Fuzzy Comprehensive Evaluation Model of the Farmers’ Sense of Gain in the Provision of Rural Infrastructures: The Case of Tourism-Oriented Rural Areas of China

Abstract

During the rapid development of rural infrastructures in China, many important issues such as the real wishes of farmers and the proper management of the infrastructure have been overlooked, resulting in a weak sense of gain among farmers. To propose effective improvement strategies, this research aimed to explore the influence mechanism of the farmers’ sense of gain and to build a comprehensive evaluation model of farmers’ sense of gain in the provision of rural infrastructure. To achieve the above aims, we first proposed hypotheses among four constructs and selected an evaluation index for each construct. Using Structural Equation Modeling (SEM) and a questionnaire survey, we then verified the proposed hypotheses and developed a fuzzy comprehensive evaluation model of the farmers’ sense of gain in the provision of rural infrastructure. The results first showed that the content of gain and the way of gain directly affect the farmers’ sense of gain, and the governance indirectly affects the farmers’ sense of gain. Moreover, the way of gain has the greatest impact on the sense of gain, followed by the governance and the content of gain. Furthermore, the analysis results demonstrate that the developed fuzzy comprehensive evaluation model is feasible and effective in evaluating farmers’ sense of gain in the provision of rural infrastructures. The findings of this study first enrich the relevant theories of farmers’ sense of gain in the provision of rural infrastructure. The findings also provide theoretical support for the government authorities to put forward effective governance strategies for rural infrastructure from the perspective of farmers’ sense of gain.

1. Introduction

Rural infrastructure is not only the foundation for the development of various rural undertakings but also an integral part of the rural economic system [1]. Its construction and operation are closely related to farmers’ quality of life. However, owing to the urban–rural dual structure in many countries in the world, there is still a big gap between the level of rural infrastructure and that of cities, which affects the interests of farmers and restricts the sustainable development of rural areas [2,3]. Many related policies and initiatives have been proposed to narrow this gap. Taking China as an example, a new rural policy that proposes continuous improvement of the conditions of rural infrastructure for sustainable development was promulgated in 2018. It is worth noting that this policy takes the “sense of gain” as one of the important evaluation indicators. The sense of gain can reveal shortcomings in the rural infrastructure from the perspective of farmers, helping to suggest rapid and accurate improvements according to the farmers’ feelings.

Through an in-depth literature review, we found that many previous studies used satisfaction rather than sense of gain to evaluate rural infrastructure. Based on clear expectations, satisfaction can measure the gap between experience and expectations [4]. The American Customer Satisfaction Index and other statistical methods have been widely used to evaluate the condition of rural infrastructure and the satisfaction of farmers. By making some adjustments to the American Customer Satisfaction Index, Chen et al. formed an index system and evaluation model for farmers’ satisfaction with China’s rural public infrastructure and used the Interpretive Structural Model (ISM) to validate it [5]. Li et al. (2020) analyzed a satisfaction model on the internal mechanism, which reveals the relationship between expectations, perceived quality, perceived value, and satisfaction by using Structural Equation Modeling (SEM) [6].

However, the fact is that farmers do not have a clear expectation of use of the rural infrastructure. They make subjective evaluations based on their experience of use. Considering that gain can express people’s unclear expectations, the sense of gain is suitable for measuring the subjective feeling after the experience of use [7]. Several previous research studies have used the sense of gain to evaluate public services and public utilities. Gu et al. (2020) built an evaluation index system for the sense of gain from the perspectives of material and spirit and used the sense of deprivation as the opposite to supplement the evaluation of the sense of gain [7]. Wang et al. (2020) summarized the influencing factors of the sense of gain from four aspects, namely, objective factors, subjective factors, institutional factors, and practical participation factors [8]. Sun (2020) and Feng et al. (2021) carried out evaluations of the public health service [9] and the public education service [10] through the sense of gain, respectively. However, there is currently very little published research on the evaluation of the rural infrastructure from the perspective of the sense of gain.

Considering the actual needs and the current research status, there is an essential need to explore the influencing factors, influencing mechanisms, and realistic levels of farmers’ sense of gain in the provision of rural infrastructure from the subjective perspective of farmers. Therefore, this study aimed to build a comprehensive evaluation model of the farmers’ sense of gain in the construction and operation of rural infrastructure. The objectives of this study are: (1) to propose hypotheses for the influence mechanism of the farmers’ sense of gain from four perspectives, which are the social governance system, the content of gain, the way of gain, and the sense of gain through a theoretical analysis; (2) to develop an evaluation index system for the constructs in the influence mechanism through a comprehensive literature review and rural field investigations; (3) to conduct a questionnaire survey and field investigations; (4) to use Structural Equation Modeling (SEM) and AMOS software to verify the proposed hypotheses; (5) to develop a fuzzy comprehensive evaluation model of the farmers’ sense of gain in the provision of rural infrastructure. The research results could contribute to the body of knowledge of the evaluation of the farmers’ sense of gain and the theories of sustainable development of rural areas. Moreover, the proposed model can help to efficiently discover deficiencies in the provision of rural infrastructure and provide theoretical support for putting forward targeted improvement strategies for the sustainable development of rural infrastructure.

The rest of this paper is organized as follows. In Section 2, hypotheses of the influence mechanism of the sense of gain are proposed based on a comprehensive literature review. In Section 3, the construction of the evaluation index system, fuzzy comprehensive evaluation model, and data collection are presented. In Section 4, the proposed hypotheses and the proposed comprehensive evaluation model are validated using the collected data. In Section 5, a thorough discussion of the main findings is conducted. In Section 6, main conclusions, practical implications, originality, limitations of this study, and suggestions for future research are presented.

2. Literature Review and Hypothesis Development

2.1. Influence Mechanism of the Sense of Gain

The sense of gain is a compound word, which is synthesized through the two morphemes of “gain” and “sense”. From an objective perspective, “gain” is used to describe material and non-material acquisitions. From a subjective perspective, “sense” is used to describe the direct reflection of the objective things in the human brain through subjective perception. Therefore, the sense of gain includes both objective acquisition and subjective feeling. The objective acquisition is acquired by individuals in social development [11]. The subjective feeling is a kind of feeling about whether life has become better or not based on the objective acquisition [12]. In addition, the sense of loss and relative deprivation are the opposite of the sense of gain and harm the sense of gain [13].

The sense of gain also reflects fairness and justice during the acquisition and distribution of social resources [8]. The sense of gain refers to the subjective identification of benefits for people in all aspects of life in the process of reform and development [8]. Rural infrastructure is an important social resource for the development of rural areas. It is also called social overhead capital, which is invested before any other investments [14]. The acquisition and use of the rural infrastructure reflect that farmers get the benefits that they deserve in social development [15]. If the farmers subjectively approve of the benefits, they will have a sense of gain.

According to a comprehensive literature review of the research on the sense of gain, this study proposes a conceptual framework of the influencing mechanism of the sense of gain, as shown in Figure 1. Because the sense of gain includes both objective acquisition and subjective feeling, the sense of gain has a positive association with the content of gain, the social governance system, and the way of gain [16]. As the basis of the sense of gain, the content of gain is a subjective evaluation based on what people have acquired [17]. The social governance system and the way of gain provide conditions and driving forces for the sense of gain [18]. By adopting SEM, this study used this model as a basic structural model in SEM.

The evaluation of the content of gain refers to the farmers’ subjective feelings of whether the rural infrastructure meets their needs [19]. According to Maslow and Lewis’s (1987) theory of human motivation, human needs include basic needs, psychological needs, and self-fulfillment needs [20]. Therefore, the content of gain mainly comes from the satisfaction of two aspects of needs. One aspect is that the material environment provided by the rural infrastructure meets farmers’ needs in terms of daily life and work such as electricity supply, convenient roads, and clean water. Another aspect is that the farmers’ needs for self-actualization and respect are satisfied during the provision of rural infrastructure [21]. For example, relevant suggestions for infrastructure development have been adopted. The subjective cognitive evaluation of the satisfaction of multiple needs represents the farmers approving of the content of gain, leading to the sense of gain.

The evaluation of the social governance system refers to the farmers’ subjective feelings of a fair and just governance system and a good social trust atmosphere. With the development of rural infrastructure, there are more and more highly concerning issues about the distribution of resources and interests. For example, farmers are highly concerned about whether compensation for land acquisition is fair. If such issues cannot be solved fairly and justly, the farmers’ sense of gain will be harmed because of the feeling of unreasonable distribution of benefits [22]. In addition, a social trust atmosphere makes farmers believe that there is no corruption in the process of infrastructure development [23]. The government does its due diligence in high-quality infrastructure construction and management and guarantees the interests of farmers, leading to a high level of the farmers’ sense of gain.

The evaluation of the way of gain refers to the farmers’ subjective feelings of whether they can participate in the improvement of the content of gain through multiple channels. In the process of meeting needs, individuals will have a better subjective experience if they can give full play to their ability and participate in social development [24]. When people are satisfied through their efforts, people will show more positive emotions [25]. Such positive emotions brought about by people’s hard work and satisfying their needs will enhance the sense of gain. For example, in the planning stage of a village road infrastructure, the village committee actively solicits farmers’ opinions and adopts reasonable suggestions, leading to an increase in the farmers’ sense of gain.

The sense of gain describes the farmers’ emotional experience generated by being satisfied by the content of gain through the way of gain under a good governance system. An individual will have a positive emotional experience when they obtain the content of gain by the convenient way of gain when the governance is fair and just. If the case is the opposite, an individual will have a negative emotional experience. The sense of gain is evaluated by the level of positive or negative emotional experience.

2.2. Definition of the Evaluation Boundary

Before any evaluation, it is necessary to define the boundary of the evaluation including the object, subject, and the time point of the evaluation. The evaluation object of the farmers’ sense of gain in the provision of rural infrastructure highly depends on the type of rural area and the level of regional development.

On the one hand, different types of rural areas have different emphases in the construction and operation of rural infrastructure. Rural areas can be divided into agriculture-oriented rural areas, industry-oriented rural areas, tourism-oriented rural areas, and life-oriented rural areas [26]. Although there is no unified standard for the classification of infrastructure in the world, infrastructure is generally divided into productive infrastructure, social infrastructure, and ecological infrastructure [27,28]. Moreover, with increasing concern about the environment, ecological infrastructure has been paid more attention [29]. Different types of rural areas have different focuses in the provision of infrastructure. For example, agricultural-oriented rural areas and industrial-oriented rural areas place more emphasis on productive infrastructures such as irrigation facilities, power supply facilities, roads, bridges, ports, and similar transportation facilities. These infrastructures are specifically aimed at supporting direct productive activities. Tourism-oriented rural areas emphasize not only productive infrastructures but also social infrastructures and ecological infrastructures [30]. Tourism-oriented rural areas have to not only keep the basic lifestyle and local characteristics of the area but also provide convenient infrastructures to attract tourists for leisure, accommodation, and consumption. Therefore, the infrastructure of tourism-oriented rural areas usually includes a higher level of economic infrastructure, ecological infrastructure, and social infrastructure, which is very comprehensive. Therefore, this study aims to evaluate farmers’ sense of gain regarding infrastructure in tourism-oriented rural areas.

On the other hand, the levels of rural infrastructure show large differences in regions with different levels of social and economic development [31]. Taking Germany as an example, the development of the western region is generally better than that of the eastern region [32]. Research on farmers’ sense of gain regarding rural infrastructure in regions with a higher level of development can provide more sufficient experience and enlightenment. In addition, regions with a higher level of development have ample funds for developing tourism-oriented rural areas. In light of the above, in this study we have selected tourism-oriented rural areas in the region of eastern China.

The evaluation subject for the sense of gain is the farmers who live and work in various types of rural areas. They convey their sense of gain by assessing their subjective feelings when using the rural infrastructure and being affected by the rural infrastructure. From the perspective of the entire life cycle, there are many stages during the construction and operation of rural infrastructure. This study chose the planning and decision-making stage and the operation stage as the evaluation time point for the sense of gain. It is well known that the planning and decision-making stage of the development of infrastructure has the greatest impact on the function of the infrastructure; the operation stage of the infrastructure determines whether the infrastructure can perform the expected role. Moreover, the construction of rural infrastructure has generally been carried out in a mature market-oriented way, such as selecting contractors through bidding and using supervision enterprises to manage the construction. Furthermore, the long-time top-down government-led development mechanism of rural infrastructure easily ignores the important position of farmers in the use of the rural infrastructure and overlooks the farmers’ real needs for the infrastructure in the planning and decision-making stage [33]. In addition, there is a common problem in rural infrastructure in which the government pays more attention to the construction of the rural infrastructure but neglects its operation and management. Some rural infrastructures are not sufficiently managed and maintained after completion because there is a lack of professional operation teams and proper regular operation [34]. These problems cause the farmers’ sense of gain to remain at a low level in the provision of rural infrastructure.

3. Methodology and Data Presentation

As a subjective feeling, the sense of gain has a certain degree of ambiguity. In addition, it is difficult to strictly divide the boundaries of different levels of sense of gain. These points make it difficult to directly measure the sense of gain accurately. Therefore, this study adopts a fuzzy comprehensive evaluation method to deal with the fuzzy problems in the evaluation based on deconstructing the influence mechanism of sense of gain. In 1965, Zadeh (1996) proposed the “fuzzy set”, which was used as a quantitative description method to describe the problem of unclear boundaries or unclear characters [35]. Fuzzy set-based methods provide a means of solving the uncertainty problem caused by the independence of indicators and the fuzziness of evaluation [36,37]. Based on fuzzy mathematics, fuzzy linear transformation, and the maximum membership principle, the fuzzy comprehensive evaluation uses the membership function to transform uncertainty into certainty and quantify the fuzzy problem [38]. Fuzzy comprehensive evaluation has been used in many decision support problems [39,40]. According to the objectives of this study and the theory of fuzzy comprehensive evaluation, this study first constructs an evaluation index system, and then six steps are proposed to realize the fuzzy comprehensive evaluation of farmers’ sense of gain in the provision of rural infrastructure.

3.1. Construction of Evaluation Index System

According to the objective of this study, a comprehensive evaluation index system is first constructed, as shown in

Table 1. Evaluation index system for the farmers’ sense of gain.

Objective Variable	Latent Variable	Observable Variable	Code	Reference
Sense of gain (U)	Content of gain (U1)	The construction and operation of infrastructure have increased your income.	U11	Zhang [41]
		The construction and operation of infrastructure have provided more employment opportunities, such as selling goods and opening restaurants.	U12	Yang [42]
		The highways and roads are in good condition, making travel more convenient.	U13	Zhu et al. [43]
		The power supply is in good condition, and the voltage is stable and uninterruptible.	U14	Du et al. [44]
		The tap water supply is stable, and the water quality is good, free of foreign objects and odors.	U15	Zhang et al. [45]
		The garbage is cleaned up in a centralized manner and treated regularly.	U16	Zhang [46]
		The drainage of rainwater and sewage is in good condition so that there is no accumulation of rainwater and sewage.	U17	Ma et al. [47]
		The network is fully covered, and the internet speed is high.	U18	Cui and Feng [48]
		The village has cleaners to clean, and the village is clean and tidy.	U19	Feng [49]
		The greenery and natural environment of the village are in good condition.	U110	Li [50]
		There are rural hospitals or other medical institutions in the village that provide basic curative and preventative health services.	U111	Yang and Wang [51]
		There are kindergartens and primary schools nearby to meet the educational needs of children.	U112	Zhang and Liang [52]
		There are places and facilities for recreation, fitness, and exercise in the rural area to meet your needs.	U113	Yang et al. [53]
	Governance (U2)	In the construction and operation of infrastructure, village-level officials and members of the village committee work diligently to solve practical problems.	U21	SPRR 1, 2016 to 2021 DNO 2
		In the construction and operation of infrastructure, there is no problem of abuse of power and corruption.	U22	SPRR 1, 2016 to 2021 DNO 2
		In the construction and operation of infrastructure, there is no interference or occupation by gang crime.	U23	SPRR 1, 2016 to 2021 DNO 2
	Way of gain (U3)	In the construction and operation of infrastructure, local residents have opportunities to cooperate with enterprises, and the cooperation is good.	U31	SPRR 1, 2016 to 2021 DNO 2
		In the construction and operation of infrastructure, local residents participate in the negotiation of major issues.	U32	2017 to 2021 DNO 2
		In the construction and operation of infrastructure, residents put forward their own needs and opinions.	U33	2016 to 2018, 2020, 2021 DNO 2

¹ SPRR denotes the strategic plans for rural revitalization; ² DNO denotes Document No. 1 of the People’s Republic of China.

. The index system is based on the conceptual framework of the influencing mechanism of the farmers’ sense of gain. The index system considers the overall objective of sense of gain ($U$), which is determined by three major indexes, namely, the content of gain ($U_1$), the governance ($U_2$), and the way of gain ($U_3$), i.e., $U=f (U_1,U_2,U_3)$.

In this study, we took three steps to select and finalize the indictors for the index system. The selection of the indicators considered measurability, reliability, and sufficiency. First, we sorted and reviewed a large number of relevant literature and policy documents related to rural infrastructure and selected the primary indicators. In terms of the indicators for the content of gain, we conducted a comprehensive literature review using the keyword “rural infrastructure” in the journal database of China National Knowledge Infrastructure (CNKI) and Web of Science and found 44 relevant papers published after 2011. These papers cover all kinds of infrastructure, including productive infrastructures, social infrastructures, and ecological infrastructures. We selected 13 representative indicators from these papers.

In terms of the indicators for the governance and the way of gain, there is not much related academic research. However, relevant indicators are continuously mentioned in many relevant policy documents for many years and appear relatively concentrated. The policy documents include the strategic plans for rural revitalization and Document No.1 of the People’s Republic of China from 2016 to 2021. We selected three indicators for governance, which appear in all policy documents and focus on a fair and just social atmosphere. We also selected three indicators for the way of gain with a 70% frequency of occurrence in all policy documents, and we aimed to describe whether farmers have effective ways to express their needs and opinions. The sense of gain was measured by asking farmers the following question: Considering the content of gain, the social governance system, and the way of gain comprehensively, what is your level of sense of gain?

Second, to make the indicators more suitable for the actual situation of tourism-oriented rural areas in eastern China, we conducted a field survey in Jiangning District, Nanjing City, Jiangsu Province of China from March to April 2021. Some unsuitable indicators were deleted or modified. For example, after the field survey, it was found that the extent of agriculture in tourism-oriented rural areas was small and did not require irrigation systems, so the indicators related to irrigation systems were removed. Third, the index system and the indicators were modified and finalized after several discussions with five experts. The experts have conducted many years of research on rural development or have participated in relevant rural construction and management practices.

3.2. Fuzzy Comprehensive Evaluation Model

3.2.1. Establishment of Evaluation Factor Set

Evaluation factor set refers to variables that are used to evaluate. In the evaluation factor set $U=\left\{U_1,U_2,\dots U_i\right\}$, $U_i$ is the latent variable $i$ that was proposed in this study in Table 1. For each latent variable, there are corresponding observable variables. Therefore, the evaluation factor set for each latent variable is $U_i=\left\{U_{i1},U_{i2},\dots U_{ik}\right\}$ where $U_{ik}$ is the specific observable variable $k$ in the latent variable set $i$.

It is worth noting that not all latent variables and observable variables in Table 1 are in the evaluation factor set. This study conducted a questionnaire survey, adopted SEM, and used the software AMOS 24 to further screen the evaluation variables. Only variables meeting the requirements of the measurement model and those of the structural model were selected for the judgment factor set. For observable variables, factor loadings should be greater than 0.5; for latent variables, the p-value of a path to the sense of gain should be less than 0.1, indicating a significant relationship.

3.2.2. Determination of Weight Coefficient Vector

The weight coefficient vector of latent variables is $W=\left\{W_1,W_2,\dots W_i\right\}$ where $W_1+W_2+\dots W_i=1$ represents the importance of each latent variable. The weight coefficients of latent variables are determined by the path relationships and standardized path coefficients in the developed SEM model. The weight coefficient vector of observable variables for each latent variable is $W_i=\left\{w_{i1},w_{i2},\dots w_{ik}\right\}$ where $w_{i1}+w_{i2}+\dots w_{ik}=1$. The weight coefficients of observable variables are determined by the factor loading coefficients. The relative importance of the latent variables or observable variables is expressed as a normalized value according to the results in the developed SEM [54].

3.2.3. Establishment of Evaluation Grade Set

Evaluation grade set is the big difference between fuzzy comprehensive evaluation and other evaluation methods. To have discrimination, the evaluation result is generally divided into 3–7 grades. Because of the fuzziness of the evaluation, fuzzy comprehensive evaluation measures the evaluation results of each observable variable by the membership to each evaluation grade. The fuzzy evaluation grade set is represented as $V=\left\{v_1,v_2,\dots ,v_j\right\}$ where $v_j$ is the evaluation grade $j$. In this study, the evaluation was conducted through a questionnaire survey and measured by using a 7-point Likert scale.

3.2.4. Determination of Fuzzy Membership Matrix

The fuzzy membership matrix $R_i$ is represented as follows:

$$R_i=\left[\begin{array}{c}{r}_{i11} \dots r_{i1j}\\ ⋮ \ddots ⋮\\ r_{ik1} \dots r_{ikj}\end{array}\right],\left(0\le r_{ikj}\le 1\right)$$

(1)

where $r_{ikj}$ indicates the membership of the evaluation results for the specific observable variable $k$ in the latent variable set $i$ to evaluation grade $j$. In this study, the membership to each evaluation grade was determined by the proportion of people who selected the evaluation grade.

3.2.5. Calculation of Evaluation Fuzzy Vector

The evaluation fuzzy vector is calculated through the commonly used fuzzy operator $M\left(•,+\right)$, which is a linear weighted method retaining all information of each indicator. The evaluation fuzzy vector $B=\left\{B_1,B_2,\dots B_i\right\}$ is the product of the weight coefficient vector and the fuzzy membership matrix. The evaluation fuzzy vector $B_i$ for the latent variable $i$ is calculated using the following equation.

$$B_i=W_i\circ R_i=\left[w_{i1},w_{i2},\dots w_{ik}\right]\circ \left[\begin{array}{c}{r}_{i11} \dots r_{i1j}\\ ⋮ \ddots ⋮\\ r_{ik1} \dots r_{ikj}\end{array}\right]=\left[b_{i1},b_{i2},\dots ,b_{ij}\right]$$

(2)

3.2.6. Defuzzification of Evaluation Results

The final comprehensive evaluation results are dependent on the evaluation fuzzy vector. There are two commonly used methods to conduct the defuzzification of the evaluation results, namely the maximum membership principle method and the weighted average method. In the maximum membership principle method, the final evaluation result is assigned to the grade with the maximum membership value. In the weighted average method, the final evaluation result is determined by the product of the evaluation fuzzy vector and the evaluation grade vector. This study adopted the weighted average method to conduct the defuzzification of the evaluation results. The final evaluation results for the latent variable $i$ and the objective variable were calculated using the following equations.

$$X_i=B_i\circ V^{{\rm T}}$$

(3)

$$X={\displaystyle \sum }_{i=1}^m{W}_i\cdot B_i$$

(4)

In the above equation, $X_i$ and $X$ indicate the final evaluation results for the latent variable $i$ and that for the objective variable, respectively; $m$ is the number of latent variables.

3.3. Data Collection and Presentation

In this study, we conducted questionnaire surveys to collect data and validate the effectiveness of the proposed model. The main body of the questionnaire is divided into two parts. The first part aims to collect the basic information of the respondents. The second part aims to solicit the opinions of the respondents on the measurement variables proposed in Table 1. The evaluation of the measurement variables uses a 7-point Likert scale (1 = “strongly disagree” and 7 = “strongly agree”). Because the granularity of the 7-point Likert scale is finer, the collected data more easily obey the multivariate normal distribution, which is suitable for the parameter estimation of the SEM by the maximum likelihood method [55].

In this study, we conducted the questionnaire survey in seven tourism-oriented rural villages in Jiangning District, Nanjing City, Jiangsu Province, China from March to April 2021. Jiangsu Province is one of the most important provinces in the eastern region of China. According to statistical data from the National Bureau of Statistics of China, the total population of Jiangsu Province ranked fourth in 2020 in China; the rural population of Jiangsu Province accounts for 59.5% of the total population of the province. The revitalization of villages and the development of rural infrastructures are at the forefront in China. The completion rate of beautiful and livable villages of Nanjing City, which is the provincial capital, takes the lead in the whole province. Nanjing’s tourism-led rural development is potentially a benchmark for other regions.

When selecting the villages, we first searched village information from the internet. After comparing the types and development levels of the searched villages, we selected seven tourism-oriented villages located in Nanjing City with different development types and levels of development. Datangjin Village has a population of 166 and is mainly engaged in eco-tourism agriculture such as lavender fields. Donglong Village has a population of about 350 people and is mainly engaged in agricultural experience services such as strawberry picking. Paifang Village has a population of about 200 people and mainly develops tourism through the tea production process. Qianjiadu Village has a population of about 117 people and develops tourism mainly by scenic sightseeing. Lijia Village has a population of about 400 and attracts tourists with its ancient buildings and amusement parks. Zhou Village has a population of 142 people, and the tourism development is mainly based on local delicacies. Xujiayuan Village has a population of 142 and is the main destination for picnics.

The number of questionnaires is determined by the SEM’s requirements for the number of samples. The SEM requires that the number of samples is at least 5 times the number of indicators to ensure reliable results [56]. Therefore, at least 100 samples should be collected because the study initially had 20 indicators. In reality, 120 questionnaires were sent out face-to-face to avoid invalid responses and based on a conservative estimate of the return rate. The research unit is a farmer. When conducting the face-to-face survey, the number of surveys is in a similar proportion to the population of each village. In each village, the farmers were randomly selected. The research team explained the purpose of this research to each farmer and gave introductions to help the farmers to answer the questions. Finally, 117 responses were received, representing a response rate of 97.5%. Among the returned questionnaires, 107 questionnaires were valid and were used in this study. The profiles of the 107 samples are shown in

Table 2. Descriptive analysis results.

Item	Measurement	Frequency	Percentage
Gender	Male	50	46.73%
	Female	57	53.27%
Age	20–35	15	14.02%
	36–50	38	35.51%
	51–65	34	31.78%
	>65	20	18.69%
Political ideology	Members of the Communist Party of China	6	5.61%
	Members of the Chinese Communist Youth League	2	1.87%
	Nonpartisan	99	92.52%

.

The results show that the proportion of women is slightly higher than that of men, which is related to the larger number of male migrant workers in cities. All age groups account for more than 10% of the population, which can fully reflect reality. The proportion of the two age groups 36–50 and 51–65 years old is higher than that of the two age groups 20–35 and over 65 years old. This is consistent with the reality of the serious aging problem in rural villages. Most people are without party affiliation, while members of the Communist Party of China and the Chinese Communist Youth League accounted for a very small proportion. The data are representative because the information of the comprehensive samples is consistent with the situation of tourism-oriented rural areas.

4. Model Evaluation and Comprehensive Evaluation Results

4.1. Model Evaluation

Partial least squares structural equation modeling (PLS-SEM) was selected because it can cope with a non-normal data set and small sample size. The software application used to analyze the data was AMOS 24.

4.1.1. Measurement Model Evaluation

Table 3. Summary of the factor analysis results.

Evaluation Criteria		Value
Cronbach α		0.820
KMO		0.686
Bartlett’s test of sphericity	chi-square	919.907
	df	171
	p-value	0.000

and

Table 4. Measurement model evaluation.

Latent Variable	Observable Variable	Significance Estimate			Std. after Adjustment	CR	AVE
		Unstd. 1	p	Std. 2
Content of gain	U12	1.111	0.000	0.544	0.533	0.838	0.433
	U15	1.057	0.000	0.546	0.511
	U16	0.774	0.000	0.685	0.660
	U17	1.087	0.000	0.747	0.720
	U19	1.197	0.000	0.778	0.814
	U110	1.359	0.000	0.727	0.781
	U113	1.000	—	0.522	0.511
Governance	U21	1.323	0.000	0.735	0.730	0.647	0.384
	U22	0.920	0.000	0.578	0.577
	U23	1.000	—	0.525	0.534
Way of gain	U32	0.859	0.000	0.828	0.770	0.864	0.763
	U33	1.000	—	0.893	0.966

¹ Unstd. denotes unstandardized estimates; ² Std. denotes standardized estimates.

show the evaluation results of the measurement models. The measurement model evaluation aims to determine whether the indicators match or accurately reflect the evaluation target. Table 3 shows that Cronbach α > 0.8, KMO > 0.6, and p-value < 0.05. The results indicate that the collected data are suitable for measurement model evaluation with factor loading.

This study selected factor loading to analyze the measurement model. When the factor loading of an observable variable is less than 0.5, the observable variable cannot reflect the corresponding latent variable, and the variable should be deleted [57]. After confirmatory factor analysis, this study deleted observable variables with factor loadings less than 0.50 including $U_{11}, U_{13},U_{14},U_{18},U_{111},U_{112}, and U_{31}$. Table 4 shows that the values of composite reliability (CR) are over 0.6, ensuring the reliability of internal observable variables within each latent variable [54]. The values of the average variance extracted (AVE) are greater than 0.36, ensuring the convergent validity of the latent variables. Overall, these results demonstrate that the measures in this study have adequate reliability and validity.

4.1.2. Structural Model Evaluation and Path Analysis

Table 5. Hypothesis testing results of the model.

Path	Estimate	S.E.	C.R.	p1
Content of gain<---Governance	0.257	0.139	1.847	*
Way of gain<---Governance	1.583	0.393	4.024	***
Content of gain<---Governance	0.268	0.165	1.629	0.103
Way of gain<---Governance	0.245	0.081	3.006	**

¹ *** p < 0.001, ** p < 0.05, * p < 0.100.

and

Table 6. Summary of the goodness-of-fit of the fitted structural model.

Measurement	Criteria	Fitted Result	Judgment
χ2/df	<3.0	1.892	Acceptable
RMSEA	<0.1	0.092	Acceptable
GFI	>0.8	0.853	Acceptable
CFI	>0.8	0.882	Acceptable
TLI	>0.8	0.851	Acceptable

, and Figure 2 present the structural model evaluations. When analyzing according to the hypotheses proposed in the conceptual framework of the influencing mechanism of the farmers’ sense of gain, some path coefficients are not significant in the unstandardized estimates because their p-values are greater than 0.100. Therefore, we sequentially removed paths with a p-value > 0.100 until the p-value of each path coefficient was less than 0.100. In this process, the two paths “the governance positive impacts the sense of gain” and “the way of gain positive impacts the content of gain” were removed because their p-values are greater than 0.100. However, the path “the content of gain positive impacts the sense of gain” was kept even though its p-value = 0.103 because the content of gain is the basis of the sense of gain. If there is no content of gain, the corresponding sense of gain will vanish. The hypothesis testing results of the model are shown in Table 5. The final fitted model with standardized estimates is presented in Figure 2. In Figure 2, e1–e12 is error; e13–e15 is disturbance. They all refer to the unexplained variance of a variable when estimated by other variables.

The results of the goodness-of-fit of the fitted structural model as shown in Table 6 determine that the model is statistically valid. For overall model fit assessment, the examined chi-square with the degree of freedom (χ²/df) is below three, which is acceptable [58]; the root mean square error of approximation (RMSEA = 0.092) is below the acceptable value 0.1, confirming the validity and significance of the model [59]. The reason why the RMSEA is not below 0.8 is most probably because of the small sample size of this study [60]. For component fit assessment, the goodness-of-fit index (GFI = 0.853) and the comparative fit index (CFI = 0.882) are above the acceptable value of 0.8. Overall, these results indicate the model fit is acceptable.

4.2. Index Weight Coefficients

According to the results in the fitted model, the content of gain (path coefficient = 0.17) and way of gain (path coefficient = 0.31) directly affect the farmers’ sense of gain. Moreover, the governance indirectly affects the farmers’ sense of gain through its influence on the content of gain (path coefficient = 0.25) and its influence on the way of gain (path coefficient = 0.74). There is a supposed path between the governance and the sense of gain. The weight coefficients of the three latent variables are determined by normalizing the path coefficients. The weight coefficients of the indicators, which are observable variables, are determined by normalizing the factor loading. A summary of the weight coefficients of the index system is shown in

Table 7. Summary of the index weight coefficients.

Latent Variable	Observable Variable	Factor Loading	Normalized Weight Coefficient	Weight of Latent Variable	Overall Weight of Observable Variable
Content of gain	U12	0.530	0.117	0.218	0.025
	U15	0.518	0.114		0.025
	U16	0.664	0.146		0.032
	U17	0.726	0.160		0.035
	U19	0.808	0.178		0.039
	U110	0.776	0.171		0.037
	U113	0.515	0.114		0.025
Governance	U21	0.728	0.398	0.366	0.146
	U22	0.579	0.317		0.116
	U23	0.521	0.285		0.104
Way of gain	U32	0.795	0.460	0.416	0.191
	U33	0.935	0.540		0.225

. The index weight coefficients are assigned to the weight coefficient vector (W).

4.3. Comprehensive Evaluation Results

In this study, we determined the fuzzy membership matrix $R_i$ by calculating the frequency at which each evaluation grade was selected, as shown in

Table 8. Fuzzy membership matrix.

Latent Variable	Code	Evaluation Grade
		1	2	3	4	5	6	7
Content of gain	U12	0.150	0.402	0.206	0.187	0.056	0.000	0.000
	U15	0.467	0.383	0.037	0.075	0.037	0.000	0.000
	U16	0.701	0.262	0.019	0.019	0.000	0.000	0.000
	U17	0.477	0.402	0.093	0.019	0.009	0.000	0.000
	U19	0.430	0.439	0.065	0.065	0.000	0.000	0.000
	U110	0.336	0.486	0.065	0.075	0.037	0.000	0.000
	U113	0.056	0.411	0.252	0.243	0.019	0.019	0.000
Governance	U21	0.019	0.093	0.383	0.411	0.047	0.047	0.000
	U22	0.028	0.103	0.449	0.374	0.037	0.009	0.000
	U23	0.093	0.103	0.374	0.393	0.028	0.009	0.000
Way of gain	U32	0.000	0.271	0.290	0.280	0.121	0.037	0.000
	U33	0.056	0.364	0.346	0.103	0.084	0.047	0.000

. The evaluation grade vector is assigned as $V=\left\{1,2,3,4,5,6,7\right\}$.

Based on the index weight coefficients, the fuzzy membership matrix, and the evaluation grade set, we calculated the results of the fuzzy comprehensive evaluation of the farmers’ sense of gain in the provision of rural infrastructure in tourism-oriented rural areas. The evaluation scores of the governance, the content of gain, the way of gain, and the sense of gain are 4.641, 6.045, 4.868, and 5.041, respectively.

5. Discussion

This research verified the influencing mechanism of farmers’ sense of gain regarding rural infrastructure by using SEM. The results indicate that: (1) the content of gain and the way of gain directly affect the farmers’ sense of gain; (2) the governance indirectly affects the farmers’ sense of gain; (3) the way of gain has no obvious relationship with the content of gain. These conclusions are consistent with the current situation of infrastructure construction and management in tourism-oriented rural areas in eastern China and have many theoretical and practical implications.

The conclusion that the content of gain and the way of gain directly affect the farmers’ sense of gain can be regarded as common sense. However, the influence of governance has many implications. First, the indirect impact of governance on the farmers’ sense of gain most probably implies that it is difficult for farmers to directly know the quality of the governance or that it is not a matter of daily concern to farmers. However, the quality of the governance is reflected in the content of gain and the way of gain [61]. For example, farmers do not know whether there is corruption in the construction and operation of rural infrastructure, but they can make a judgment about it based on whether their proposals are responded to and the final status of the completed infrastructure. From this perspective, the government should consider proposing or adding some ways to let farmers know about the status, achievements, or breakthroughs of the governance, reducing the sense of distance from farmers [62]. Second, the conclusion that governance does not directly affect the farmers’ sense of gain seems to be inconsistent with some studies confirming that governance affects happiness [63]. However, this verifies the difference between sense of gain and happiness, in which sense of gain is based on the perception of objective gain while happiness is a more subjective feeling. From a theoretical perspective, research on farmers’ sense of gain should not be confused with that of happiness.

In terms of the insignificant relationship between the way of gain and the content of gain, the top-down government-led development mechanism of rural infrastructure is well reflected. Currently, the government determines whether to build infrastructure and what kinds of or what level of infrastructure to build. The government has not made much progress in consulting farmers, and farmers’ opinions are used more for reference [33,64]. Cases of successful development of villages and rural infrastructures, such as in Japan and Germany, show that encouraging farmers to participate and leveraging the joint efforts of the government and farmers are critical aspects [65,66]. In terms of tourism-oriented rural areas, farmers’ opinions are very important because, as the local stakeholder, they participate in the provision of tourism products and services to obtain economic income. From a practical perspective, the government should provide more channels or help to form communities-of-interest or communities-of-place to let farmers express their needs and opinions to increase their sense of gain [66].

The normalized weight coefficients of the way of gain, the governance, and the content of gain are 0.416, 0.366, and 0.218, respectively. The weight coefficients represent the level of concern among farmers about these aspects at the current stage. It is worth noting that, compared with the content of gain, the way of gain and the governance have higher weight coefficients. The highest weight coefficient for the way of gain indicates that farmers currently are very concerned about the expression of their wishes during the development of rural infrastructure [67]. The way of gain can allow farmers to have a deeper understanding of rural development and better participate in development [68]. Participation will satisfy farmers’ wishes, increase farmers’ income, and bring cohesion to farmers, so farmers believe that the way of gain is very important. During the field investigations in this study, the farmers expressed that the government needs to pay more attention to providing more channels for them to express their wishes, needs, and opinions in the provision of rural infrastructure. Good expression channels and feedback will enable farmers to better participate in the provision of rural infrastructure, thereby increasing their sense of gain [69].

The relatively high weight coefficient of governance indicates that the institutional environment and social environment in the provision of rural infrastructure are currently also very important to farmers. Through the field investigations, we found that if farmers feel that public officials are not responsible, or that there may be corruption problems, or that the social trust atmosphere is poor, they will subjectively believe that there are problems and doubt the fairness and justice during the provision of rural infrastructure even if they are enjoying good infrastructure and services [70]. Therefore, the quality of governance must be maintained at a high level. Otherwise, it may lead to the Tacitus trap where an unpopular government is hated no matter what it does and whether it is right or wrong [71].

The most striking result is that the content of gain, which is the objective basis for the sense of gain, has the lowest weight coefficient. From a theoretical perspective, this can be explained by the utility theory [72] and the theory of human motivation [20]. After many years of unremitting efforts by the government, the infrastructure supply of tourism-oriented rural areas in eastern China is relatively complete and is maintained at a relatively high level. Through the field investigations of this study, we found that farmers are generally satisfied with the good rural infrastructure and related services. A diminishing marginal effect of the content of gain and decreasing utilities of the farmers have appeared. Farmers do not pay so much attention to material acquisition and begin to prioritize psychological needs and self-fulfillment needs. From a practical perspective, the government should maintain the existing infrastructure level and improve relevant services.

This study set the evaluation grading as 1 point to 7 points, where 7 is the highest level and 4 is the middle level. The comprehensive evaluation results of the way of gain, the governance, the content of gain, and the sense of gain are 4.868, 4.641, 6.045, and 5.041, respectively. The evaluation results not only indicate the direction of efforts for the government in the future but also provide decision support for the government to adjust corresponding policies. The highest score of the content of gain indicates that farmers have recognized the current material achievements and related services in the provision of rural infrastructure. The field investigation verified that the results match the current situation. The relatively low score of the way of gain indicates that farmers do have difficulty in participating in the provision of rural infrastructure. During the field investigation, we found that issues such as a lack of channels through which to participate, being unable to participate, or receiving no explanations for not adopting farmers’ opinions were frequently mentioned. The lowest score of governance indicates that farmers’ feelings are positive but not particularly strong. During the field investigation, it was found that the farmers generally believe that they are in a good social atmosphere. However, there are still problems of corruption and loopholes in social security, etc. The scores of the sense of gain and the influencing factors all demonstrate that the government should pay more attention to the way of gain and the governance, which can contribute more to the improvement of the sense of gain. Research on rural areas in other countries also confirms the importance of farmers’ participation [73,74]. Similarly, governance for rural revitalization and sustainable development has also been verified by researchers in many countries [75,76]. The positive role of specific infrastructure construction has been proved by many studies, such as the maintenance of rural vitality by schools [77].

6. Conclusions and Recommendations

This research explored the influence mechanism of farmers’ sense of gain, developing and verifying a fuzzy comprehensive evaluation model of farmers’ sense of gain in the provision of rural infrastructure. The results first showed that content of gain and the way of gain directly affect the farmers’ sense of gain, and that governance indirectly affects the farmers’ sense of gain. Moreover, the way of gain has the greatest impact on the sense of gain, followed by governance and the content of gain. Furthermore, the analysis results demonstrated that the developed fuzzy comprehensive evaluation model is feasible and effective in evaluating farmers’ sense of gain in the provision of rural infrastructure. It can be extrapolated from the results that in tourism-oriented rural areas with better economic development, in order to improve farmers’ sense of gain, the government should not only provide basic infrastructure needed by farmers but also improve the level of farmers’ participation in governance.

In terms of originality, this study evaluates infrastructure provision from the perspective of farmers’ sense of gain rather than civil engineering. It is original to evaluate the efficiency and effect of infrastructure provision from a systematic perspective by considering the content of gain, governance, and way of gain. The findings of this study first enrich the relevant theories of farmers’ sense of gain in the provision of rural infrastructure. The findings also provide theoretical support for the government authorities to put forward effective governance strategies regarding rural infrastructure from the perspective of farmers’ sense of gain. Finally, the findings help to identify the key points for the sustainable development of infrastructure.

Although the objectives were achieved, there are still some limitations. First, the sample size is limited because the questionnaire survey and field investigations were affected by COVID-19. In addition, the rural population is getting smaller and smaller because of rapid urbanization. Moreover, the findings of this study were interpreted well in the context of tourism-oriented rural areas in China, but this may be different from the contexts of other countries and other types of rural areas. In particular, when selecting evaluation indicators, researchers should make modifications of the index system according to the specific conditions of their countries or rural areas.

Nonetheless, based on the findings of this study, future research can focus on strengthening farmers’ participation and improving the governance system during the provision of rural infrastructure and construction of high-quality village projects. Moreover, the model can be tested and refined with samples from multiple countries and regions, and the situations in different countries can be compared. In addition, the question of how the combination of different types of infrastructure, governance, and the way of gain can effectively improve the sense of acquisition can also be the focus of future research.