What Is Farmers’ Level of Satisfaction under China’s Policy of Collective-Owned Commercial Construction Land Marketisation?

Abstract

The entry of collective-owned commercial construction land into the market is a major reform of China’s land management system, which will help promote the appreciation of rural land, establish a unified urban and rural construction land market, and help rural revitalisation and urban–rural integrated development. Based on the classic customer satisfaction index model, this study constructs a satisfaction model for farmers who enter the market with collective-owned commercial construction land. Farmers’ satisfaction is measured by 7 latent variables and the corresponding 22 observed variables, forming a causal chain containing 13 pairs of interactions. Taking as an example Dazu District of Chongqing City, one of the pilot areas where China’s collective-owned commercial construction land has come on the market, AMOS statistical analysis software is used to test the hypotheses. The research results show that: farmers’ information awareness has the greatest impact on farmers’ satisfaction; the higher farmers’ perceived quality is, the more they can improve their satisfaction; there is a significant relationship between farmers’ satisfaction, farmers’ complaints and farmers’ trust; and the three are closely related. Finally, based on the research results, we try to put forward targeted policy suggestions in order to provide a useful reference for government to push for the promotion of the collective-owned commercial construction land marketisation in other rural areas of China and its future improvement.

1. Introduction

Rapid economic development is driving the urbanisation process of various countries, and brings about the ever-changing relationship between people and land: the corresponding land system and national policies should also be adjusted and reformed accordingly [1,2]. In the new era of socialist development with Chinese characteristics, the implementation of the rural revitalisation strategy requires that farmers should participate in the practice of rural revitalisation and be able to enjoy the results and evaluate their effects [3], and its outcome lies in whether the policy can be implemented at the grassroots level while adhering to the main position of the farmers. The farmers’ own evaluation of agricultural and rural policies can well reflect the actual effectiveness of the policy implementation. The policy of collective-owned commercial construction land marketisation means that, on the premise that the ownership of collective land remains unchanged and in accordance with the principles of law, voluntariness, fairness, and openness, rural collectives will grant the right to use collective business construction land for a certain number of years to land users, who will pay the land grant (rent) according to the contract. Due to the complexity of the land system and the dualistic structure of land ownership [4], idle and inefficient use of collective land in China’s rural areas was common in the past, and the maximum benefit of the land was not utilised. In order to solve this problem, the national government has gradually liberalised the entry of collective business construction land into the market, setting up 33 pilot districts and counties in 2015. Additionally, the promulgation of the new Land Management Law in 2020 officially removed the legal barriers to the entry of collective business construction land into the market, giving rural construction land with a production and business nature the opportunity to enter the urban construction land market and enjoy the same rights as state land. This will not only solve a certain amount of farmers’ income problems and have a significant positive impact on sustainable livelihoods for farmers [5,6,7], but will also activate idle rural resources and enrich the realisation of the public ownership of land, which is an important innovation of the socialist land management system with Chinese characteristics. Most importantly, before the transfer and lease of the collective commercial construction land, more than two-thirds of the members or member representatives of the collective economic organisation must have approved it, otherwise the land marketisation process will be deemed to have lapsed and terminated, and this action guarantees the villagers’ right to make choices and supervise this process. This means that, in the process of promoting the policy of collective-owned land market entry, the satisfaction and support of farmers, the main body of rights and interests, will directly affect the efficiency and effectiveness of the market entry. Hence, it is important to understand the satisfaction level of farmers in the pilot areas.

The first research results on satisfaction appeared in the field of marketing, and in 1965 the American scholar Cardozo was the first to put forward the concept of customer satisfaction [8]. Later, Western scholars explored the definition, the influencing factors, and the applications of satisfaction and developed various types of customer satisfaction models, among which the more representative ones are the Swedish customer satisfaction model (SCSB), the American customer satisfaction model (ACSI), and the European customer satisfaction model (ECSI) [9]. In the mid-1990s, domestic research on satisfaction indices gradually took shape, with research and applications based on satisfaction involved in various industries [10]. It generated a large number of results in the fields of public service satisfaction, enterprise customer experience satisfaction, farmers’ satisfaction, and residents’ life satisfaction. Farmers’ satisfaction is the extent to which they feel satisfied after participating in a policy or enjoying a service, when comparing their prior expectations with the actual perceived effects [11]. As farmers’ actual perceptions are influenced by their own conditions, different farmers may have different opinions about the same type of thing. In recent years, as the state attaches importance to the rural problem in three dimensions, scholarspaid more attention to the satisfaction of this special group of farmers, and carried out research on satisfaction closely related to farmers in the areas of land expropriation [12,13,14], arable land rights [15], targeted and precise poverty alleviation [16], agricultural policies [17], etc. In terms of the choice of research methods, scholars have mostly favoured the use of descriptive statistical analysis and regression models [18], but satisfaction evaluation is a study that needs to explore deep-seated influences and their relational constructs, and the systematic nature of structural model equations can help fill this gap. Among them is the American ACSI model, which has been chosen by an increasing number of researchers in the field of research on measuring satisfaction in China for its strengths in combining the most representative of customer satisfaction theories, namely: the expectation–difference theory, perceived performance theory, and equity theory [19,20]. Most of the existing studies on the market entry of collective-owned commercial construction land focus on the development of legal systems [21,22], the improvement of the value-added revenue distribution system [23,24], and the construction of a unified urban–rural land market [25], which all focus on the macro-level of the legal system and planning. This is not conducive to the accurate judgement and identification of farmers’ true feelings towards the market entry of collective-owned land and its influencing factors, and it is not possible to obtain the effect of the implementation of policy on the ground. While the pilot market entry of collective-owned land for construction has been gradually expanded, there are still problems with the implementation of that policy in some areas, such as farmers’ low motivation, contradictory income distribution among rural collective-owners, and imperfect supporting systems for market entry [26]. There is an urgent need to analyse the perceptions of local farmers in the market for collective-owned management construction land from a systemic scientific perspective, in order to examine systematically what specific factors affect farmers’ satisfaction, and to further optimise the design and implementation of the market entry system for collective-owned management construction land.

Based on these considerations, the main research questions in this study are: What is the satisfaction of farmers in the pilot policy of market access to collective land for construction? What may affect farmer satisfaction, and what is the mode of action for that? Therefore, through systematic theoretical analysis and field surveys, we apply the classic customer satisfaction index model to farmers’ evaluation of the government’s collective commercial construction land entering the market, establish a model of farmers’ satisfaction index for collective commercial construction land marketisation, quantitatively analyse the implementation effect of the collective land market entry policy from the perspective of farmers’ subjective feelings, find the influencing factors of farmers’ satisfaction, and summarise the experience through a discussion of the research results, hoping to contribute to the government’s efforts to further protect farmers’ rights and interests and improve the quality of collective land market entry work.

2. Theoretical Model and Research Hypothesis

2.1. Theoretical Model

The structural equation model (SEM), also known as the analysis of covariance (ANOVA) model, was introduced in the 1970s by Joreskog and Wiley [27,28], who integrated statistical methods, such as factor analysis and path analysis. The method is a combination and improvement of methods, such as path analysis, exploratory factor analysis and analysis of variance, and consists of two parts: a measurement model and a structural model, which is a comprehensive method for testing the relationship between latent variables [29]. The model takes the form of Equation (1), where X is a vector composed of exogenous variables; Y is a vector of endogenous variables; Λx is the factor loading of X; Λy is the factor loading of Y; ξ and η are, respectively, the vector of the exogenous latent variables and the vector of endogenous latent variables; and δ and ε are the measurement error vectors [30], written as:

$$\{\begin{array}{l}\mathrm{X}=\wedge \mathrm{x}\eta +\delta \\ \mathrm{Y}=\wedge \mathrm{y}\zeta +\epsilon \end{array}$$

(1)

The mathematical expression of the structural model is Equation (2), where η and ζ are, respectively, the exogenous latent variable and endogenous latent variable matrices; B and Γ denote, respectively, the coefficient matrices of the endogenous and the exogenous latent variables; and ζ is the structural model residual [31].

$$\eta =\mathrm{B}\eta +\Gamma \zeta +\zeta$$

(2)

2.2. Model Construction and Research Hypotheses

The most widely used model and method for measuring public satisfaction is the American ACSI model [32], which is based on a series of causal equations linking customer expectations, perceived quality, and perceived value to customer satisfaction; and customer satisfaction is, in turn, linked to outcomes, which are evaluated through customer complaints and customer loyalty [33].

There are many factors that affect farmers’ satisfaction with the market entry of collective-owned management construction land, so this paper takes the US Public Sector Customer Satisfaction Index model as the core framework, and adjusts the latent variables and causality by combining them with the actual situation of China’s market entry of collective-owned management construction land, and selects the following seven latent variables: farmers’ information awareness; farmers’ expectations; farmers’ perceived quality; farmers’ perceived value and farmers’ satisfaction; farmers’ complaints; and farmers’ trust. After identifying the latent variables, the observation variables corresponding to each of the seven latent variables were determined in accordance with the principles of accuracy and survey convenience (

Table 1. Description of the model variables.

Potential Variables	Observed Variables	Variable Values	Kurtosis	Skewness	Means	Variance
Farmers’ information awareness	Dissemination of information on policies related to land marketisation (X1)	1 = “very poor”, 2 = “poor”, 3 = “average”, 4 = “fairly good”, 5 = “very good”	0.346	−0.550	2.88	0.996
	Transparency of information disclosure (X2)	1 = “very opaque”, 2 = “not very transparent”, 3 = “average”, 4 = “fairly transparent”, 5 = “very transparent”	0.054	−0.961	3.13	1.158
	Participation in voting in the land marketisation (X3)	Yes, No	0.126	−2.007	0.47	0.500
Farmers’ expectations	Expectations of living standards after land marketisation (X4)	1 = “significantly lower”, 2 = “somewhat lower”, 3 = “the same as before”, 4 = “somewhat improved”, 5 = “significantly improved”	−0.124	0.553	3.13	0.826
	Expectations of the amount of compensation to be obtained after land marketisation (X5)	1 = “very low”, 2 = “relatively low”, 3 = “average”, 4 = “relatively high”, 5 = “very high”	0.193	−0.069	2.93	0.751
	Expectations of employment help after land marketisation (X6)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “average”, 4 = “Somewhat satisfied”, 5 = “Very satisfied”	−0.492	0.554	2.98	0.695
	Expectations of medical insurance and social insurance subsidy after entry market (X7)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “average”, 4 = “more satisfied”, 5 = “very satisfied”	−0.239	0.495	2.84	0.770
Farmers’ perceived quality	Satisfaction with the amount of benefits distributed after market entry (X8)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “average”, 4 = “more satisfied”, 5 = “very satisfied”	0.379	−0.103	3.02	0.746
	Fairness of distribution of earnings (X9)	1 = “not fair”, 2 = “not very fair”, 3 = “fair”, 4 = “fairer”, 5 = “very fair”	0.161	−0.288	3.23	0.829
	Procedural fairness and legality of land marketisation (X10)	1 = “not fair”, 2 = “not very fair”, 3 = “fair”, 4 = “quite fair “, 5 = “very fair”	0.431	−0.347	3.12	0.937
	Increase in access to employment after land marketisation (X11)	1 = “none at all”, 2 = “not much”, 3 = “fair”, 4 = “some increase”, 5 = “considerable increase”	−0.255	−0.125	2.89	0.843
	Medical insurance and social insurance subsidy after land marketisation (X12)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “neutral”, 4 = “quite satisfied”, 5 = “very satisfied”	0.159	0.115	2.98	0.815
Farmers’ perceived value	Change in living standards (X13)	1 = “much worse”, 2 = “somewhat worse”, 3 = “no change”, 4 = “somewhat better”, 5 = “much better”	0.480	0.913	3.27	0.701
	Change in income level (X14)	1 = “much worse”, 2 = “somewhat worse”, 3 = “no change”, 4 = “somewhat better”, 5 = “much better”	−0.176	1.674	3.05	0.525
	Change in living environment (X15)	1 = “much worse”, 2 = “somewhat worse “, 3 = “no change”, 4 = “somewhat better”, 5 = “much better”	−0.353	1.212	2.99	0.657
Farmers’ satisfaction	Satisfaction compared with expectations (Y1)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “average”, 4 = “quite satisfied”, 5 = “very satisfied”	0.173	−0.297	3.04	0.985
	Satisfaction compared with ideal satisfaction (Y2)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “neutral”, 4 = “quite satisfied”, 5 = “very satisfied”	0.120	−0.832	3.14	1.059
	Overall satisfaction (Y3)	1 = “very dissatisfied”, 2 = “not very satisfied”, 3 = “neutral”, 4 = “quite satisfied”, 5 = “very satisfied”	0.080	−0.848	3.27	1.099
Farmers’ complaints	Likelihood of negative publicity about land marketisation (Y4)	1 = “not likely”, 2 = “probably unlikely”, 3 = “uncertain”, 4 = “probably likely”, 5 = “very likely”	0.392	−0.341	2.47	1.006
	Entry policy deficiencies (Y5)	1 = “no deficiencies”, 2 = “not too serious”, 3 = “uncertain”, 4 = “quite serious”, 5 = “very serious”	0.315	−0.781	2.63	1.085
Farmers’ trust	Confidence that future land marketisation will go smoothly (Y5)	1 = “no confidence”, 2 = “hardly any confidence “, 3 = “uncertain”, 4 = “some confidence”, 5 = “complete confidence”	−0.173	−1.108	3.54	1.103
	Level of support for government policy on land marketisation (Y6)	1 = “none”, 2 = “hardly any”, 3 = “uncertain”, 4 = “quite high “, 5 = “high”	−0.041	−1.075	3.39	1.098

). The details of the seven latent variables are as follows. Based on relevant research experience, we analyse the relationship between the core variables in the model and propose the corresponding basic theoretical hypotheses (Figure 1).

(1)

Farmers’ information awareness

Information disclosure and publicity are an important part of the work procedures for collective commercial construction land to enter the market, which can reflect the transparency of the entry work from the side. Information disclosure protects farmers’ right to know to a certain extent and is particularly important in the process of collective-owned commercial construction land marketisation. Therefore, the variable of farmers’ information awareness is introduced into the model to explore the direction and magnitude of its impact on farmers’ satisfaction. On this basis, the study puts forward the following hypotheses:

Hypothesis 1a (H1a).

Farmers’ information awareness positively influences farmers’ expectations.

Hypothesis 1b (H1b).

Farmers’ information awareness positively influences farmers’ perceived value.

Hypothesis 1c (H1c)

. Farmers’ information awareness positively influences farmers’ perceived quality.

Hypothesis 1d (H1d).

Farmers’ information awareness has a positive influence on farmers’ satisfaction.

(2)

Farmers’ expectations

Psychological expectations can significantly influence the degree of perceived quality and are one of the determinants of satisfaction [34,35], because farmers’ satisfaction is a comparison of perceptions before and after the market entry of collective-owned business construction land [36], and satisfaction is easily achieved if the quality is higher than expected. On this basis, the study puts forward the following hypotheses:

Hypothesis 2a (H2a).

Farmers’ expectations positively influence perceived quality.

Hypothesis 2b (H2b).

Farmers’ expectations positively influence perceived value.

Hypothesis 2c (H2c).

Farmers’ expectations positively influence farmers’ satisfaction.

(3)

Farmers’ perceived quality

Public satisfaction is mainly influenced by the quality of public service outcomes and the quality of the delivery process [37]. Farmers’ perceived quality refers to the most direct feelings and evaluations of farmers, based in collective-owned management construction land marketed areas, about the results brought about by the marketed work during and after the marketisation process. On this basis, the study puts forward the following hypotheses:

Hypothesis 3a (H3a).

Farmers’ perceived quality positively influences farmers’ perceived value.

Hypothesis 3b (H3b).

Farmers’ perceived quality positively influences farmers’ satisfaction.

(4)

Farmers’ perceived value

Perceived value refers to the subjective feelings of farmers about whether marketing the land is worth it, including how it affects their standard of living, income level, and living environment, when comparing the changes before and after the land is put on the market [38]. On this basis, we formulate the following hypotheses:

Hypothesis 4a (H4a).

Farmers’ perceived value positively influences farmers’ satisfaction.

(5)

Farmers’ complaints and farmers’ trust

If farmers believe that the market entry policy is flawed in its formulation, they will complain when they are dissatisfied, which will easily lead them to engage in negative propaganda and will also reduce farmers’ trust. Citizens can draw on their individual experiences to judge the performance of public services and influence public confidence in the government through their level of satisfaction [39], and farmers will have some trust and support for the government when they are satisfied with the policy of collective-owned commercial construction land marketisation as a whole. On this basis, the study puts forward the following hypotheses:

Hypothesis 5a (H5a).

Farmers’ satisfaction has a negative effect on farmers’ complaints.

Hypothesis 5b (H5b).

Farmers’ satisfaction positively influences farmers’ trust.

Hypothesis 5c (H5c).

Farmers’ complaints have a negative impact on farmers’ trust.

3. Methodology

3.1. Research Route

Dazu District of Chongqing City, one of the 33 pilot areas in the country, was selected as the research area, and a field questionnaire was used to collect information on the satisfaction of farmers in the pilot market of collective-owned management construction land. After eliminating the invalid questionnaires, the reliability and validity of the questionnaires were checked by SPSS software. On the basis of the reliability and validity of the questionnaires, a model of farmers’ satisfaction was constructed based on the theoretical basis of satisfaction, and the model structure was mapped and calculated by the relevant tools of AMOS 22.0 software in order to obtain the relationship between the latent variables in the model and the degree of their influence. In addition, the model was tested for fitness, and, if the results met the criteria, the model was analysed together with the model path coefficients. Otherwise, the model would have needed to be constantly re-corrected to meet the criteria for model fitness Finally, the main factors affecting farmers’ satisfaction and their internal mechanisms against the background of collective commercial construction land entering the market were clarified, and the deficiencies in the process of collective land entering the market were found, conclusions were drawn, and suggestions were made for optimisation measures (Figure 2).

3.2. Study Area

Dazu District is located in the western part of Chongqing, China, between latitude 29°23′ and 29°52′ N and longitude 105°28′ and 106°2′ E (Figure 3). It has an area of 1436 square kilometres and a resident population of 840,000, with a rural population of 329,500. In 2015, the state launched the pilot market entry of collective business construction land, with the approval of the Central Committee’s Deep Reform Group. Dazu District in Chongqing, as one of the 33 pilot counties (cities and districts) in the country, launched the pilot market entry reform of rural collective business construction land. Up to April 2020, a total of 90 cases and 195.18 hm² of land in Dazu District were traded on the market [40], involving many towns (streets), such as Longshui, Baoding, Shima, Yongxi, Yulong, Tangxiang, Longgang, etc. It revitalised the rural stock of land and provided land security for the development of leisure agriculture, rural tourism, and agricultural product processing industries in the area. As Dazu District is the only pilot area in Chongqing, how to find a more reasonable and orderly way to allow farmers to continue to benefit and build a better rural area by putting collective business construction land on the market will be a key issue that Dazu District needs to further explore and solve in practice.

3.3. Data Collection and Sampling

The data used in this paper comes from a field survey conducted by the research team in July 2021 in towns (streets) in Dazu District that participated in collective construction land marketisation, using a random sampling method to randomly select participating towns (streets), covering 29 village groups in 11 towns. During the research process, one person from each household was used as a representative. As the literacy level of the survey respondents was generally low, a questionnaire supplemented by a semi-structured interview was chosen, with the surveyor interviewing the respondents face-to-face and the surveyor explaining the questionnaire questions in easy-to-understand language, with the average household survey time being around 20 min. This ensured that the questionnaires were filled out accurately and ensured the quality of the data. A total of 200 questionnaires were returned, and after collecting and eliminating invalid questionnaires, 177 valid questionnaires were finally selected, with a valid return rate of 89%. The questionnaire consisted of two main parts, namely: basic information about the farmers and satisfaction variables; and the use of a Likert scale to evaluate the indicators. The specific descriptive statistics of the variables are shown in Table 1.

4. Research Results and Analysis

4.1. Sample Characteristics

The basic information of the farming households in this survey is shown in

Table 2. Statistics of basic information of farmers.

Category	Breakdown	Sample Statistics	Percentage
Gender	Male	80	45.2%
	Female	97	54.8%
Age	Under 25 years of age	2	1.1%
	Age 26–40	14	7.9%
	Age 40–60	63	35.6%
	Over 60 years old	98	55.4%
Educational attainment	No education	67	37.9%
	Primary school	63	35.6%
	Lower secondary	31	17.5%
	High school/junior college	9	5.1%
	College/Bachelor’s degree and above	7	4.0%
Are you a village official?	Yes	36	20.3%
	No	141	79.7%
Main source of income	Working in agriculture	78	44.1%
	Main source of income combining farming with other non-agricultural work	58	32.8%
	Non-agricultural farming	41	23.2%
Annual household income	Less than 10,000 RMB	59	33.3%
	10–40,000 RMB	77	43.5%
	40,000–70,000 RMB	30	16.9%
	70,000–100,000 RMB	8	4.5%
	Over RMB 100,000	3	1.7%

. From the statistical table of this basic information, it can be seen that, in this survey, the proportion of males accounted for 45.2% of the farmers and the proportion of females accounted for 54.8%. This shows that the majority of farming households in the region still rely on agriculture as their main source of livelihood. In terms of the basic statistical characteristics of the sample (Table 2), the sample used in this paper is reasonably representative.

4.2. Analysis of the Reliability and Validity of the Data

“Reliability” is an important criterion for testing the accuracy of a scale. In this paper, the Cronbach’s alpha (Cronbach’s coefficient) method was used to test whether the reliability of the data was up to standard. The Cronbach’s alpha value for the whole questionnaire was 0.840, and then a reliability test was undertaken separately for each latent variable item. Generally speaking, any data from the questionnaires greater than 0.7 can be considered reliable [41]. As can be seen from

Table 3. Confidence test results.

Item	Number of Items	α Confidence Coefficient
Farmers’ information awareness	2	0.807
Farmers’ expectations	4	0.807
Farmers’ perceived quality	5	0.864
Farmers’ perceived value	3	0.716
Farmers’ satisfaction	3	0.863
Farmers’ trust	2	0.714
Farmers’ complaints	2	0.838

, the data reliability of all question items is high, and the quality of the questionnaire is relatively good. “Validity” is the extent to which the measured results reflect the content of the survey. The KMO test and Bartlett’s sphericicty test were applied to the survey data, and the test results in

Table 4. Validity test results.

KMO Sampling Adequacy Measure		0.826
	Approximate chi-square (χ2)	1784.053
Bartlett’ssphericity test	Bartlett’s sphericity test Degrees of freedom (df)	231
	Significance P	0.000

show that the KMO value is 0.826, which is between 0.7 and 0.9, indicating that the data is suitable for factor analysis, with good structure [42]. The 22 observed variables were divided into seven factors based on the results of the factor analysis, and these seven factors were able to explain 71.577% of the overall variance (

Table 5. Total variance explanation table.

Score	Initial Eigenvalue			Extraction of Load Sums of Squares			Sum of Squared Rotating Loads
	Total	Percentage Variance	Cumulative %	Total	Percen-Tage Variance	Cumula-Tive %	Total	Percentage Variance	Cumu-Lative %
1	7.027	31.942	31.942	7.027	31.942	31.942	3.371	15.324	15.324
2	2.008	9.128	41.070	2.008	9.128	41.070	2.602	11.828	27.153
3	1.807	8.213	49.283	1.807	8.213	49.283	2.457	11.167	38.320
4	1.507	6.852	56.135	1.507	6.852	56.135	1.987	9.033	47.353
5	1.287	5.849	61.984	1.287	5.849	61.984	1.917	8.715	56.068
6	1.210	5.501	67.485	1.210	5.501	67.485	1.766	8.026	64.094
7	0.900	4.092	71.577	0.900	4.092	71.577	1.646	7.482	71.577

Note: Extraction method: Principal component analysis.

): this meant meeting the requirement of social science research for the total variance of the survey sample data to be greater than 70% [43], with good explanatory power of the factors on the variables.

A test of composite reliability (CR) and convergent validity was also carried out. Before doing the structural equations, doing the CFA is part of the required analysis [44]. The CR value is a combination of all variable confidence measures and indicates the internal consistency of the construct pointers, with a higher CR indicating the internal consistency of the construct. Fornell and Raquel recommend that this value should be greater than 0.7 [45,46]. AVE is a measure of the explanatory power of the variance of the measured variables for the latent variables: the higher the AVE, the higher the confidence and convergent validity of the construct. Ideally, the standard value should be greater than 0.5, with 0.36 to 0.5 being an acceptable range [47,48]. As shown in

Table 6. Potential variables and item test results.

Potential Variables	Observed Variables	Factor Loading	CR	AVE
Farmers’ information awareness	X1	0.906	0.728	0.501
	X2	0.749
	X3	0.348
Farmers’ expectations	X4	0.728	0.812	0.520
	X5	0.759
	X6	0.725
	X7	0.670
Farmers’ perceived quality	X8	0.783	0.868	0.570
	X9	0.670
	X10	0.721
	X11	0.672
	X12	0.684
Farmers’ perceived value	X13	0.692	0.724	0.466
	X14	0.879
	X15	0.633
Farmers’ satisfaction	Y1	0.865	0.734	0.587
	Y2	0.833
	Y3	0.779
Farmers’ complaints	Y4	0.878	0.838	0.721
	Y5	0.814
Farmers’ trust	Y6	0.781	0.864	0.680
	Y7	0.812

, we observed the reliability of the scale through CR values, and we can see that all variables have CR values greater than 0.7, indicating that the individual variables of the scale have high reliability, and secondly, all variables have mean variance extracted AVE values between 0.4 and 0.5, indicating good convergent validity between the constructs.

4.3. Research Based on the Structural Equation Model

4.3.1. Evaluation of the Overall Suitability of the Structured Equation Model

A preliminary structural equation model of farmers’ satisfaction with the market entry of collective-owned land for construction was established using AMOS software. The order of steps is: Draw unobserved variables-observed variables. Add unique variables. Click on the object property to name it, while connecting the relationship between variables, as shown in Figure 4 below. The preliminary structural equation model contains 7 latent variables and 22 observed variables. In order to ensure the scientific validity of the model, 29 residual terms were introduced, numbered from e1 to e29, and the correlation co-efficient between the residual terms and the observed variables was 1. Finally, drag the research data in the order of “Select data files-List variables in data set” to the appropriate position.

The model was tested for fitness with the analysis properties tool, and from

Table 7. Results of model fitness test indicators.

Evaluation Indicators	X2/df	RMSEA	NFI	CFI	IFI	TLI
Reference standards	<3	<0.08	>0.90	>0.90	>0.90	>0.90
Before amendment	1.754	0.065	0.806	0.904	0.906	0.886
Indicator values for this model	1.548	0.056	0.939	0.935	0.936	0.923
Model adaptation judgement	Ideal	Ideal	Approach	Ideal	Ideal	Ideal

, it can be seen that some of the initial model fit indices did not reach the standard, indicating that the model fit needs to be improved. Therefore, the model was revised according to the MI (modification indices) values, adding e1–e4 and e3–e10 to account for possible correlations between the observable variables and to reduce the model cardinality as much as possible, without violating the theoretical hypothesis. This is conducted by checking the “modification indices” box in the “output” screen.

The modified model fit was improved; the fit condition was good; and the fitness indicators basically met the requirements [49] (Table 7). The model estimation was performed on the modified model using a maximum likelihood approach. The standardised model path coefficients and significant results are shown in Figure 4 and Table 7.

4.3.2. Analysis of the Empirical Results of Testing the Research Hypothesis

According to the revised model diagram of farmers’ satisfaction (Figure 5), the model paths were analysed and compared with the hypotheses presented in the previous section in order to determine whether the results obtained supported the hypotheses (

Table 8. Model estimation results.

Assumptions	Estimate	CR	p	Conclusion
H1a: Farmers’ information awareness positively influences farmers’ expectations.	0.344	3.664	***	Supported
H1b: Farmers’ information awareness has a positive impact on farmers’ perceived value.	−0.035	−0.381	0.703	Not supported
H1c: Household information awareness has a positive impact on farm household perceived quality.	0.209	2.523	*	Supported
H1d: Farmers’ information awareness positively influences farmers’ satisfaction.	0.345	3.836	***	Supported
H2a: Farmers’ expectations positively influence perceived quality.	0.519	5.401	***	Supported
H2b: Farmers expect a positive impact on perceived value.	0.348	2.83	*	Supported
H2c: Farmers’ expectations positively influence farmers’ satisfaction.	0.013	0.119	0.906	Not supported
H3a: Farmers’ perceived quality positively influences farmers’ perceived value.	0.352	2.893	*	Support
H3b: Farmers’ perceived quality positively influences farmers’ satisfaction.	0.271	2.493	*	Supported
H4a: Farmers’ perceived value positively influences farmers’ satisfaction.	0.205	1.847	*	Supported
H5a: Farmers’ satisfaction has a negative impact on farmers’ complaints.	−0.365	−4.206	***	Supported
H5b: Farmers’ satisfaction positively influences farmers’ trust.	0.477	5.233	***	Supported
H5c: Farmers’ complaints have a negative impact on farmers’ trust.	−0.213	−2.154	*	Supported

Note: * indicates significant at the statistical level of 0.05, *** indicates significant at the statistical level of 0.001.

). From the estimation results, it can be seen that hypotheses H1a, H1c−H2b, H3a−H3b, and H5a−H5c all reach the significance level, and the hypotheses are valid. In addition, the p values of the three paths of farmers’ information knowledge on farmers’ perceptions, farmers’ expectations on farmers’ satisfaction, and farmers’ perceived value on farmers’ satisfaction are all greater than 0.05, indicating that there is no significant correlation between the variables; hypotheses H1b, H2c and H4a are not valid, and therefore are not discussed. The model estimation results were combined with the path standardisation coefficients to make a specific analysis of farmers’ information knowledge, farmers’ perceived quality, farmers’ perceived value, and other factors which influence farmers’ satisfaction after the market entry of collective-owned management construction land.

(1)

Farmers’ information awareness and farmers’ satisfaction

The standardised path coefficient of 0.344 indicates that awareness of market entry information has a positive effect on farmers’ satisfaction, and therefore hypothesis H1a holds. From the results of the path analysis, the coefficients of the observed variables of information dissemination on market entry policies, transparency of information disclosure and participation in market entry voting are, respectively, 0.916, 0.734, and 0.327, all of which are positive. The greatest influence is on the dissemination of information about the policy on access to the market, followed by the degree of transparency of access to the market. In this survey, 36.2% of the farmers felt that the market entry was not well publicised, which is the largest proportion of the answers to the three questions.There were cases where farmers suggested that they would like the market entry process to be more transparent, and that a higher-level manager should be sent to supervise the market entry discussion meetings. This shows that there is still room for the government and village organisations to improve the publicity of the market entry policies and procedures, and that the democratic decision-making mechanism for collective-owned land entry is not perfect. Democratic supervision and decision making can prevent the abuse of power and reduce conflicts of interest. In the future, it will be necessary to increase the dissemination of policies and information on collective-owned land entry into the market, and to promote the participation of farmers in the democratic decision-making process of land entry in order to increase their satisfaction.

(2)

Farmers’ perceived quality and farmers’ satisfaction

Farmers’ perceived quality has a significant positive effect on farmers’ satisfaction at the 10% level, with a standardised path coefficient of 0.271 and a critical ratio between the latent variables of 2.493. Hypothesis H3b holds, so improving farmers’ perceived quality can effectively increase farmers’ satisfaction. Among the farming households’ perceived quality, the main influences are the social security benefits allowance given to them after the marketisation, the fairness of the distribution of benefits, and satisfaction with the amount of benefits distributed after the marketisation, with factor loading coefficients of 0.815, 0.781, and 0.78, respectively. This indicates that farmers are most interested in the medical and social insurance benefits and the corresponding income distribution that will directly benefit their production and livelihood.

(3)

Farmers’ perceived value and farmers’ satisfaction

Among the observed variables of perceived value, a change in living standards (0.701) has the greatest impact, followed by change in living environment (0.657) and change in income level (0.525). This suggests that taking measures to address living standards and the living environment is an important task to improve farmers’ satisfaction from the perspective of their perceived value. It is worth noting that the countryside and its land are the place where farmers live and the home on which they depend. Therefore, the farmers located around the land and involved in the market are very worried about the environmental issues arising from the industries that will be built after the land is put on the market, i.e., they are particularly concerned about whether the foreign enterprises that will move in will damage the quality of the original rural environment. In the final analysis, the objective and direction of all policies should be to improve the livelihood of the farmers, increase the proportion of revenue allocated to farmers, protect the rural environment, and strengthen infrastructure development.

(4)

Farmers’ satisfaction, farmers’ complaints, and farmers’ trust.

There is a strong correlation between farmers’ satisfaction, farmers’ complaints, and farmers’ trust. The path coefficient between farmers’ satisfaction and farmers’ complaints was significant at the 1% level, with a path coefficient of −0.365, indicating that the better the farmers’ satisfaction, the smaller the chance of generating farmers’ complaints. The path coefficient between farmers’ satisfaction and farmers’ complaints is 0.477, which is significant at the 1% level, and the two are positively correlated, i.e., the higher the farmers’ satisfaction, the stronger the farmers’ trust will be. Farmers’ complaints have a significant negative effect on farmers’ trust with a coefficient of −0.213, which means that an increase in farmers’ satisfaction can have the effect of reducing farmers’ complaints and increasing farmers’ trust. Among the observed variables of farmers’ complaints, the factor loading coefficient of the defective market entry policy is 1.085, which indicates that the current market entry policy for collective-owned management construction land may still have deficiencies in the market entry procedures, information disclosure, and the distribution of the amount of market entry proceeds, etc. The policy should pay attention to the legal and transparent market entry procedures, and determine the proportion of land value-added proceeds more scientifically and fairly in order to improve the collective-owned management construction land market entry policy. This will improve the policy on the listing of land for collective-owned operation and construction, satisfy farmers, reduce the possibility of negative publicity related to the listing policy, and increase farmers’ confidence in the smooth implementation of the listing process and the level of government policy support in the future.

5. Discussion and Recommendation

5.1. Farmers’ Satisfaction Situation

One of the main objectives of the policy of putting collective construction land on the market is to increase farmers′ land property income and promote rural revitalization, which plays an important role in improving the long-term welfare and economic growth of farmers. However, most of the existing literature focuses on the macro-institutional level of the land taxation system, division of ownership, interest pattern, and rule setting in this context [50,51,52], or it focuses on subjects limited to the government and grassroots organisations [53,54], while studies from the perspective of farmers’ satisfaction as micro-entities are scarce. To fill this gap, our study focuses on answering the question of how satisfied farmers are in the pilot areas of the land policy.

This study focuses on the satisfaction level of farmers as the main participants in the promotion of the policy of the entry of collective-owned business construction land into the market and analyses the factors and mechanisms that affect the satisfaction of farmers. Therefore, in conjunction with the theoretical model, a total of 13 research hypotheses were proposed in this paper, and, after testing, all hypotheses passed the significance test, except for hypotheses H1b and H2c, which were not significant. Then, the results of data analysis were summarised to draw the following findings. In general, most farmers are moderately satisfied, followed by feeling relatively satisfied. Farmers’ satisfaction with the market entry of land for collective-owned management is influenced by various factors, including farmers’ information awareness, farmers’ perceived quality, and farmers’ perceived value, with the degree of influence being: farmers’ information awareness > farmers’ perceived quality > farmers’ perceived value. Farmers’ complaints and farmers’ trust, as the posterior variables of farmers’ satisfaction, are also affected to some extent.

(1) Farmers’ information awareness has the greatest impact on farmers’ satisfaction, which is reflected by the dissemination of information on policies related to market entry, the degree of information disclosure and transparency, and the participation in market entry voting. The more information farmers have, the higher their level of satisfaction is. This shows that the government should strengthen the propaganda of the policy on the entry of collective-owned land into the market in a way that is easily accepted and explain the logic of the entry of collective-owned land into the market more accurately to farmers, so that they can perceive the transparency of the policy and accurately understand and support the policy, thus increasing their satisfaction.

(2) The higher the quality of farmers’ perceptions of collective-owned land transfer to the market, the more effective the promotion of their satisfaction is. Under the dimension of perceived quality, the most influential factor in farmers’ satisfaction is the distribution of medical and social insurance benefits and proceeds after the market entry. Therefore, the formulation of a sound compensation system and a fair standard for the distribution of compensation proceeds are key measures to improve farmers’ satisfaction. The positive effect of farmers’ perceived value on farmers’ satisfaction shows that farmers’ satisfaction can only be further improved if the fundamental interests of farmers in the area where the land has been marketed are effectively safeguarded, so that farmers consider the benefits and compensation brought about by the marketed collective-owned land to be “worth it”, compared with those offered by the non-marketed land.

(3) There is a significant effect between farmers’ satisfaction, farmers’ complaints, and farmers’ trust, and the three are closely related. Farmers’ satisfaction has a greater impact on farmers’ trust than farmers’ complaints, and the higher the farmers’ satisfaction with the market entry process is, the higher their trust in the government is; otherwise, they will complain and thus reduce their support. It is important that the implementation of the marketed land for collective-owned management and construction should be adjusted and optimised in a timely manner in order to address the needs and grievances of farmers.

5.2. Policy Improvement Recommendations

On the basis of the above findings, and in order to make the entry of the collective-owned commercial construction land into the market work to promote more quality so that farmers are more satisfied, this paper makes the following optimisation suggestions:

(1) Strengthen the publicity work on market entry and improve the supervision and management mechanism of market entry. The government should intensify its efforts to publicise the market entry and make use of a variety of online and offline channels to publicise and explain policies and information, and provide farmers with positive and correct guidance so that they can truly understand the government’s policy on the market entry of collective-owned land for business purposes and understand the details of the land entry. In this way, they can develop an understanding and support in order to better promote the process of collective-owned land entry. At the same time, the whole process of listing this land involves a number of links that require democratic decision making; for example, the listing requires a voting meeting of the villagers’ representatives, and all aspects of information on the listing transaction should also be monitored by the villagers. Therefore, we should, firstly, actively promote the extensive participation of villagers, keep abreast of farmers’ needs, promote scientific and democratic public decision making, improve the efficiency of the listing of collective-owned business construction land, and thus improve overall work performance; and, secondly, accept the supervision of farmers, and information on the transaction of land related to the listing should be announced to the farmers in a timely manner, making clear its land use, transaction price, and length of use. The villagers also have the right to check and hold the land transferee accountable when its land use deviates from the plan or other undesirable problems occur. For example, in another pilot area, Pidu District, Chengdu, information on collective business construction land is made public on the official portals of the Chengdu Ministry of Land and Resources and the Pixian Ministry of Land and Resources. In addition, the Ministry of Land and Zhejiang’s Deqing County also set up a trading platform for the transfer of land, with information on land entering the market made public.

(2) Improve the perceived quality and perceived value of farmers in the marketed areas in multiple ways. Address the issues that are of greatest concern to farmers, starting with the elements that are their main demands, and fully respect the opinions of farmer groups in terms of the mode of market entry, the proportion of value-added revenue distribution, and the use of the land [55], in order to improve the satisfaction of farmers, thereby ensuring the quality of the overall policy implementation of the market entry of collective-owned management construction land. For example, in the pilot area of Liaoning province, 80% of the income of the collective economic organisations is allowed to be distributed fairly among the members of the village via collective economic organisations. Furthermore, land is the most valued means of production for farmers, and they prefer to rent it out to achieve the sustainable development of the collective-owned economy than transfer it directly. Therefore, innovative modes of listing can be encouraged by inviting localities to take shares or participate in specialised agricultural enterprises, build storage facilities and shop fronts, etc., in order to increase their long-term income and provide more employment opportunities for collective-owned members. The case experience of Sanya in Hainan is the adoption of the “company + village collective + farming households” form, or the land shares and dividends model, whereby the village committee’s collective income can reach an average of at least 200,000 yuan per year in guaranteed land dividends. In addition, it is important to design and implement timely measures for the purchase of medical insurance and social security for pensions, which are the most popular measures among farmers, so that they can feel the practical benefits of the marketed collective-owned land for themselves.

(3) Improve government responsiveness and increase farmers’ trust. Farmers’ satisfaction is a cumulative subjective feeling [56]. In the process of market entry, if the government and village collective-owners can face up to and pay attention to farmers’ real wishes, it is then necessary to adjust the existing deficiencies and shortcomings of the policy on the market entry of collective-owned land for construction; make up for the shortcomings; ensure that farmers can receive the benefits they deserve from the land entry; maximise the value of the land; reflect efficiency and fairness; and establish a responsible government imageyo highlight the main position of farmers in the land reform system; to get rid of the role of passive recipients of the policy; to enhance farmers’ support and willingness to participate in the pilot market entry of land for collective-owned management and construction; and generate orderly behaviour. The implementation of all these goals will fundamentally promote the market entry of land for collective-owned management and construction so as to achieve the expected results.

5.3. Research Limitations and Prospects

This study yields some significant discoveries, but there are still shortcomings that need to be addressed in later studies. The shortcomings of this study are that: only one location, Dazu District, Chongqing, was selected as the study area; the scope of the study is relatively narrow; and the sample size is small due to limited funds, so the breadth and depth of the investigation in this study are not sufficient. The scope of the case area should be expanded in future studies to conduct more comprehensive investigations of other pilot areas in order to obtain more experience in the market entry of collective business construction land. In addition, during the research process, we were concerned that farmers’ satisfaction is a cumulative subjective feeling that may change over time, so future research could conduct further analysis and investigation along the path of the change process of farmers’ satisfaction with collective management construction land and the reasons for its change. Only then can we promote the research in the field of farmers’ satisfaction in collective-owned commercial construction land marketisation in a more scientific way.

6. Conclusions

In promoting the market entry of collective-owned management construction land, the government must implement the central idea of the rural revitalisation strategy and strive to maintain, develop, and realise the fundamental interests of the majority of farmers. So, this paper takes a microscopic view of farmers to study the effects of policy implementation, and we have several key findings. Firstly, the impact of farmers’ information awareness on farmers’ satisfaction is the most important, and can significantly affect the perceived quality and perceived value of farmers’ access to the market which shows that, compared with other projects, the government’s work on publicity and information notification may be particularly important. Secondly, farmers are most concerned about the distribution of the proceeds, especially the fairness of the distribution of the proceeds. Therefore, it is important to improve the transparency and fairness of the proceeds distribution process, and to further explore and improve the methodological system of proceeds distribution in order to ensure that farmers receive their rightful land property rights and interests.

Through a field study of farmers in Dazu District, Chongqing, China, and based on quantitative analysis, we explored the satisfaction situation of farmers under the policy of collective-owned commercial construction land marketisation and the reasons for its impact, and the main contributions of this paper are as follows: The classic customer satisfaction index (ACSI) model, which is used in the field of public service measurement, was applied to the assessment of farmers’ satisfaction, and a model of farmers’ satisfaction with the market entry of collective management construction land was constructed, based on the results of the regional assessment. Applying this model, the key factors affecting farmers’ satisfaction with land acquisition were identified, and the results can provide a theoretical basis for the development of China’s collective commercial construction land marketisation system. The deficiencies in the process of collective land entering the market were identified, providing the government with highly targeted policy recommendations that can increase the satisfaction of farmers in the areas where collective commercial land is entering the market, and all this provides a reference for this pilot area and even other areas of the country.