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Article

Effects of Livelihood Capital on the Farmers’ Behavioral Intention of Rural Residential Land Development Right Transfer: Evidence from Wujin District, Changzhou City, China

by
Ting Zhang
1,
Jia Li
2,* and
Yan Wang
3
1
Faculty of Humanities & Social Science, Nanjing Forestry University, Nanjing 210037, China
2
College of Economics and Management, Nanjing Forestry University, Nanjing 210037, China
3
School of Economics, Zhengzhou University of Aeronautics, Zhengzhou 450015, China
*
Author to whom correspondence should be addressed.
Land 2023, 12(6), 1207; https://doi.org/10.3390/land12061207
Submission received: 28 April 2023 / Revised: 1 June 2023 / Accepted: 9 June 2023 / Published: 10 June 2023
(This article belongs to the Special Issue Land Governance and Local Tenure Systems: The Role of Government Policy)
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Abstract

:
In the process of rapid urbanization and industrialization, there is a significant gap between farmers’ participation and rural homestead reorganization for the more diversified external environment. Despite considerable research focused on homestead withdrawal, the transfer of rural residential land development rights has received comparatively little attention. To realize the optimal use of rural homesteads’ resources and reducing potential living risks, this paper conducted an empirical study on the impact of farmers’ livelihood capital on their behavioral intention of rural residential land development right transfer within Wujin District as an example by incorporating the concepts of livelihood capital and risk perception into the theory of planned behavior (TPB). The results of this research show that the increase in livelihood capital may reduce the level of risk perception. The livelihood capital influences farmers’ intentions towards rural residential land development right transfer through risk perception and individual cognition. Based on the findings of this study, it is suggested to diversify livelihood strategies and improve the quality of livelihood capital, in order to reduce the constraint impact of risk perception on farmers’ behavioral intention (BI).

1. Introduction

The rapid growth of urbanization and industrialization has triggered the depopulation of rural areas for labor migration, leading to the extreme phenomenon of tense urban–human–land relationships and a large amount of idle rural homesteads. Simultaneously, the problem of ‘village-hollowing’ and low allocation efficiency of homestead resources have become increasingly prominent. It has become urgent to address this critical issue; in particular, the effective utilization of rural homestead resources in developed areas, where land resources are extremely scarce and rural homesteads are vacant, is crucial for the long-term sustainable development of rural land use [1]. Furthermore, there are hidden dangers in public security due to the farmers in developed areas spontaneously leasing their houses to migrant workers without legal approval [2,3,4]. As a result, a series of policies and regulations formulated by central government have been issued to encourage voluntary and paid withdrawal from homesteads, aiming to centralize dispersed rural homesteads through rural land system reforms, in order to achieve unified planning and standardized use of rural homesteads, and thereby enhance the living environment, quality of life, and social governance [2,3,4]. As a key element of the reforms, rural residential ‘land development right transfer’ (LDRT) involves the voluntary withdrawal from homesteads with compensation and the transfer of the corresponding rural residential land development rights. In practice, there are deviations between the expected system goals and practical implementations while farmers have little incentive to join in the LDRT, due to the homestead possibly being observed as the carrier of hometown emotional attachment and economic security [5,6]. In some areas, only 7.9% of farmers withdrew from their homesteads based on field survey data [7]. Therefore, the study of the behavioral intention of LDRT in this paper is of great significance to the optimal use of the rural homesteads resource and reducing potential living risks.
The topic of rural residential land has gained attention in recent years, and previous literature mainly concerns policy suggestions addressing the issues of homestead withdrawal and homestead property rights [8,9]. Furthermore, the research on homestead withdrawal focuses on the farmers’ intention and influencing factors, and current voluntary and compensation instruments of rural homestead withdrawal. Resource endowment [10], generation differences [11], and individual cognition [7,12] were observed as internal determinants of homestead withdrawal intention [13,14,15], while village committees [16], formal and informal institutions [17], social security system [5,18], external environment differences [2], and homestead situation [10,19] were regarded as external factors. The majority of research involves empirical studies based on underdeveloped areas, and few studies have been undertaken in developed areas. The relationship between the internal determinants and external factors has received comparatively little attention. It is necessary to investigate the influencing mechanism of the behavioral intention of LDRT based on disentangling the relationships between internal determinants and external factors. Risk perception, as an internal determinant, will inhibit farmers’ intentions of rural homestead withdrawal, while livelihood capital can alleviate the negative impact of risk perception on the farmers’ intentions of rural homestead withdrawal, especially combined with subjective norms and perceived behavioral control, which can reflect external resource conditions. Therefore, risk perception and livelihood capital need to be incorporated into the theory of planned behavior (TPB) to further study the farmers’ intentions of rural homestead withdrawal.
In China, there is a system known as the “separation of three rights” that divides homestead property rights into ownership rights, qualification rights, and homestead land use rights. It has been demonstrated that the “separation of three rights” makes the qualification rights become the derivative of homestead land use rights [20]. Among them, the qualification right represents the identity attribute and welfare attribute, emphasizing the social security function of the homestead [21]. The duration of the homestead land use rights has not been specified by current laws, and farmers who obtain the qualification rights will keep the homestead land use rights. Since the homestead land use rights embody the property attribute of the homestead [21], farmers can acquire income through leasing and transferring rural homesteads under certain conditions. Currently, the transfer of the homestead land use rights is realized through the form of a contract. The legalized contracts provide institutional guarantee in land transfer for developing house leasing, homestay, tourism, and pension industries, during which farmers can share benefits from the local economic growth. Furthermore, the transfer time duration is not formed a unified standard, but local governments have established their standards in terms of the local context.
Wujin District, Changzhou City in Jiangsu Province of China, has a conflict between competing land resources and a rising number of idle homesteads. As a pilot area, Wujin has undertaken the national rural homestead system reform in recent years, making significant progress in rural land resource allocation, village construction layout, rural homestead withdrawal, and rural residential land transfer. Due to thriving township enterprises, the economy here is relatively booming, which brings a high level of industrialization and urbanization. As a fundamental industry, agriculture has distinct characteristics and promotes the development of local tourism, simultaneously. The farmers in Wujin possess flexible and varied livelihoods, as a result, the behavioral intention of rural residential land development right transfer is more uncertain. This paper conducted an empirical study on the impact of farmers’ livelihood capital on their behavioral intention of rural residential land development right transfer with Wujin District as an example. The remainder of this paper is divided into three sections. The second section proposes the theoretical framework and research hypotheses that livelihood capital affects farmers’ behavioral intention of rural residential land development right transfer. The third section explores the mechanisms of farmers’ livelihood capital on their behavioral intention of rural residential land development right transfer by a structural equation model (SEM). The fourth section presents and discusses the results. The study may enrich academic research on the reform of the homestead system and improve the quality of policy making, which may directly enhance land-use efficiency and make farmers the primary beneficiaries of rural residential land development right transfer.

2. Theoretical Analysis and Research Hypotheses

2.1. Theoretical Basis

The theory of planned behavior (TPB), proposed by Ajzen [22], contains attitude to behavior, subjective norm, and perceived behavioral control. The theory proposes that an individual’s behaviors are the result of deliberate plans, namely AB, SN, and PBC determine individuals’ behavioral intention. TPB, verified in a large number of empirical studies [23,24,25], is mature enough to be widely used in sociology, pedagogy, and management. Some studies applied TPB to the explanation and prediction of behavioral intention and behaviors [26,27,28]. It is also suggested that the explanatory power of TPB is low in some situations [29,30]. Ajzen also put forward that TPB, with certain openness, should be adjusted and extended according to the specific situation during use [31]. Given this, other variables were introduced into the TPB, such as institutional influences [26], the behavioral goal [32], perceived difficulty [33], policy cognition, and psychological constructs [12].
The present study focuses on influencing factors on farmers’ BI of LDRT. In this situation, farmers’ behavioral intention is on the basis of individual cognition (AB, SN, and PBC) under the TPB framework. According to the sustainable livelihood theory, farmers’ livelihood capitals (LC) determine their livelihood strategies and behavioral intention [34]. Previous studies have proved the effect of livelihood capital on individuals’ behavior and intention from five dimensions including natural capital, physical capital, human capital, financial capital, and social capital [35,36]. Meanwhile, farmers may be confronted with a series of risks, such as the increasing cost of living, difficulty in employment, lack of social security, low compensation, and homestead appreciation in the process of rural residential land development right transfer. Based on their livelihood capitals, farmers develop different risk perceptions, and multiple risk factors are considered comprehensively under individual cognition (AB, SN, and PBC). Consequently, the perception of risks obstructs farmers’ BI of LDRT. According to the theoretical analysis, the present research constructed an extended TPB by incorporating livelihood capital and risk perception into the theoretical framework, as shown in Figure 1.

2.2. Research Hypotheses

According to the TPB framework, AB, SN, and PBC will jointly affect individuals’ behavioral intentions, which can be used to explain individuals’ behavioral motivation and intention [31]. TPB is widely applied to explore farmers’ homestead withdrawal intention [2,12]. This paper employed TPB to explain the effect of individual cognition (AB, SN, PBC) on the behavioral intention of rural residential land development right transfer.
Attitude to behavior refers to farmers’ positive or negative evaluation of their willingness to participate in development right transfer. In the cognition of farmers, development right transfer will bring immediate and long-term benefits, such as a guarantee for lives and property, an increase in household income through subsidies, improvement of human settlements and the ecological environment, and long-term development of future generations, which embodies farmers’ survival rationality, economic rationality, ecological rationality, and developmental rationality. It is presented that the more active the farmers’ attitude to behavior, the stronger the farmers’ behavioral intention.
Subjective norm refers to the social pressure, including the influence of the people around you and policy guidance of the government, felt by farmers when they have the behavioral intention of development right transfer. This paper measured farmers’ SN with the “influence of family”, “farmers’ understanding of relevant policies”, and “village committee and government interference”. The more positive the subjective norm perceived by farmers, the stronger the farmers’ behavioral intention of development right transfer is.
Perceived behavioral control refers to the degree of control that farmers feel when making decisions about development right transfer, including farmers’ self-efficacy and perceived external resource conditions. This paper defines PBC as sufficient financial strength, sufficient expertise, and satisfaction with government assistance. The stronger the farmers’ perceived behavioral control, the more likely it is they have the behavioral intention of development right transfer. Therefore, this paper proposes Hypothesis 1a, 1b, and 1c (H1a, H1b, H1c).
Hypothesis 1a (H1a).
AB positively impacts the farmers’ BI of LDRT.
Hypothesis 1b (H1b).
SN positively impacts the farmers’ BI of LDRT.
Hypothesis 1c (H1c).
PBC positively impacts the farmers’ BI of LDRT.
The above are the influencing factors of farmers’ behavioral intention of development right transfer under the classical TPB framework. To explain farmers’ behavioral intention of development right transfer more comprehensively and accurately, this paper incorporated livelihood capital and risk perception into TPB.
Livelihood capital is divided into five dimensions: natural capital, physical capital, human capital, financial capital, and social capital. Based on a sustainable livelihood framework, farmers’ livelihood capitals determine their livelihood strategies and behavioral intention [34], which is reflected as behavioral intention increasing with the boost of livelihood capital. Livelihood capital is not only the basis for farmers to make a living but also an important factor affecting farmers’ behavioral intention of development right transfer. On the one hand, when farmers’ livelihood capital improves, their self-efficacy refers to farmers’ abilities and confidence, as well as the time, manpower, and resources needed in the process of development right transfer. Additionally, a substantial improvement of livelihood capital means an increase in external support (government support and subsidies) obtained and perceived by farmers. Consequently, farmers’ livelihood capital will affect their behavioral intention as a motivating condition. On the other hand, the improvement of livelihood capital will promote farmers’ perception of immediate and long-term benefits, and, subsequently, a positive evaluation of development right transfer. In conclusion, the higher the farmers’ livelihood capital, the more positive the attitude to behavior and perceived behavioral control are, and, correspondingly, the stronger the behavioral intention. Farmers’ risk perception also depends on their livelihood capital, it is suggested that the difference in livelihood capital leads to different characteristics of risk perception. For example, with a higher level of education, farmers are more sensitive to uncertainty in risk. Last but not least, individuals with a higher level of livelihood capital have a stronger ability to cope with risks, and the risk scenario shaped by livelihood capital is more positive [37], which means that the higher the livelihood capital, the less uncertainty is perceived, and, correspondingly, the lower the level of risk perception. To sum up, this paper proposes Hypotheses 2a, 2b, 2c, and 2d (H2a, H2b, H2c, 2d).
Hypothesis 2a (H2a).
LC positively impacts the farmers’ AB.
Hypothesis 2b (H2b).
LC positively impacts the farmers’ PBC.
Hypothesis 2c (H2c).
LC positively impacts the farmers’ BI of LDRT.
Hypothesis 2d (H2d).
LC negatively impacts the farmers’ RP.
Risk perception is an important process of decision making and also affects behavioral intention. Risk perception is defined as evaluating how risky a situation is, including the degree of uncertainty and controllability of uncertainty [38]. Risk contains three basic elements: loss, the significance of the loss, and uncertainty [39]. Farmers may find it difficult to be self-sufficient after development right transfer, facing the risk of higher living costs. The most intuitive risk is that when farmers migrate from the countryside, grain and vegetables cannot be obtained on the arable land, but only in the market. As land resources become scarce, farmers will encounter the possibility of homestead appreciation. Finally, from a traditional cultural standpoint [40], the cultural tradition of being attached to one’s native land makes farmers worry that it will be difficult for them to revert to their origin. Farmers react after assessing possible risks, which affects their behavioral intention of development right transfer. From the definition of risk perception, attitude to behavior, and perceived behavioral control, risk perception includes the assessment of benefits and the control degree of perceived uncertainty, which is consistent with the interest-based behavior attitude of attitude to behavior and the degree of control that farmers perceived when making decisions on perceived behavioral control. As a result, with the rising RP, AB, and PBC will increase, respectively. The livelihood capital will affect farmers’ behavioral intention of development right transfer under the influence of risk perception through three paths: the first one is that LC indirectly influences behavioral intention of development right transfer through RP and AB; the second one is that LC indirectly influences behavioral intention of development right transfer through RP and PBC; the third is that LC indirectly influences behavioral intention of development right transfer through RP. Therefore, this paper proposes Hypotheses 3a, 3b, and 3c (H3a, H3b, H3c).
Hypothesis 3a (H3a).
RP negatively impacts the farmers’ AB.
Hypothesis 3b (H3b).
RP negatively impacts the farmers’ PBC.
Hypothesis 3c (H3c).
RP negatively impacts the farmers’ BI of LDRT.

3. Model Construction and Data Sources

3.1. Study Area

Wujin District covers an area of 1065 km2 (latitudes 31°20′ N~31°54′ N, longitudes 119°40′ E~120°12′ E), as shown in Figure 2. It is one of the most developed regions in East China, its GDP was 295, 158 million CNY in 2021, among which primary, secondary, and tertiary industries accounted for 1.3%, 54.7%, and 44.0%, respectively, and the rural per capita disposable income has been higher than GDP for many years. In 2021, the resident population was 1.72 million, among which the rural population was 0.27 million, accounting for 15.64%. As of 2022, the urbanization rate of Wujin District reached 70.86%, and a large number of farmers have settled in cities, giving rise to many abandoned homesteads, which causes a waste of land resources.
Since 2020, the Ministry of Agriculture and Rural Affairs of China has listed Wujin District as one of the pilot areas for rural homestead system reform, including withdrawal from rural homesteads and the circulation of rural homesteads. As mentioned earlier, the villages in Wujin have a high degree of industrialization and urbanization, resulting in migrant workers flooding into villages, which has brought increasing demand for rural houses. Consequently, due to the growth of the idle area of rural homesteads, LDRT has become an important measure to support the sustainable use of rural land resources. Thus, this research selected 12 villages in Wujin District as the field survey area.

3.2. Variable Selection and Measurement

Based on the theoretical framework mentioned earlier, LC affects the BI of LDRT through RP, AB, SN, and PBC. Because it is hard to directly measure for RP, AB, SN, PBC, and BI of rural residential land development right transfer, the research employs observation variables to reflect these latent variables to test the theoretical hypothesis. According to previous studies on the structure of risk perception [37], the setting of RP is considered from the uncertainty of farmers’ future life expectancy and changes in the value of homesteads after the transfer of rural residential land development right. This paper employed “The worry about the rising cost of living” (RP1), “The worry of not being able to revert to their origin” (RP2), and “The worry about homesteads appreciation” (RP3) to measure the RP. For AB, SN, and PBC, multiple indicators from varied dimensions were selected to describe these latent variables based on the TPB and relevant studies [31,41] (Table 1). As for LC, it generally includes the overall measurement of human capital, natural capital, physical capital, financial capital, and social capital [34]. The “quality of cultivated land”, “household labor force ratio”, “per capita housing area”, “the ease of getting a loan”, and “the frequency of communication with village cadres and government staff” were chosen to measure five dimensions of livelihood capital, respectively. The options of these items were assigned a score of 1 to 5 in order. All scores of items were standardized, and their average values were taken as indicators of LC.

3.3. Research Methods

A structural equation model (SEM) that integrates statistical methods of regression analysis, factor analysis, and path analysis can test direct and indirect relations among latent variables that are difficult to measure directly through observation variables, which reflects the deep combination of quantitative analysis and qualitative research. Therefore, SEM should be applied to multivariable empirical analysis. The SEM consists of a measurement model and a structural model, reflecting the relationship between latent and observation variables and the structural relationship between latent variables separately. The equations are as follows.
η = Λ η + Γ δ + γ
In the equation, η is the endogenous latent variable; δ represents the exogenous latent variable; Λ and Γ are the coefficient matrices between the endogenous latent variable and the exogenous latent variable, and the γ is the measurement error of the structural equation. This research employs SEM to testify to the mechanisms of farmers’ livelihood capital on their BI of LDRT based on the theoretical framework of TPB.

3.4. Data Collection

The research collected data from a face-to-face field survey in Wujin District, carried out by random sampling in March 2023, to test the hypothesis in this paper. The respondents of this survey were heads of households or major decision makers on agricultural production. A total of 550 questionnaires were distributed in this field survey, with the specific distribution shown in Table 2. The study finally obtained a total of 409 valid samples, with an effective questionnaire recovery of 74.36%.

4. Results and Analysis

4.1. Descriptive Statistics

The basic characteristics of the sample can be found in Table 3 and Table 4 below. The respondents were split across gender unevenly, accounting for 60.88% (Male) and 39.12% (Female), with a large proportion falling between the ages of 18~45 (44.50%), falling between a family size of 4~5 people (50.61%), with a household homestead land scale from 0 to 150 m2 (57.94%) and from 150 to 200 m2 (23.72%). Over half of the participants fell between the annual household income of 0~CNY 10,000 (46.70%) and CNY 10,000~CNY 20,000 (34.72%). The sample was split relatively evenly across educational background, except for elementary school and below, with junior high school, high school, junior college/higher vocational college, and university and above around 25%, respectively.

4.2. Reliability and Validity

A confirmatory factor analysis (CFA) was used in this study to test the reliability and validity of the scales by AMOS 24.0 and SPSS 26.0. The reliability test was measured by composite reliability (CR) and Cronbach’s α. The value of CR and Cronbach’s α should both be higher than 0.7 [42]. As shown in Table 4, the scores of CR and Cronbach’s α ranged from 0.77 to 0.94 and from 0.73 to 0.94, respectively, suggesting high internal consistency reliability.
The convergent validity was measured by factor loadings and the average variance extracted (AVE). It is suggested that factor loadings and AVE should be at least 0.50 [43]. As depicted in Table 5, factor loadings for each construct (statistically significant (p < 0.001)) ranged from 0.52 to 0.95, above the critical value of 0.50. In addition, AVEs ranging from 0.53 to 0.80 were all greater than the cut-off value of 0.50, indicating that the convergent validity of constructs of the measurement model was established [44].
This research adopted the comparison between the square root of AVE for individual constructs and the correlations among the latent variables to test the discriminant validity, which assesses to what extent a particular latent variable in the model is different from the other latent variables. Comparing the square root of AVE with all correlations among the latent variables in Table 6, the results reflect that the discriminant validity of the measurement model was relatively good [44].

4.3. Goodness-of-Fit Test

Based on the research hypothesis in this paper, AMOS 24.0 was used to construct a theoretical model and test the goodness of fit between the theoretical model and measurement model. Ideally, for a theoretical model that fits the data, the Tucker–Lewis Index (TLI), the comparative fit index (CFI), the goodness of fit index (GFI), the adjusted GFI, and the incremental index (IFI) should fall between 0.80 and 0.90 (or higher) [45], the root mean square error of approximation (RMSEA), and the standardized root mean square residual (SRMR) should be at most 0.10 [43]. Table 7 shows that the structural model fits the data well: χ2/dƒ = 2.81, GFI = 0.92, AGFI = 0.88, IFI = 0.97, CFI = 0.97, and TLI = 0.96, indicating an overall good fit of the measurement model.

4.4. Results of Structural Equation Model Regression

This research tested the path relationship coefficient of the structural model, and the results suggest that the path relationship coefficients of the other eight hypotheses were statistically significant to at least 5% except for H2c and H3c (Livelihood capital → Behavioral intention; Risk perception → Behavioral intention). The influence directions were also consistent with the hypotheses, indicating that the research hypotheses proposed in this paper were supported. According to the analysis results, seven conduction paths were formed: Livelihood capital → Behavioral intention (Path 1); Livelihood capital → Risk perception → Attitude to behavior → Behavioral intention (Path 2); Livelihood capital → Risk perception → Perceived behavioral control → Behavioral intention (Path 3); Livelihood capital → Risk perception → Behavioral intention (Path 4); Livelihood capital → Attitude to behavior → Behavioral intention (Path 5); Livelihood capital → Perceived behavioral control → Behavioral intention (Path 6); Subjective norm → Behavioral intention (Path 7). Path 1 (H2c), which proposed a positive relationship between livelihood capital and behavioral intention, was not supported. Concerning Path 2, including H1a, H2d, and H3a, which proposed an indirectly significant relationship between livelihood capital and behavioral intention, were supported. Compared to Path 2, Path 3 (H1a, H1c, H3b) further proposed Hypothesis 1c and Hypothesis 3b, which put forward a negative relationship between risk perception and perceived behavioral control and a positive relationship between perceived behavioral control and behavioral intention, supported by the test results. Findings also provided support for Path 4, 5, and 6, including Hypothesis 2a, 2b, and 3c. These further hypotheses put forward an indirect relationship between livelihood capital and behavioral intention (through risk perception, attitude to behavior, and perceived behavioral control). Specific results concerning the hypotheses testing are presented below.
As shown in Table 8 and Figure 3, the standardized path coefficients of Attitude to behavior → Behavioral intention, Subjective norm → Behavioral intention, and Perceived behavioral control → Behavioral intention were 0.43, 0.13, and 0.19, respectively, which passed the 5% level test. The results indicate that individual cognition (attitude to behavior, subjective norm, and perceived behavioral control) had significant positive effects on the farmers’ behavioral intention of rural residential land development right transfer. In addition, the factor loadings of AB2 (0.94), SN3 (0.87), and PBC2 (0.95) had the greatest contribution to attitude to behavior, subjective norm, and perceived behavioral control, respectively.
The standardized path coefficients of Livelihood capital → Attitude to behavior, Livelihood capital → Perceived behavioral control, and Livelihood capital → Behavioral intention were 0.19, 0.23, and 0.05, respectively, which passed the 1% level test except for Livelihood capital → Behavioral intention. The results indicate that livelihood capital had significant positive effects on the attitude to behavior and perceived behavioral control. Additionally, risk perception had a negative impact on the attitude to behavior and perceived behavioral control at the 1% significance level, supporting H3a and H3b, and the same is true of Livelihood capital → Risk perception. However, it was not significant for Risk perception → Behavioral intention at the 5% significance level. Among the observed variables of the risk perception, the factor loadings of RP1, RP2, and RP3 were 0.91, 0.91, and 0.81, in turn.

5. Discussion

5.1. Impact of Livelihood Capital on Behavioral Intention through Individual Cognition

As described in the results, the impacts of livelihood capital on behavioral intention through individual cognition can be reflected via two paths: attitude to behavior and perceived behavioral control. In addition, subjective norm to behavioral intention is also supported. The results suggest that livelihood capital can promote development right transfer through individual cognition, which is consistent with the effects of attitude to behavior, perceived behavioral control, and subjective norm on behavioral intention in most studies under TPB framework [7,46].
To explore the reason that livelihood capital is not only the basis for farmers to make a living but also an important factor affecting farmers’ behavioral intention of development right transfer, the composite index of livelihood capital was used to explore the influencing mechanism of livelihood capital on behavioral intention. Different from previous studies [30,46], this research extended and incorporated livelihood capital and risk perception, with individual cognition (AB and PBC) as intermediate variables to the framework by employing a structural equation model (SEM). Finally, the effects of individual resource endowment factors and psychological factors on behavioral intention as well as the paths is examined, which enriched the application of TPB theory and improved the explanatory power of the theoretical model. Furthermore, this study focuses on land development right transfer rather than only on withdrawal from rural homesteads or rural residential land circulation, which is distinguished from other research [7,12].

5.2. Impact of Livelihood Capital on Behavioral Intention through Risk Perception

Based on the psychometric paradigm, risk perception is the process of individuals constructing risks in a subjective and intuitive sense of the adverse consequences associated with risks [37]. According to the cultural theory of risk, risk is also related to the cultural preference of society [47]. In traditional Chinese culture, the idea of being prepared for danger in times of peace is deeply rooted, but the actual risk does not rise, it is just the perception of increasing risks. Therefore, risk perception plays an important role in the relationship between livelihood capital and farmers’ behavioral intention of rural residential land development right transfer. The higher the risk perception level, the stronger their risk aversion behaviors, as the loss caused by various factors will be considered by individual farmers before their behavior implementation. When farmers perceive uncertain losses, they will reduce their behavioral intention of rural residential land development right transfer or take proactive and effective measures, such as the purchase of insurance, which demonstrates that the farmers’ response to risk perception is to relieve anxiety and unease in the decision-making process by obtaining realistic and emotional security.
The results confirm that five dimensions of livelihood capital including human capital, natural capital, physical capital, financial capital, and social capital will influence farmers’ risk perception [48,49,50]. This can be explained by sufficient farmers’ livelihood capital which indicates that they have advantages in household resource endowment and social support. Accordingly, farmers are better able to cope with risks, and their subjective perception of uncertainty and loss of rural residential land development right transfer is relatively low, namely the cognitive sensitivity of farmers to such risks is low. In addition, farmers with abundant livelihood capital and low-risk perception are more prone to have the behavioral intention of rural residential land development right transfer. These findings are consistent with previous studies that found the better the individual socioeconomic characteristics and household resource endowment, the lower the level of risk perception [49,51]. Consistent with the findings that risk perception affects individual decision making positively [52,53], the impact of risk perception on attitude to behavior and perceived behavioral control are both approved. Risk perception to behavioral intention is not significant, which indicates that risk perception does not have a direct effect on behavioral intention in this study, but a more subtle intermediate mechanism between risk perception and behavioral intention is revealed by the structural equation model (SEM), namely the increase in risk perception level strengthens individual cognition (AB and PBC), and then stimulates farmers’ behavioral intention of development right transfer. According to the results of the structural equation model (SEM), the positive effects of livelihood capital on behavioral intention through risk perception are inhibited by risk perception.
As presented in the findings above, livelihood capital affects farmers’ behavioral intention of rural residential land development right transfer indirectly through risk perception and individual cognition (attitude to behavior and perceived behavioral control), indicating the influence mechanism of livelihood capital on behavioral intention is relatively complicated. It is not simply that the higher the livelihood capital, the stronger the behavioral intention. This result is in contradiction with the findings of numerous studies [10,54,55]. The reason may be that the farmers have, to a certain extent, accumulated higher livelihood capital in the study area where there is a higher level of socioeconomic development compared to other regions, hence their behavioral intention is influenced more by psychological factors.

5.3. Strategies for Improving Behavioral Intention

Similar studies have explored the factors that affect farmers’ willingness of withdrawal from rural homesteads and rural homestead transfer [7,8]. It was found that both the internal factors and the external environment effect their behavioral intention. Most of these studies took underdeveloped areas as empirical research regions, and obtained feasible strategies to promote farmers to withdraw from rural homesteads. The ultimate goal of this research is to concentrate on idle homestead resources, and effectively improve the phenomenon of ‘village-hollowing’, achieving rural revitalization. The case study of this paper, Wujin District, is a pilot area that has undertaken the national rural homestead system reform, encountering conflicts between land scarcity and idle homesteads. In addition, Wujin district is a typical case with high urbanization and industrialization, where farmers have diverse and flexible livelihoods. Exploring the intention of farmers during the implementation of the rural residential land development right transfer, this study could provide valuable insights and implications to regions with a similar social and economic context.
Enriching the types of livelihoods and improving the level of livelihood capital can reduce the constraining effect of risk perception on farmers’ behavioral intention of rural residential land development right transfer. Making full use of the advantages of thriving local industries provides more employment opportunities and better salaries, which may increase farmers’ natural capital and physical capital, consequently, mitigating their concerns about the rising cost of living due to development right transfer. The human capital and social capital of farmers can be improved by expanding the social security system through geographical connections, business networks, and shared interests. Based on the measures above about reducing livelihood risks, farmers’ concerns about various uncertainties could be alleviated. To alleviate farmers’ concerns about the homestead appreciation after the transfer of their rural residential land development right is significant to raise farmers’ incentives during the development right transfer process.
It is also recommended that farmers’ cognition should be enhanced to improve their behavioral intention based on spontaneity. The policy of development right transfer is complicated and professional, which is not be deeply understood by the farmers themselves. Therefore, it is necessary to make farmers correctly understand the survival, economic, ecological, and developmental benefits of development right transfer through policy publicity, which encourages farmers to form a positive attitude towards development right transfer. Simultaneously, the external support that the government can provide should be better perceived by farmers through the publicity and mobilization of village cadres, reducing farmers’ perceived difficulty in development right transfer. Through the external policy pull above, the internal power of development right transfer is stimulated, and finally, farmers spontaneously participate in development right transfer.

6. Conclusions and Policy Recommendations

Based on the field survey of 409 farmers in Wujin, this paper empirically analyzed the influencing mechanism of livelihood capital on farmers’ behavioral intention of rural residential land development right transfer by structural equation model (SEM). To promote the effective use of rural homestead resources in developed areas, and improve the living environment, quality of life, and social governance, this study developed an extended TPB theoretical framework, by integrating livelihood capital and risk perception to examine and discuss farmers’ behavioral intention of rural residential land development right transfer. The main conclusion that can be drawn is that the improvement of livelihood capital can effectively reduce risk perception. The comprehensive score of farmers’ livelihood capital affects farmers’ behavioral intention of rural residential land development right transfer through individual cognition (AB and PBC). Farm households with a higher score of livelihood capital will have a stronger ability to deal with risks, and accordingly, the level of risk perception is lower. In addition, livelihood capital has a positive contribution to behavioral intention of rural residential land development right transfer indirectly through risk perception, especially in the Wujin district, a developed region. In addition, psychological variables have a prominent impact on the behavioral intention of rural residential land development right transfer, and it infers the improvement of farmers’ risk perception increases their risk aversion behavior. Finally, farmers may take active and effective measures to reduce risks and increase their resilience. In order to reduce the constraint impact of risk perception on farmers’ behavioral intention, strategies for diversifying livelihoods and improving the quality of livelihood capital could be conducted.
According to the analysis and findings above, this paper proposes the following policy recommendations: First, the physical capital could be improved to increase farmers’ income, and optimize the social security system, in order to reduce farmers’ worries about withdrawal from rural homesteads. Second, a value-added benefit sharing mechanism might be designed to promote the incentives of different stakeholders such as farmers, village collectives, and social organizations. Third, it is necessary for village cadres and government staff to carry out diversified publicity activities, in order to popularize policies and regulations related to rural homestead transfer and withdrawal from rural homesteads, as well as improving farmers’ understanding of the policy and increase their acceptance of rural residential land development right transfer. Furthermore, complete dependence on government support is not sufficient. It is essential to foster the development of noninstitutionalized social network support systems by enhancing geographical connections and business networks, and then facilitating the withdrawal from rural homesteads.

Author Contributions

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

Funding

This research was funded by the National Social Science Fund of China, grant number 21CJL012; College Students’ Innovative Entrepreneurial Training Plan Program, grant number 202210298162H.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Not applicable.

Acknowledgments

The authors wish to express their appreciation to the editors and reviewers for their insightful comments and suggestions.

Conflicts of Interest

The authors declare no conflict of interest.

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Land 12 01207 g001 550
Figure 1. The theoretical framework of LC affecting the BI of LDRT.
Figure 1. The theoretical framework of LC affecting the BI of LDRT.
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Land 12 01207 g002 550
Figure 2. Study area and location.
Figure 2. Study area and location.
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Figure 3. Result of structural equation model regression. Note: ***, * indicate significance at 1% and 5% levels, respectively.
Figure 3. Result of structural equation model regression. Note: ***, * indicate significance at 1% and 5% levels, respectively.
Land 12 01207 g003
Table
Table 1. Selection and definition of variables.
Table 1. Selection and definition of variables.
Latent VariableObservation VariableMagnitude Definition
Risk Perception (RP)The worry about the rising cost of living RP11 = strongly disagree/worry,
2 = somewhat disagree/worry,
3 = general,
4 = somewhat agree/worry,
5 = strongly agree/worry
The worry of not being able to revert to their origin RP2
The worry about homesteads’ appreciation RP3
Attitude to behavior (AB)Guarantee for lives and property AB1
Increase in household income AB2
Long-term development of future generations AB3
Improvement of human settlements and the ecological environment AB4
Subjective norm (SN)Family interference SN1
A good understanding of relevant policies SN2
Village Committee and government interference SN3
Perceived behavioral control (PBC)Sufficient financial strength PBC1
Sufficient expertise PBC2
Satisfied with the government’s publicity and assistance PBC3
Behavioral intention (BI)Withdrawal with reasonable subsidy BI1
Follow the general trend BI2
Withdrawal with employment security BI3
Table
Table 2. Questionnaire distribution and quantity.
Table 2. Questionnaire distribution and quantity.
Questionnaire Distribution and QuantityNumber of Questionnaires
Huangli townXishu village50
Gezhuang village30
Xiangquan village30
Qianhuang townJiangpai village30
Zhuzhuang village20
Xueyan townYapu village100
Chengdong village50
Chengwan village20
Panjia village50
Jiaze townXicheng village50
Minshi village50
Fengyang village50
Table
Table 3. Descriptive statistics of respondents.
Table 3. Descriptive statistics of respondents.
Statistical IndicatorsClassificationSample NumberProportion (%)
GenderMale24960.88
Female16039.12
Age≤45 years old18244.50
46~50 years old5212.71
51~55 years old6215.16
56~60 years old5713.94
61~65 years old368.80
>65 years old204.89
EducationElementary school and below143.42
Junior high school11528.12
High school9022.01
Junior college/higher vocational college9422.98
University and above9623.47
Table
Table 4. Descriptive statistics of rural households and homesteads.
Table 4. Descriptive statistics of rural households and homesteads.
Statistical IndicatorsClassificationSample NumberProportion (%)
Family size≤3 people14134.47
4 to 5 people20750.61
>5 people6114.91
Household homestead land scale≤100 m210325.18
100–150 m213432.76
150–200 m29723.72
200–250 m2215.13
>250 m25413.20
Annual household income≤CNY 10,00019146.70
CNY 10,000 to CNY 20,00014234.72
CNY 20,000 to CNY 30,0005413.20
>CNY 30,000225.38
Table
Table 5. Results of reliability and validity tests.
Table 5. Results of reliability and validity tests.
Latent VariableObservation VariableUnstd.S.E.Z-Valuep-ValueFactor LoadingCronbach’s αCRAVE
Risk Perception (RP)RP11.00 0.910.910.910.77
RP21.040.0425.65***0.91
RP30.890.0421.71***0.81
Attitude to behavior (AB)AB11.00 0.910.940.940.79
AB21.030.0331.47***0.94
AB31.010.0427.86***0.89
AB40.940.0422.21***0.81
Perceived behavioral control (PBC)PBC11.00 0.930.900.910.77
PBC21.020.0334.59***0.95
PBC30.750.0419.39***0.73
Subjective norm (SN)SN11.00 0.520.730.770.53
SN21.340.149.84***0.76
SN31.590.1610.08***0.87
Behavioral intention (BI)BI11.00 0.890.920.920.80
BI20.910.0424.72***0.88
BI31.050.0426.74***0.92
Note: *** indicates significance at 1% levels.
Table
Table 6. Correlations and average variance extracted.
Table 6. Correlations and average variance extracted.
MSDLCRPABPBCSNBI
LC0.000.56
RP3.041.10−0.200.88
AB2.800.980.22−0.240.89
PBC2.760.870.29−0.310.800.88
SN2.910.790.26−0.050.500.610.73
BI2.940.960.24−0.150.660.630.420.89
Note: The bold diagonal elements are the square roots of each AVE; construct correlations are off diagonal.
Table
Table 7. Test result of overall fit of structural equation model.
Table 7. Test result of overall fit of structural equation model.
Goodness-of-Fit Indexχ2/dfSRMRRMSEAGFIAGFIIFICFITLI
Acceptable Fit Values≤3<0.08<0.08>0.90>0.90>0.90>0.90>0.90
Fit Values2.810.040.070.920.880.970.970.96
ResultAcceptAcceptAcceptAcceptAcceptAcceptAcceptAccept
Table
Table 8. Estimation results of the SEM.
Table 8. Estimation results of the SEM.
Path RelationshipUnstd.S.E.Z-Valuep-ValueβResults
H1a: AB → BI0.420.094.87***0.43Accept
H1b: SN → BI0.230.112.10*0.13Accept
H1c: PBC → BI0.190.101.99*0.19Accept
H2a: LC → AB0.310.083.73***0.19Accept
H2b: LC → PBC0.360.084.66***0.23Accept
H2c: LC → BI0.080.071.220.220.05Not accept
H2d: LC → RP−0.410.10−4.20***−0.21Accept
H3a: RP → AB−0.170.04−4.39***−0.20Accept
H3b: RP → PBC−0.220.04−6.40***−0.27Accept
H3c: RP → BI0.010.040.330.740.01Not accept
Note: ***, * indicate significance at 1% and 5% levels, respectively.
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Zhang, T.; Li, J.; Wang, Y. Effects of Livelihood Capital on the Farmers’ Behavioral Intention of Rural Residential Land Development Right Transfer: Evidence from Wujin District, Changzhou City, China. Land 2023, 12, 1207. https://doi.org/10.3390/land12061207

AMA Style

Zhang T, Li J, Wang Y. Effects of Livelihood Capital on the Farmers’ Behavioral Intention of Rural Residential Land Development Right Transfer: Evidence from Wujin District, Changzhou City, China. Land. 2023; 12(6):1207. https://doi.org/10.3390/land12061207

Chicago/Turabian Style

Zhang, Ting, Jia Li, and Yan Wang. 2023. "Effects of Livelihood Capital on the Farmers’ Behavioral Intention of Rural Residential Land Development Right Transfer: Evidence from Wujin District, Changzhou City, China" Land 12, no. 6: 1207. https://doi.org/10.3390/land12061207

APA Style

Zhang, T., Li, J., & Wang, Y. (2023). Effects of Livelihood Capital on the Farmers’ Behavioral Intention of Rural Residential Land Development Right Transfer: Evidence from Wujin District, Changzhou City, China. Land, 12(6), 1207. https://doi.org/10.3390/land12061207

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