plays an intermediary role in the whole mechanism and directly affects the tourists’ revisit intention. Also, tourist motivation can affect tourist satisfaction by affecting tourists’ perceived value, and ultimately affect tourists’ revisit intention.
2) Among the five measurement indexes of tourist motivation, “Increasing knowledge” has the greatest influence on tourists’ revisit intention, followed by “Relieve Stress” and “Learn about Culture”, which indicates that tourists pay more attention to the folk culture and architecture of Gankeng Hakka Town and want to have some experience different from daily life. The main visitors of the town are the staffs of the surrounding industrial parks and science parks. Their main tourism purpose is to release stress of work and life and to seek inner peace. Operator of Gankeng Hakka Town should improve the construction of science education facilities such as museums for the needs of major customers, and integrate Hakka elements into the marketing activities to increase the visitors’ experience of Hakka culture.
3) Among the five measurement indexes of tourists’ perceived value, “Rich Atmosphere” has the greatest influence on tourists’ revisit intention, followed by “Money Worth”, which indicates that tourists can enjoy authentic Hakka landscape and experience unique Hakka activities in the town, and they can also consume at a reasonable price in the town. Since the town is not fully open to the public, visitors cannot stay overnight or experience all the facilities in the town, which causes large time cost to tourists, especially those who drive by themselves, so the “Time Worth” has the least influence on tourists’ revisit intention. The town’s operator should speed up the town’s opening up and improve the construction of infrastructure such as parking lot and extend the stay time of visitors in the town to increase their tourists’ perceived value.
4) Tourist satisfaction is measured by “Satisfactory Landscape”, “Satisfactory Service” and “Satisfactory Product”, while “Satisfactory Landscape” has the greatest influence on tourists’ revisit intention, indicating that tourists most want to experience the natural customs and cultural landscape of the town. The town’s landscape layout is full of Hakka characteristics, and can attract tourists. “Satisfactory Service” has the least influence on tourists’ revisit intention, indicating that tourists are not satisfied with the overall service level of the town. Gankeng Hakka Town services mainly include the tour guide services, shopping services and town staff services, all of which will have an impact on the tourist satisfaction. Therefore, the town’s operator should strengthen the training and evaluation of the service skills of tour guides and town staffs, and strengthen the supervision of retail services quality, increase the evaluation and feedback mechanism for tourists, and timely obtain opinions and suggestions from tourists on towns and solve problems.
This study explores the relationship between tourist motivation, tourists’ perceived value, tourist satisfaction and tourists’ revisit intention in Chinese tourism characteristic towns, which has certain implications for the future research. However, there are still some limitations in this study. Firstly, the research object is only applicable to Gankeng Hakka Town, and it lacks reference for other characteristic towns. Secondly, the sample is not enough, which will affect the accuracy of the research results. Thirdly, the questionnaire indicators in this study are not detailed and specific, and need to be further subdivided and deepened in future research.
Thanks to the Guangdong Provincial Science and Technology Plan Project Study on the Development of New Cultural Formats in Guangdong-Hong Kong-Macao Greater Bay Area (Grant No. 2018A070712022) for funding this research and we feel grateful to Jinan-UF Joint Laboratory for assistance with the experiments.