Revitalizing Idle Rural Homesteads: Configurational Paths of Farmer Differentiation and Cognition Synergistically Driving Revitalization Intentions

Abstract

Against the intensifying mismatch between urban and rural land resources, activating farmers’ intentions to revitalize their idle homesteads is a key issue in optimizing land resource allocation and promoting urban–rural integrated development. However, existing studies mostly focus on the marginal effect of a single factor and ignore the synergistic effect of multiple factors, making it difficult to reveal the complex causal logic of farmers’ decision-making. This study aims to explain the causal asymmetry and equivalent path problem in farmers’ revitalized decision-making by capturing the multidimensional interaction mechanism of “external stimulus–mental cognition”. This study integrates the social stratification theory, the theory of planned behavior, and the Stimulus–Organism–Response framework to systematically explore how the interactive configuration of farmer differentiation and cognition from a multidimensional perspective drives the formation of farmers’ willingness to engage in high inventory activities, based on the 881 farmer research data in Shaanxi Province, using fuzzy-set qualitative comparative analysis (fsQCA) methodology. This study found that (1) a single condition cannot independently explain the intentions of farmers to revitalize, and its formation needs to rely on the synergistic linkage of multiple conditions; (2) the configuration of farmers’ high intentions to revitalize includes “wealth capital differentiation–dual cognitive-driven type”, “single cognitive-driven type”, “reputation capital differentiation–single cognitive-driven type”, “wealth capital differentiation–single cognitive-driven type”, which wealth capital differentiation is the common core condition triggering high intention; and (3) the formation of farmers’ low revitalization intentions stems from the insufficient differentiation of farmers and the lack of cognitive elements. Therefore, policymakers should take a holistic perspective in enhancing farmers’ revitalization intentions, focusing on the rational allocation between farmer differentiation and farmers’ cognition.

1. Introduction

As the core link of the social ecosystem, land not only carries human livelihoods and cultural heritage but also drives the sustainable transformation of developing countries through its multifunctionality, becoming a key spatial carrier for achieving the United Nations Sustainable Development Goals (SDGs) [1]. In developing countries, the multifunctionality of land promotes sustainable transformation by balancing ecological conservation and socio-economic development [2]. However, the Chinese rural land system faces a great challenge: rapid industrialization and urbanization have increased the demand for urban construction land and simultaneously triggered the phenomenon of farmer differentiation. Deeply differentiated farmers have gradually become less dependent on land, but they are unwilling to give up the assets and rights attached to their rural household registration, resulting in a dual evolution of “population loss and land expansion [3]”. National statistics show that the non-farm employment rate of rural laborers has soared from 7.6 per cent in 1978 to 41.3 per cent in 2020 [4]. This difference significantly reduces farmers’ dependence on land, giving rise to the paradoxical phenomenon of homestead utilization: many rural homesteads have been idle, while the demand for urban construction land has been growing year after year [3]. This aggravation of the contradiction between urban and rural land allocation not only highlights the profound dilemma of the dual-land system but also puts forward practical requirements for deepening the reform of the residential land system from the perspective of optimizing the allocation of factors and transforming the relationship between people and land.

To solve this dilemma, China launched a pilot reform of the homestead system in 2015. The reform attempted to break the urban–rural land dilemma by focusing on the “separation of three rights”: the separation of ownership, eligibility, and use rights, while maintaining collective ownership [5]. Based on guaranteeing the first two rights, the pilot areas activated the right to use idle homesteads through self-employment, leasing, and shareholding, giving farmers complete usufructuary rights. This reform is in line with the changes in the relationship between people and land, not only safeguarding farmers’ property rights and interests but also releasing the value of land through the flow of urban and rural factors [6]. The current era is characterized by a shortage of urban and rural resources and a constantly changing rural population structure. Revitalizing homesteads not only optimizes the allocation of land resources but also supports new urbanization through the redistribution of value-added gains and promotes the process of citizenship for the agricultural transfer population.

As the decision-making body of revitalizing idle homesteads, farmers’ intentions to revitalize play a key role in the effectiveness of revitalizing idle homesteads [7]. Existing academic research has examined farmers’ intentions to revitalize idle homesteads through four disciplinary perspectives: (1) Neoclassical economics prioritizes cost-benefit calculations and treats farmers as rational actors who maximize utility [8]. (2) Neo-institutional economics emphasizes government regulatory controls as the driving factor of farmers’ revitalization behavior [9]. (3) Behavioral economics introduces limited rationality and cognitive bias into farmers’ decision-making models [10]. (4) Social psychology applies the extended theory of planned behavior (TPB) to capture the influencing factors of farmers’ revitalizing behavior [11]. There is a significant tendency of theoretical simplification in the existing studies in analyzing the decision-making of farmers’ revitalization behavior: neoclassical economics’ assumption of “complete rationality” ignores the complexity of social psychology. New institutional economics focuses too much on government regulation and weakens individual cognition. It is difficult for behavioral economics and social psychology to independently support systematic analyses due to the fragmentation of their theoretical frameworks. This fragmentation of single-disciplinary perspectives makes it difficult for existing studies to capture the multidimensional interaction mechanism of “external stimulus–psychological cognition”, and even more so, they cannot explain the causal asymmetry and the problem of equivalent paths in the decision-making of farm households. In addition, most of the existing studies rely on linear regression or structural equation modelling to explore the marginal net effect of a single factor on farmers’ intentions to revitalize their idle homesteads [3,12]. Although these studies provide partial explanations, there are still two key limitations: (1) The fragmentation of a single disciplinary perspective makes it difficult to explain the complexity of the decision-making process of revitalizing idle homesteads. (2) Assuming linear, symmetric causality between variables makes it difficult to capture the complex mechanisms of “causal asymmetry” and “equivalent paths” of multiple conditions [13].

The formation of farmers’ intentions to revitalize their idle homesteads is the result of the combination of multi-level and complex factors, and only by grasping the group path that drives the formation of farmers’ intentions to revitalize can we truly elucidate the nature of the complex interactions between the multiple factors and the logic of farmers’ decision-making that is embedded in them. Therefore, this study constructs a “farmer differentiation–farmer cognition” cooperating drive model, and uses fuzzy set qualitative comparative analysis to try to solve the following problems: (1) How are the multiple condition variables under the dimensions of farmer differentiation and cognition linked and matched to jointly affect the intentions of farmers to revitalize idle homesteads? (2) What kind of condition combinations can lead to high farmer intentions to revitalize? (3) How to provide theoretical reference and practical support for revitalizing farmers’ intentions from the perspective of cooperation.

The marginal contributions of this study include, first, theoretical integration. This study integrates the social stratification theory, the theory of planned behavior, the Stimulus–Organism–Response (SOR) framework; constructs a synergistic model of “farmer differentiation–farmer cognition”; and incorporates wealth capital differentiation (WCD), reputation capital differentiation (RCD), behavioral attitude (BA), subjective norm (SN), perceived behavioral control (PBC), and homestead dependence (HD) into a unified analytical framework to reveal the interactive mechanism between external stimuli and internal psychology. Second, a methodological breakthrough. This study introduces fsQCA into the study of homestead revitalization intention, which confirms the “causal complexity” of farmers’ decision-making and provides a methodological example for analyzing complex social phenomena. Third, the practical response. Identifying four group configurations leading to high intention and four group configurations leading to low intentions to revitalize proves that policies need to design differentiated intervention strategies from the perspective of a “combination of conditions” rather than optimizing a single factor in isolation.

The remaining sections of the article are Section 2, which constructs the theoretical framework; Section 3, which describes the research design; Section 4, which presents the results of the histogram analysis; Section 5, which discusses the findings; and Section 6, which gives the conclusions, implications, shortcomings, and outlook of this study.

2. Theoretical Analysis Framework Construction

The SOR theory emphasizes that external stimuli are the basis for triggering internal mental activities and stresses that these stimuli need to be transformed through the individual’s internal mental-processing mechanism, ultimately forming behavioral intentions. External stimuli include objectively existing environmental and individually perceived situational stimuli, both of which act on the recipient of the stimulus, i.e., the organism. After receiving the stimulus, the organism produces conscious or unconscious internal psychological responses, either emotional or cognitive [14]. Conversely, reactions are individual responses based on cognitive and emotional changes, including intrinsic and behavioral responses. Intrinsic responses are usually individual attitudes, while behavioral responses are collective, convergent, or avoidance behaviors [15]. Subsequent research breaks through the rigid constraints of the classical model and suggests that stimuli bypass the organism to affect the response in a given context directly [16,17,18]. Therefore, this study suggests that the formation of farmers’ intentions to revitalize results from the synergistic effect of “external stimuli and internal psychology”.

2.1. Stimulus: Farmer Differentiation

A stimulus is an external stimulus that affects an individual’s emotional and cognitive mental state [15]. The rapid advancement of industrialization and urbanization has led to the phenomenon of farmer differentiation, with the proportion of part-time farmers continuously increasing and the proportion of pure farmers decreasing, gradually reducing their dependence on land [3,19]. Weber proposed that, in the economy and society, there are three orders of social stratification: the economic order, the legal order, and the social order. The economic order is economic differentiation, the legal order is power differentiation, and the social order is reputation differentiation [20]. Later, social theorists evolved on this basis to take wealth, power, and reputation as the three basic dimensions of social differentiation [21]. However, during the development of the theory, the dimension of power differentiation (the position of the individual in the hierarchical organization) has not been widely accepted by subsequent researchers [3,22], and in actual research, the power capital of farmers is generally weak, and there is a problem of applicability.

Therefore, this paper divides the farmer differentiation dimension into wealth capital differentiation and reputation capital differentiation, where WCD is an individual’s ability in the commodity market and RCD is an individual’s status in social interactions.

2.1.1. Wealth Capital Differentiation and Farmers’ Intentions to Revitalize

The differentiation of wealth capital and the intentions to revitalize are discussed from the “rational man” perspective in neoclassical economics. Farmers at different levels of differentiation have different degrees of dependence on land and expect different benefits from revitalization. Most farmers with deeper wealth capital differentiation have left traditional agricultural production in the countryside to work in non-agricultural industries with higher marginal rates of return in the cities [3]. The burden of livelihood and uncertainty of income for this group is smaller, and the family’s ability to resist risk and poverty is stronger, so the dependence of farmers with deeper wealth capital differentiation on the security function of the homestead land has been reduced [23]. However, this part of the group bears huge social costs in the citizenship process. The continuous capital investment is the solid foundation for them to complete the process of citizenship, which is almost decisive for the success or failure of their citizenship. The proceeds from the revitalization of homesteads can be used for the deep-wealth capital-differentiated farmers to go to the city to start their businesses or to buy property in the town [24]. Therefore, farmers with deep wealth capital differentiation value the property function of the homestead more. Shallow wealth differentiation farmers are still materially bound by the “security of homestead” and cannot bear the risk of increased production and life that revitalizing idle homesteads may bring, resulting in low motivation to revitalize idle homesteads.

2.1.2. Reputation Capital Differentiation and Farmers’ Intentions to Revitalize

Based on the perspective of the “social person” in the doctrine of interpersonal relationships, we discuss the differentiation of reputation capital and its intentions to revitalize idle homesteads. Fei Xiaotong regards the concept of “face” as a kind of reputation system in the acquaintance society of rural China, where “face” refers to social evaluation and community prestige, which is the “grade rating” of the village collective for an individual farmer [25]. In rural society, the depth of reputational capital differentiation and the “earning of face” and “loss of face” are norms and guidelines that can guide farmers in their long-term interactions. Farmers with deeper reputational capital differentiation usually have more opportunities for cooperation and access to information, are more satisfied with the current situation, are more optimistic about future development, and have a more convenient, productive life [3]. As this group has stronger development endowments and marketability, it is more determined to develop in the direction of the “urban dream”. It can invest its limited resources mainly in upgrading the family’s social status. So, the idle homestead revitalization has become the inevitable choice of their allocation of land resources to obtain property income [26]. At the same time, the reputation capital differentiation level of this part of the group is higher than that of the surrounding groups, which means that their citizenship has not only begun but also progressed smoothly, which makes their “land attachment” to the residential land lower.

2.2. Organism: Farmer’s Cognition

The body is stimulated to produce conscious or unconscious internal psychological responses, that is, the individual’s different internal psychological state and individual cognition due to the stimulation of external factors [15]. The Theory of Planned Behavior (TPB) is an important theoretical framework for explaining human behavioral decision-making, laying a theoretical foundation for analyzing individual decision-making mechanisms [27]. The model suggests that an individual’s behavioral intention is primarily shaped by three key cognitive dimensions: behavioral attitude, subjective norm, and perceived behavioral control. It is worth emphasizing that TPB has significant theoretical extensibility, which allows researchers to embed new variables according to specific research contexts while ensuring the stability of the theoretical core [28].

The theory of place (TP) holds that individuals will have a strong emotional response to a place if they have frequent contact with it or live in it for a long time and that a place not only provides a background for individuals’ lives but also a sense of security and a sense of identity [29]. As a place with the function of production and life for farmers, the homestead has a rich and profound meaning of “place”. In the early stage of urbanization, the homestead was mainly a carrier for farmers’ production and life, close to the concept of “space” in geography. When the agricultural population began to move and in the process of civilization, the homestead took on more emotional functions, and “space” gradually changed to “place”, and the farmers gave the place a unique meaning through their own “experience construction”. As farmers establish an emotional connection with the place, they become emotionally attached to the homestead, which is called place attachment. Because this study is in the context of revitalizing idle homesteads and improving the Theory of Planned Behavior, it includes homestead dependence in the cognitive dimension of farmers.

2.2.1. Behavioral Attitude and Farmers’ Intentions to Revitalize

BA is the expected evaluation and value judgment of farmers’ response to the policy of land use on homesteads [30]. Behavioral attitude originates from behavioral beliefs, including outcome beliefs (the likelihood of the behavioral beliefs occurring) and outcome evaluations (evaluations of the changes brought about by revitalization) [28]. Farmers’ behavioral beliefs are mainly reflected through affective and instrumental attitudes, with affective attitudes referring to the emotional benefits brought by farmers to revitalize idle homesteads, and instrumental attitudes referring to the benefits brought by revitalization, which include not only monetary benefits but also other benefits brought by revitalization, such as providing employment opportunities and avoiding land idleness. This emotional and instrumental attitude will directly affect farmers’ intentions to revitalize.

2.2.2. Subjective Norm and Farmers’ Intentions to Revitalize

SN refers to the pressure perceived by farmers from social reference groups in their decision-making process regarding homestead land use [3]. Subjective norm originates from normative beliefs (farmers’ views on whether important organizations promote the revitalization of homesteads) and motivations for conformity (farmers’ intentions to follow the expectations of important figures) [31]. Subjective norm reflects the influence of external pressures on farmers to revitalize idle homesteads, which mainly come from informal groups such as family members, friends, and relatives, and formal groups such as village committees, and the claims and actions of these individuals or organizations that are closely linked to farmers on revitalizing idle homesteads will have an impact on the formation of farmers’ intentions to revitalize.

2.2.3. Perceived Behavioral Control and Farmers’ Intentions to Revitalize

PBC refers to farmers’ perceptions of the ease or difficulty of revitalizing idle homesteads and their controllable resources [27]. Perceived behavioral control originates from control beliefs, including control beliefs (farmers’ perceptions of factors that promote or hinder revitalization of idle homesteads) and perceived intensity (farmers’ perceptions of “self-efficacy”) [3]. Control beliefs mainly come from the factors farmers perceive as hindering or promoting their revitalization, such as policy information, professional skills, and other resource constraints. Perceived intensity is the degree of farmers’ confidence in their ability to participate in the revitalization of idle homesteads. Therefore, the stronger the PBC of farmers, the easier it is for them to form the intentions to revitalize.

2.2.4. Homestead Dependence and Farmers’ Intentions to Revitalize

Resource dependence theory emphasizes that the degree of dependence of an organization or individual on a resource depends on three aspects: (1) The importance of the resource to the organization/individual. (2) The degree of scarcity of the resource. (3) The abundance of alternative resources [32], which provides important theoretical support for understanding individual behaviors and strategies. In the context of homestead revitalization decision-making, homestead dependence mainly refers to the demand relationship that farmers have for the multiple functions performed by homesteads in their daily lives and economic activities. As a multifunctional composite space on which farmers rely for their survival and development, the homestead is the center of the interaction of human-land relations in rural areas, and its functional attributes are transformed or diversified with the changes of the times and people’s needs. This study refers to existing studies [33] and combines the functions of the homestead to characterize homestead dependence as psychological emotional dependence, residential security dependence, property dependence, and auxiliary production dependence. Psychological emotional dependence refers to the fact that rural migrant laborers have a certain degree of psychological attachment to the homestead due to traditional concepts such as “ancestral home consciousness.” Residential security dependence refers to the demand of the rural labor force for the residence base as a means of production in terms of basic residence and old age. Property dependence refers to the situation where farmers believe their homestead may appreciate in the future and expect to obtain specific economic compensation by withdrawing from the homestead or other means. Auxiliary production dependence refers to the demand of the rural labor force for the residence base to be used for storing food, agricultural machinery, and tools, and for the development of the garden economy. The auxiliary production dependence means that the rural transferred laborers have the demand for homesteads for storing food, agricultural machinery and tools, developing courtyard economy and livestock raising, etc., in order to obtain part of the economic income. Therefore, the degree of dependence of farmers on their homesteads will affect the formation of their intentions to revitalize.

2.3. Theoretical Model Building

The formation of farmers’ intentions to revitalize is a complex process of continuous interaction of multiple factors. Based on the above analysis, this study follows the SOR theory and comprehensively considers wealth capital differentiation, reputation capital differentiation, behavioral attitude, subjective norm, perceived behavioral control, and homestead dependence as antecedent condition variables, and the intentions of farmers to revitalize their idle homesteads as the outcome variable. This study also applies the fsQCA method to clarify the interrelationships between farmer differentiation and farmers’ cognition, as well as the driving path and complex causal mechanism behind the adaptive configuration of farmers’ revitalization intentions. Accordingly, the configuration effect model of farmers’ intentions to revitalize is constructed, as shown in Figure 1.

3. Research Design

3.1. Research Method

Qualitative Comparative Analysis (QCA) is a research method based on holistic thinking that integrates the strengths of qualitative and quantitative analyses, adopts a holistic perspective, converts cases into groupings of conditioned variables, models the relationships between variables based on set affiliation, and uses Boolean algebra to identify the necessary and sufficient conditions for the response outcome and search for diverse and equivalent paths that produce the same outcome to enable comparative analysis of complexified systems between cases [34]. fsQCA combines fuzzy sets, principles of fuzzy logic, and QCA to deal with the problems of varying degrees and partial affiliations by calibrating the affiliation of the conditional variables as continuously varying fuzzy set variables [35]. The approach can reveal complex complementarities and nonlinear relationships among variables and also provide deeper empirical and theoretical explorations of the optimal combination of factors for a given outcome. And scholars have advocated the use of asymmetric configurational analysis in explaining complex phenomena, especially when it comes to human behavior that is usually unlikely to follow a symmetric stance [35]. Therefore, this part of this study chooses the fuzzy set qualitative comparative analysis with high data precision and strong scenario applicability.

3.2. Case Selection

This study chooses Shaanxi Province as a case study area, mainly based on its typical characteristics and regional representativeness in farmer differentiation and the revitalization of idle homesteads. First of all, Shaanxi Province, as a largely agricultural province in the west, has significant geospatial differentiation, with a clear gradient of economic development in three major regions. This multidimensional pattern of farmer differentiation provides ideal samples for the study of the heterogeneity of farmers. Secondly, since 2015, Shaanxi Province has undertaken a national pilot reform of the rural homestead system. In April 2020, the Shaanxi Provincial Department of Agriculture and Rural Development further deepened the reform by launching a three-year provincial pilot program for revitalizing and utilizing idle homesteads and idle dwellings in 12 counties (districts), including GaoLing District, Xi’an City [3]. From the perspective of regional representativeness, Shaanxi Province has established a systematic four-level linkage pilot system and a precise policy framework. Diversified paths for the revitalization of homesteads have been formed on this basis. These innovative practices have not only been listed as typical cases nationwide by the Ministry of Agriculture and Rural Affairs but have also been promoted nationwide through policy documents such as the Comprehensive Rural Revitalization Plan. The exploration of Shaanxi Province fully demonstrates the exemplary value of its reform experience. At the same time, Shaanxi Province has the characteristics of an eastern suburban integration area, a traditional agricultural area in central China, and an ecological reserve in western China, and its findings have universal value for rural revitalization in central and western China, especially providing policy references for the Loess Plateau area, the Qinba mountainous area, and other similar geographic areas. The case location is shown in Figure 2.

3.3. Measurement of Variables

The questionnaire has two parts: the basic demographic characteristics of farmers and the scale of factors affecting farmers’ intentions to revitalize. The scale items were measured on a 5-point Likert scale, with 1 to 5 ranging from “completely disagree” to “completely agree”.

3.3.1. Outcome Variables

The scale of farmers’ intentions to revitalize measures their intention to carry out the behavior and how much effort they are willing to make to achieve it. The measurement of the RIs refers to previous research and combines with expert discussion to form the final measurement items (see

Table 1. Variable measurement items.

Variables	Measurement Items	Variables	Measurement Items
Conditional variable
WCD	WCD makes land quality better	RCD	RCD has made me more literate
	WCD makes residential land more functional		RCD makes employment status more stable
	WCD improves infrastructure		RCD allows me to enjoy more social security
	WCD increases advanced machinery		RCD expands information channels
	WCD reduces household debt		RCD increases adaptability to daily life
	WCD has increased household savings		RCD makes me more mentally resilient
	WCD reduces the difficulty of loans
BA	Improving the economic situation	SN	Government encourages
	Increases employment opportunities		Village collectives encourage
	Improvement measures can be implemented		Family and friends support
	The revitalization plan can be implemented		Neighbors support
PBC	Overcoming the difficulties of revitalization	HD	Need to reside
	Taking the risk of revitalization		Need for pension security
	Access to relevant resources		Need to store farm equipment and sundries
	Familiar with revitalization mode		Need to develop courtyard economy
Outcome variable
RI	I am willing to learn ways to revitalize		I am willing to revitalize idle homesteads
	I am willing to recommend others

).

3.3.2. Conditional Variables

(1) Wealth Capital Differentiation

Regarding measuring WCD, a previous study took the framework of sustainable livelihood analysis as a theoretical perspective and approximate wealth capital as a sub-intermingling of physical and financial capital to design a corresponding scale [22]. In addition, natural capital is the production vehicle on which farmers rely for their livelihoods and is an important influence on livelihood decisions, affecting farmers’ potential income and food consumption [36]. Natural capital is not only a source of livelihood for farmers but also an intergenerational asset that provides them with economic and social status. Therefore, natural capital must be considered a natural resource that households utilize to accomplish their livelihood goals [36]. In summary, considering the results of existing studies, the WCD measurement scale was further designed in three categories: natural capital, physical capital, and financial capital, as shown in Table 1.

(2) Reputational Capital Differentiation

Regarding measuring RCD, a previous study took the framework of sustainable livelihood analysis as the theoretical perspective and approximate reputational capital as the intermingling of human capital and social capital to design the corresponding scale [22]. In addition, urbanization has brought a lot of new things and prompted many rural laborers to move to the cities. The traditional family pension mode of “raising children for the sake of old age” has been impacted, and the psychological condition of most farmers has fluctuated to some extent due to the influence of various factors [37]. Therefore, psychological capital, as a psychological quality manifested by the fluctuation of the living environment, should be fully considered. To summarize, combining the results of existing studies, we further design the RCD scale from the three categories of human capital, social capital, and psychological capital, as shown in Table 1.

(3) Behavioral Attitude

BA focus on how much farmers have liked the revitalization of idle homesteads since the implementation of homestead revitalization. Currently, there are only scales for measuring behavioral attitude of farmers regarding other productive life behaviors. For example, Lan et al. developed a four-item scale on outcome evaluation and outcome beliefs when studying farmers’ intentions to renew the contract for transferring land to family farms [30]. However, most of the above studies focus on other production and life situations of farmers, and fewer related studies focus on revitalizing idle homesteads. Therefore, this study is based on the idle homestead revitalization context, drawing on the mature scales of existing studies and inviting experts in related fields to make appropriate modifications to adapt to the research context. The scale items are shown in Table 1.

(4) Subjective Normnorm

The SN measurement scale measures the influence of social groups felt by farmers when revitalizing idle homesteads. Mature scales for measuring subjective norm exists in the existing literature, mostly from farmers’ formal social support (government, village collectives) and informal social support (family, friends, and neighbors) [31]. Therefore, this study combines the context of revitalizing idle homesteads and appropriately modifies the existing maturity scale with specific measurement questions, as shown in Table 1.

(5) Perceived Behavioral Control

The PBC Scale measures farmers’ perception and resource accessibility assessment of implementation barriers in the revitalization of homestead land. E et al. developed the Perceived Behavioral Control Scale based on the research background of the transfer of development rights of homesteads. The scale measures individual perceived behavioral control through control beliefs and perceptual intensity and has been cited and validated by scholars [32]. Therefore, this study adapted the scale to the specific context of idle homestead revitalization, and the specific measurement questions are shown in Table 1.

(6) Homestead Dependence

The HD scale is used to measure the degree of dependence on the security function and property function of the homestead [3]. The measurement of homestead dependence mainly refers to the study by He et al., which measured farmers’ homestead dependence from four dimensions: psychological–emotional dependence (PED), residential security dependence (RSD), property dependence (PD), and auxiliary production dependence (APD) [33]. Therefore, this study combines the specific context of revitalizing idle homesteads with appropriate modifications to the existing scale, and the specific measurement questions are shown in Table 1.

3.4. Data Collection and Processing

3.4.1. Data Sources

The data for this study come from a special survey of farmers implemented between July and September 2023 in Shaanxi Province. As a direct stakeholder in the reform of homestead tenure, the survey focuses on homestead-holding households that are experiencing or are in close contact with the phenomenon of farmer differentiation. The study uses a multi-stage mixed sampling frame to ensure sample representativeness: firstly, 10 prefecture-level cities are sampled in three geographic subregions, namely, Guanzhong Plain Urban Agglomeration, the Qinba Mountainous Area of Southern Shaanxi, and the Loess Plateau Area of Northern Shaanxi, based on the gradient of regional development; secondly, one county (district) is sampled in each municipal area using random sampling, and two towns are sampled in each county using systematic random sampling; and finally, two administrative villages are sampled in each town using systematic sampling, and two villages are sampled in each town. Two administrative villages were sampled, and 25 household questionnaires were completed in each sample village using snowball sampling (the distribution of sampling points is shown in Figure 2). A total of 1000 pieces of raw data were obtained, and 119 invalid questionnaires were excluded after outlier detection, logical checking, and completeness checking by SPSS 26.0, finally constructing a dataset containing 881 valid observations (valid recovery rate of 88.1%). The samples covering different types of farmer groups meet the heterogeneity analysis requirements needed for this study. The demographic characteristics are shown in

Table 2. Descriptive analysis of demographic information.

Variable	Item	Frequency/ Percentage (%)	Variable	Item	Frequency/ Percentage (%)
Sex	Male	475/53.9	Distance of the homestead from the county	≤5	122/13.8
	Female	406/46.1		(5,10]	293/33.3
Head of household	No	494/56.1		(10,20]	320/36.3
	Yes	387/43.9		>20	146/16.6
Age	(18,30]	119/13.5	Degree of idle homesteads	Unused	297/33.7
	(30,40]	111/12.6		Seasonal	250/28.4
	(40,50]	495/56.2		annual	334/37.9
	(50,65]	156/17.7	Do you own multiple homesteads?	No	651/73.9
Household size	(1,3]	268/30.4		Yes	230/26.1
	(4,6]	449/51.0	Cognition of ownership of homesteads	Nations	167/19
	≥7	164/18.6		Team	276/31.3
Intending to purchase urban housing	No	401/46		Own	381/43.2
	Yes	480/54		Don’t know	57/6.5

.

3.4.2. Data Verification

(1) Common method bias test

This study used Harman’s single-factor method to test for common method bias. Non-rotated factor analysis extracted seven factors, with a cumulative variance explanation rate of 84.136 (See

Table A1. Total Variance Explained.

Ingredient	Initial Eigenvalue			Extract the Sum of the Squares of the Loads
	Total	Percentage of Variance	Cumulatively	Total	Percentage of Variance	Cumulatively
1	7.538	23.557	23.557	7.538	23.557	23.557
2	4.322	13.505	37.062	4.322	13.505	37.062
3	4.064	12.701	49.763	4.064	12.701	49.763
4	3.517	10.992	60.755	3.517	10.992	60.755
5	3.328	10.399	71.154	3.328	10.399	71.154
6	2.616	8.175	79.329	2.616	8.175	79.329
7	1.538	4.808	84.136	1.538	4.808	84.136
8	0.498	1.556	85.693
9	0.314	0.982	86.674
10	0.293	0.916	87.590
11	0.278	0.869	88.459
12	0.270	0.845	89.304
13	0.252	0.788	90.091
14	0.249	0.779	90.870
15	0.236	0.737	91.607
16	0.233	0.729	92.336
17	0.228	0.712	93.048
18	0.214	0.670	93.717
19	0.205	0.642	94.359
20	0.196	0.614	94.973
21	0.190	0.594	95.567
22	0.175	0.547	96.114
23	0.165	0.517	96.631
24	0.153	0.477	97.108
25	0.150	0.468	97.576
26	0.145	0.454	98.030
27	0.135	0.423	98.453
28	0.124	0.388	98.841
29	0.106	0.331	99.172
30	0.097	0.304	99.476
31	0.093	0.289	99.765

) and a first factor variance explanation rate of 23.557% (<50% threshold), indicating that there is no significant common method bias issue. (See Abbreviations).

(2) Reliability and validity test

The reliability and validity test of the scale (

Table 3. Reliability test results for the sample.

Latent Variable	Observational Variables	Loading Factor	Cronbach’s α Factor	AVE	CR	Mean
WCD	NC1	0.876	0.960	0.776	0.960	2.799
	NC2	0.876
	PC1	0.876
	PC2	0.886
	FC1	0.876
	FC2	0.889
	FC3	0.887
RCD	HC1	0.869	0.932	0.694	0.931	2.925
	HC2	0.855
	MC1	0.851
	MC2	0.875
	SC1	0.775
	SC2	0.766
BA	BA1	0.931	0.967	0.880	0.967	2.536
	BA2	0.928
	BA3	0.942
	BA4	0.951
SN	SN1	0.949	0.970	0.889	0.970	2.574
	SN2	0.910
	SN3	0.964
	SN4	0.947
PBC	PBC1	0.917	0.958	0.850	0.958	3.011
	PBC2	0.934
	PBC3	0.914
	PBC4	0.923
HD	HD1	0.906	0.944	0.809	0.944	3.220
	HD2	0.904
	HD3	0.910
	HD4	0.877
RI	RI1	0.842	0.875	0.701	0.875	2.798
	RI2	0.806
	RI3	0.862

) showed that Cronbach’s alpha for all variables was >0.8, and factor loadings were >0.7; AVE > 0.5, and CR > 0.7, confirming good convergent validity. The discriminant validity test shows that the AVE square root is greater than the correlation coefficient and the HTMT values are all <0.85, confirming the good validity of the model.

3.4.3. Scale Integration and Data Calibration

The fsQCA uses Boolean algebra to identify necessary and sufficient conditions for response outcomes to which the data set must belong. The sample data in this study were obtained using a Likert 5-level scale, and the raw data could not fully meet the fsQCA’s requirements for the data; therefore, with reference to existing studies [38], this study used the mean aggregation method, and the measures of the six variables of intentions to revitalize, WCD, RCD, BAs, SNs, PBC, and HD, were integrated into the corresponding variable scores (see Table 3).

Using fsQCA4.1 for the next analysis. Data calibration is accomplished by selecting thresholds to score the raw data and determining the three anchors of complete affiliation, complete non-affiliation, and maximum fuzzy point to convert all the raw data into fuzzy set affiliation scores. This can usually be accomplished by using either the direct calibration method or the indirect calibration method to calibrate the data. Direct calibration involves the researcher selecting three qualitative anchor points and using the anchor points to define each case’s affiliation level in the fuzzy set. Indirect calibration involves the researcher readjusting the measurements based on the qualitative assessment. The exact calibration method depends on the respondent’s and the researcher’s substantive understanding of the data and grounded theory. Since the data collection in this study was conducted using the subjective survey method, the direct calibration method was used to calibrate the data, and based on the existing research experience, the choice of 5, 3, and 1 were set as the full affiliation, crossover, and full non-unaffiliated points, respectively [39]. It is worth noting that during calibration, some of the cases with exactly 0.5 affiliations will be excluded, which will affect the analysis results, so this study draws on existing studies and manually calibrates the value of 0.5 of them to 0.499 to avoid this problem.

4. Results

4.1. Necessity Analysis of Single Conditions

The first test to identify the necessity of a particular condition for an outcome to occur is whether a single condition variable constitutes a necessary condition for forming a farmer’s intention to revitalize [40]. The necessity relationship is measured by consistency and coverage, requiring consistency to be greater than 0.9 to determine the necessary condition [40]. However, it is worth noting that coverage is only meaningful for variables that pass the consistency test.

As shown in

Table 4. Results of the unconditional necessity analysis.

Conditional Variable	High Intention to Revitalize		Low Intention to Revitalize
	Consistency	Coverage	Consistency	Coverage
WCD	0.760	0.694	0.520	0.656
~WCD	0.624	0.485	0.758	0.813
RCD	0.768	0.654	0.579	0.682
~RCD	0.626	0.518	0.706	0.808
BA	0.611	0.680	0.431	0.663
~BA	0.697	0.470	0.792	0.738
SN	0.624	0.690	0.435	0.665
~SN	0.697	0.471	0.797	0.745
PBC	0.722	0.606	0.563	0.653
~PBC	0.587	0.493	0.660	0.766
HD	0.683	0.504	0.739	0.754
~HD	0.667	0.649	0.514	0.691

Note: ‘~’ denotes ‘not’ for logical operations, as below.

, none of the six conditions of WCD, RCD, BA, SN, PBC, and HD individually constitutes a necessary condition for the farmer’s intention to revitalize at a high/low level (consistency is <0.9 for all). This result confirms the complexity of farmers’ decision-making and the need to drive intention formation through the synergistic effect of multiple conditions.

4.2. Sufficiency Analysis of Conditional Configurations

As the core part of fsQCA, the sufficiency analysis of conditional configuration mainly identifies the multiple configurations that realize the outcome variable [39]. This part is mainly based on the truth table of realizing high/low farmers’ intentions to revitalize, followed by analyzing the sufficiency results of conditional configurations concerning the types of solutions of different levels of complexity. A conditional configuration is considered a sufficient configuration leading to a result when the consistency between the conditional configuration and the result is greater than or equal to 0.75 [39]. fsQCA was used to construct the truth table for conditional configuration analysis on the calibrated data. After considering the actual situation of the data in this study and the existing studies, the frequency number of cases was set to 5, the consistency threshold was set to 0.8, the PRI was set to 0.6, and all the cases were retained in this study. Consistency is the core indicator in the QCA method for measuring the strength of the association between conditional combinations and outcome variables. Setting its threshold to 0.8 can effectively filter out low correlation paths, ensuring that the retained configurations have sufficient explanatory power [40], while also being compatible with the complexity of social science research. It allows for 20% of exceptional cases (such as individual differences among farmers or external interference) to avoid oversimplifying causal mechanisms. This is in line with the mainstream consensus and recommended operational guidelines in the QCA field. This is achieved by distinguishing between “core” and “peripheral” conditions by comparing intermediate and simple solutions. Core conditions are those for which “there is evidence of a strong causal link to the outcome of interest”. In contrast, marginal conditions are those for which there is weak evidence of a causal relationship with the outcome. Core conditions are common to intermediate and parsimonious solutions, while fringe conditions are eliminated in simple solutions and appear only in intermediate solutions.

Further, using the representation proposed by Ragin [41], the simple and intermediate solutions in the standardized analysis results were combined to obtain the conditional configuration types of the antecedent variables, and the conditions that appeared simultaneously in the intermediate and parsimonious solutions were defined as “core conditions”. The final conditional configuration results are presented in the form of a large black solid circle ⚫ indicating the existence of the core condition, a small black solid circle ● indicating the existence of the auxiliary condition, a large circle with a ⊗ indicating the non-existence of the core condition, a small circle with a ⮾ indicating the non-existence of the auxiliary condition, and a “space” indicating the condition may or may not exist.

4.2.1. Sufficient Conditions for High Revitalization Intention Conditional Configuration Analysis

As can be seen from

Table 5. Configuration analysis of antecedent conditions for high farmers’ intentions to revitalize.

Dimension	Conditional Variable	S1		S2	S3	S4
		S1a	S1b			S4a	S4b
FD	WCD	⚫	⚫	●	⊗	⚫	⚫
	RCD		●	●	⚫	⮾	●
FC	BA	⚫	⚫	⊗	⚫	⮾
	SN	⮾		⚫	⮾	⚫	⚫
	PBC				●	⊗	⊗
	HD	⚫	⚫	⊗	⊗	●	⮾
Original coverage		0.317	0.345	0.315	0.264	0.247	0.295
Unique coverage		0.037	0.045	0.060	0.045	0.017	0.036
Consistency		0.925	0.936	0.958	0.991	0.970	0.959
Coverage of solutions		0.594
Consistency of solutions		0.896

Note: ⚫ indicates that the core condition is present, ● indicates that the edge condition is present; ⊗ indicates that the core condition is missing; ⮾ indicates that the edge condition is missing; a space indicates that the presence or absence of the condition variable is irrelevant.

, a total of six configurations are obtained in this study, and the consistency of the overall solution reaches 0.896 (>0.8), and the coverage of the overall solution is 0.594 (>0.5), indicating that about 59.4% of the intentions to revitalize the high-farming households can be explained by these six configurations. The consistency of the six configurations meets the threshold value of greater than 0.8, and all of them can be regarded as contributing to the intention of the farmer to revitalize. Sufficient conditions are equivalent.

According to the above table, the last step of Furnari’s theory for conditional configuration is followed to name the above patterns [41] due to the neutral arrangement situation of S1a and S1b, S4a, and S4b, which means that the two paths have the same core conditions although the edge conditions are not the same. Therefore, in this study, the antecedent conditional configurations with the same core conditions were categorized into the same conformation, with a total of four types of conformations. See Figure 3 for the naming of the conformations.

(1) The wealth capital differentiation–dual cognitive-driven type

The wealth capital differentiation–dual cognitive-driven type shows that high farmer intentions to revitalize can be achieved when high WCD, BAs, and HD are the core conditions. This model shows that high intentions to revitalize can be achieved when farmers have high-wealth capital and a positive attitude towards revitalizing the homestead, even if there is a high dependence on the homestead. The differentiation of wealth and capital has prompted farmers to shift towards high-yield non-agricultural industries. Although reducing livelihood risks, it is still necessary to generate income to support the process of urbanization. This conformation also further validates the findings of an existing study that states that farmer WCD is positively associated with RIs [3]. At the same time, the conformation also highlights the significant influence of farmers’ behavioral attitude on the intentions to revitalize [42]. According to cognitive psychology theory, an individual’s beliefs determine his/her preferences, which further determine his/her decision-making and behavior. In the situation of revitalizing idle homesteads, due to the limitations of their own knowledge structure and insufficient information, farmers are unable to form a precise understanding and accurate evaluation of a new mode of production and life in a short period of time. The possibility of whether farmers form the intention to revitalize or not gradually increased with the adjustment of the cognitive level. In other words, the formation of farmers’ intentions to revitalize is the result of their rational choice based on cognitive measurement; therefore, farmers’ BAs have an important impact on the RI.

This conformation corresponds to S1a and S1b, with a consistency of 0.925 and 0.936, respectively. S1a shows that the above mechanisms can work even without subjective norm from formal and informal social networks. Group S1b shows that the central role of the above mechanism needs to be complemented by the farmers’ high reputational capital.

(2) The single cognitive-driven type

The single cognitive-driven type shows that when high SNs, low BAs, and low HD are the core conditions, and high WCD and high RCD are the peripheral conditions, high farmers’ intentions to revitalize can be achieved. When farmers exhibit limited behavioral attitude toward revitalizing idle homesteads and show no dependence on homestead functionality, the government can enhance their revitalization intentions through two key interventions. First, it amplifies subjective norm. Second, it facilitates the accumulation of both wealth capital and reputational capital.

Further to the analysis of the core conditions, the conformation reveals that the presence of SNs of the core conditions drives farmers’ high intentions to revitalize. This further confirms the theory of persuasion, which suggests that the recommendations and arguments of significant others in the individual’s community will directly or indirectly affect the formation of the individual’s intention to act [43]. Especially in uncertainty, the individual will tend to obtain information about what should be conducted from the outside community, which will lead to the individual’s behavior to obey the norms of the group. In recent years, the government has strengthened the support of relatives and friends through policy incentives, risk management, and rights protection measures, effectively enhancing the guiding effect of social norms, which also further validates the conclusion that SNs are positively associated with RIs.

(3) The reputation capital differentiation–single cognitive-driven type

The reputational capital differentiation–single perception-driven type shows that high farmer intentions to revitalize can be achieved when high RCD, high BAs, and low WCD with low HD are the core conditions, and high PBC with low SNs are the marginal conditions. This conformation indicates that the synergistic linkage of reputational capital, behavioral attitude, and perceived behavioral control mainly drives farmers’ high intentions to revitalize. The simultaneous existence of reputational capital differentiation, behavioral attitude, and perceived behavioral control in the configuration perspective reflects the potential interaction of the three.

Further analysis of the core conditions and conformation reveal that the core conditions of reputational capital differentiation and behavioral attitude exist to drive farmers’ high intentions to revitalize. RCD gives farmers a stronger development endowment and marketability, and they need to revitalize idle homesteads as their inevitable choice to allocate land resources and obtain property income to invest in improving the family’s socio-economic status. Farmers’ BA tends to produce positive results for idle homesteads. The government revitalizes typical cases and benchmarks farmers through publicity, shapes positive cognition through media guidance, and cultivates farmers’ BAs. This study has used structural equation modeling to confirm that WCD drives farmers’ RIs [3]. This conformation shows that the driving mechanism of high intentions to revitalize does not depend on a single condition; even if farmers’ wealth capital accumulation is relatively low, they can also achieve high intentions to revitalize through the cooperation of other conditions.

(4) The wealth capital differentiation–single cognitive-driven type

The wealth capital differentiation–single perception-driven type shows that high farmer intentions to revitalize can be achieved when high WCD, high SNs, and low PBC are the core conditions. This model shows that high intentions to revitalize can be achieved when the farmers have high wealth capital and positive attitudes towards the revitalization of the homestead, even if there is a high dependence on the homestead.

From the combination of core conditions, this conformation reflects that as long as the farmers themselves have high wealth capital and feel strong subjective norm, even if they lack perceived behavioral control of the revitalization of the homestead, they can also achieve high revitalization intentions of farmers. When farmers perceive poor behavioral control, as long as they can feel strong subjective norm based on some of the deeper wealth capital differentiation of farmers, they can achieve high revitalization intentions. This is mainly due to WCD, which allows some farmers to enter the city at a higher marginal rate of return than non-agricultural employment. The burden of living and income uncertainty for this group of farmers is lower. However, continuous capital inputs, such as gains from revitalizing homesteads, are needed because of the enormous social costs involved in citizenship.

The corresponding conformations are S4a and S4b, and the consistency of the two groups is 0.970 and 0.959, respectively. Group S4a shows that even if farmers are dependent on homesteads and lack reputational capital differentiation and positive behavioral attitude, the above mechanism of action is still able to function. Group S4b shows that when farmers are not dependent on the homestead, as long as it is supplemented by reputation capital differentiation, the above mechanism will still work.

4.2.2. Sufficient Conditions for Non-High Revitalization Intention Conditional Configuration Analysis

In fsQCA, additional analysis of the negation of the outcome variable (farmers’ low intentions to revitalize) can be a good complementary practice to check which combinations of conditions can lead to the negation of the outcome. This study further examined which combinations of conditions would lead to low farmers’ intentions to revitalize, and the results are shown in

Table 6. Configuration analysis of antecedent conditions for low farmers’ intentions to revitalize.

Dimension	Conditional Variable	NS1		NS2		NS3	NS4
		NS1a	NS1b	NS2a	NS2b
Farmer division	WCD	⊗	⊗	⊗	⊗
	RCD			⊗	⊗	⚫	⊗
Farmers’ perceptions	BA	⊗	⊗		⮾	⊗	⊗
	SN	⮾		⊗	⊗	⊗	⊗
	PBC	⊗	⊗	⮾			⊗
	HD		●			⚫
Original coverage		0.367	0.388	0.343	0.381	0.351	0.307
Unique coverage		0.0136	0.091	0.081	0.097	0.054	0.039
Consistency		0.885	0.895	0.958	0.945	0.881	0.934
Coverage of solutions		0.755
Consistency of solutions		0.884

Note: ⚫ indicates that the core condition is present, ● indicates that the edge condition is present; ⊗ indicates that the core condition is missing; ⮾ indicates that the edge condition is missing; a space indicates that the presence or absence of the condition variable is irrelevant.

. A total of six groupings were obtained in this study, with an overall solution consistency of 0.884 (>0.8) and an overall solution coverage of 0.755 (>0.5), suggesting that about 77.84% of the low farmers’ intentions to revitalize can be explained by these six groupings. The consistency of the six groupings meets the threshold value of greater than 0.8, and all of them are considered to be sufficient conditions contributing to the low intention of farmers to revitalize and have equivalence.

Since NS1a and NS1b, NS2a, and NS2b appeared in a neutral arrangement, the two conditional configurations had the same core conditions, although the edge conditions were not the same. Therefore, in this study, the antecedent conditional configurations with the same core conditions were categorized into the same configuration, with four types of configurations. See Figure 4 for pattern naming.

(1) The wealth capital differentiation–double cognitive-lacking type

The wealth capital differentiation–double cognitive-lacking type shows that when high WCD, positive BAs, and strong PBC are missing as the core conditions, it will lead to the formation of low intentions to revitalize. This model reveals the formation mechanism of farmers’ low intentions to revitalize, which stems from the cooperating effect of two conditions: a. insufficient wealth capital differentiation, and b. lack of positive behavioral attitude and perceived behavioral control. The combined effect of the two significantly inhibits the strength of farmers’ intentions to revitalize.

(2) The farmer differentiation–single cognitive-lacking type

The farmer differentiation–single cognitive-lacking type shows that when FD and lack of SNs are central conditions, it will lead to the development of low intentions to revitalize. This conformation suggests that the formation of low intentions to revitalize among farm households is subject to the simultaneous fulfillment of the following conditions: a. low farmer differentiation, and b. the absence of multidimensional drivers includes a lack of positive behavioral attitude, uninfluenced by subjective norm of significant others, and insufficient perceived behavioral control. These two factors make it difficult for farmers to form high intentions to revitalize their land.

(3) The double cognitive lacking-type

The double cognitive-lacking type shows that when BAs and SNs are lacking, RCD and HD are the core conditions that will lead to low intentions to revitalize the farmer. This study reveals that when the triple constraints are simultaneously met, there are a. negative behavioral attitude of farmers towards revitalizing homesteads; b. weak perceptions of relevant subjective norm; and c. the presence of strong homestead dependence characteristics. Even under the driving effect of deep differentiation of reputational capital, the inhibition of low intentions to revitalize is still formed. This conformation highlights the importance of farmers’ behavioral attitude and lack of subjective norm on the formation of low intentions to revitalize.

(4) The reputational capital differentiation–multiple cognitive-lacking type

The reputational capital differentiation–multiple cognitive-lacking type shows that when the lack of RCD, BAs, SNs, and PBC are the core conditions, it will lead to low farmers’ intentions to revitalize. This study suggests that the combination of a. insufficient reputational capital differentiation and b. multidimensional cognitive deficits (negative attitudes, weak normative pressures, and low control) will significantly inhibit farmers’ intentions to revitalize and create a low-intention state.

4.3. Robustness Test

The methodological rationale for threshold adjustment in robustness testing centers on verifying the reliability of the results through parametric sensitivity analysis [41]. Adjusting the consistency threshold aims to exclude weakly correlated paths and reduce the risk of false positives while increasing the case frequency threshold, enhancing the configuration paths’ generalizability and avoiding slight sample bias [44]. Such manipulations follow the QCA domain specification and ensure that conclusions are insensitive to threshold selection by testing the stability of the core conditions under different parameters.

Two methods were used to test the robustness by increasing the consistency and case frequency thresholds. First, the consistency threshold was raised from 0.8 to 0.85, the coverage and consistency of high and low revitalization intentions did not change significantly, and the intermediate solution was consistent with the original analysis; second, the case frequency threshold was raised from 5 to 7, the coverage of high revitalization intentions was reduced from 0.594 to 0.569, the consistency was raised from 0.896 to 0.905, the low revitalization coverage decreased from 0.755 to 0.744, the consistency increased from 0.884 to 0.884, and the generated conformation paths are either the same as the original conformation paths or a subset of the original conformation. In summary, the results are robust.

5. Discussion

About the theory. First, the structural differences of farmer differentiation (wealth capital differentiation and reputation capital) are revealed by using the social stratification theory; unlike the existing studies that mostly focus on wealth differentiation [45], this study also includes reputation capital differentiation in the analysis, which helps us to gain a deeper understanding of the concept of farmer differentiation. Reputational capital is the condensation of individual cultural level, experience, and social relationship resources, which directly affects the formation of farmers’ intentions to revitalize. This also confirms Fei’s emphasis on reputation capital driving farmers’ behavior through the “face-saving mechanism [46]”. This finding expands the boundaries of applying social stratification theory in rural land research, echoing the conclusion of existing studies that “social capital compensates for economic shortcomings [47]” but challenging the “dominance of economic rationality [48]”. Secondly, the local theory is used to improve the theory of planned behavior. Incorporating homestead dependence into the theory of planned behavior verifies the inhibitory effect of place attachment on the intentions to revitalize homesteads, consistent with existing research suggesting that land sentiments impede urbanization [49]. At the same time, it enhances the explanatory power of the theory of planned behavior, and it fills the gap of emotional elements in the research of Lu et al. [3]. Thirdly, following the SOR framework, we connect the external stimulus (differentiation) and internal psychology (cognition) and construct a cooperative model of “farmer differentiation–farmer cognition”, which breaks through the limitation of the single perspective in traditional research and expands the application scope of the above theories [32]. This interdisciplinary integration enriches the theoretical tools for studying farmer behavior and provides a new analytical paradigm for understanding complex social phenomena.

About the Method. This study reveals the multiple concurrent paths and causal asymmetry in forming farmers’ intentions to revitalize through the fsQCA method, which compensates for the limitations of traditional methods. Previous studies have focused on the marginal effects of independent variables, such as farmer differentiation and farmer cognition, based on a single perspective, such as neoclassical economics or behavioral economics. However, this study finds that forming farmers’ high intentions to revitalize depends on the cooperating “farmer differentiation-cognition factors”. For example, although wealth capital differentiation is a prevalent core driver [22], this study shows that it needs to be linked to behavioral attitude or subjective norm (e.g., group S1a/S1b) to be effective. This finding challenges the “single-condition dominance theory [48]”. Meanwhile, the reputation capital differentiation–single cognitive-driven type (S3) indicates that reputation capital and cognitive elements can form alternative paths. This echoes Bourdieu’s emphasis on the hidden role of cultural and social resources in social stratification [50]. In addition, while wealth capital differentiation was prevalent in the high-intention group (59.4% coverage), its absence was not a necessary condition in the low-intention group (only NS1a/NS1b). This validates the advantage of fsQCA in analyzing “equivalent paths” and “causal diversity” [41] and responds to Rihoux et al.’s assertion that “QCA compensates for the limitations of regression analysis [51]”. This compensates for the limitations of traditional methods in interpreting nonlinear relationships. This study provides methodological insights for subsequent research on the need to capture multifactor interactions from a configurational perspective rather than relying on marginal effects analysis in linear regression.

About the practice. This study has achieved a transition from a standardized intervention to a precise intervention. While existing policy designs emphasize “standardized incentives [52]”, this study finds that differences in the level of farmer differentiation need to be matched with differentiated cognitive intervention strategies. For example, for farmers with deeper wealth capital differentiation, subjective norm need to be strengthened to unleash their economic rationality (wealth capital differentiation–single cognitive-driven type). This discovery once again confirms the importance of subjective norm in farmers’ decision-making [7]. Farmers with shallower wealth capital differentiation need to make up for the shortfalls in resources through the accumulation of reputational capital and the cultivation of behavioral attitude (reputational capital differentiation–single cognitive-driven type). This discovery complements Guo et al.’s initiative on “social capital leveraged land reform” [27] and Xia et al.’s proposal on the influence of social networks on land decision-making [53]. This “condition adaptation” policy design approach is highly compatible with the differentiated management plan proposed by Cao et al. for newly added construction land [54]. This provides a theoretical basis for “precision policymaking” and breaks through the inefficiency of “one-size-fits-all” policies [55].

6. Conclusions and Implications

6.1. Conclusions

This study integrates SOR theory, social stratification theory, and improves the TPB to construct a configuration effect model of farmer differentiation and farmer cognition, synergistically enhancing farmers’ intentions to revitalize idle homesteads. Taking 881 farmers in Shaanxi Province as a case study, the fsQCA method was used to systematically analyze the antecedents and configuration paths of farmers’ intentions to revitalize.

(1) The formation of farmers’ intentions to revitalize is a topic that involves multiple complex causal relationships. None of the six antecedent conditions, or their negations, is a necessary condition that leads to high/low farmers’ intentions to revitalize. The six antecedent conditions fall into two categories: farmer differentiation (wealth capital differentiation, reputation capital differentiation) and farmers’ cognition (behavioral attitude, subjective norm, perceived behavioral control, and homestead dependence). Farmers’ intentions to revitalize are shaped by combining several influencing factors in the two levels of “farmer differentiation–farmer perception”.

(2) The six configurations that lead to the formation of farmer differentiation intentions to revitalize are summarized into four conformations: a. Wealth capital differentiation–double cognitive-driven type (even if there is a homestead dependence, high wealth capital differentiation farmers form the intentions to revitalize by reinforcing their behavioral attitude). b. Single cognitive-driven type (when farmers do not depend on homesteads and lack positive behavioral attitude, subjective norm can be a substitute for driving revitalization intention). c. Reputational capital differentiation–single cognitive-driven type (reputation capital differentiation cooperates with perceived behavioral control to make up for the lack of wealth capital to drive the revitalization intention). d. Wealth capital differentiation–single cognitive-driven type (even if the perceived behavioral control of the high-wealth capital farmers is weak, they can still drive the intention through subjective norm). This suggests that there are “equivalent paths”.

(3) The six configurations that led to the formation of farmers’ intentions to non-revitalize were summarized and categorized into four conformations, namely, wealth capital differentiation–double cognitive-lacking type, farmer differentiation–single cognitive-lacking type, double cognitive-lacking type, and reputational capital differentiation–multiple cognitive-lacking type. This suggests that the combinatorial absence of key conditions significantly inhibits farmers’ intentions to revitalize.

(4) Reputation capital differentiation, behavioral attitude, subjective norm, perceived behavioral control, and homestead dependence cannot be ignored. But wealth capital differentiation is a universal key condition that contributes to the formation of farmers’ high intentions to revitalize. From the cross-sectional comparison of the various configurations that cause high intentions to revitalize, wealth capital differentiation exists in all four conformations.

This study provides theoretical and practical insights into sustainable land governance in rapidly urbanizing regions worldwide. On the one hand, the multiple concurrent pathways and causal asymmetries in forming farmers’ intentions to revitalize, as revealed in this study, deepen the understanding of the complexity of social–ecological systems. It also echoes the call for optimizing land resource allocation in SDG 11 (Sustainable Cities and Communities) of the United Nations Sustainable Development Goals (SDGs). On the one hand, the differentiated policy paths proposed in this study provide a replicable solution for developing countries to solve the paradox of “population loss–land expansion”.

6.2. Implications

Revitalizing idle homesteads is a matter of resource allocation and an entry point for the sustainable transformation of social and ecological systems. Policy design must establish an intention-raising mechanism from a holistic perspective, abandoning the “one-size-fits-all” mentality. Policymakers must focus on the reasonable combination and configuration between farmer differentiation and farmers’ cognition without unthinkingly copying or paying too much attention to farmers’ shortcomings. The lack of specific conditions does not mean farmers’ intentions to revitalize cannot be realized. The strength of particular conditions does not guarantee that farmers’ intentions to restore will be formed. This study proposes the following precise intervention strategies based on the differentiated characteristics of the grouping path.

(1) For farmers with deeper wealth capital differentiation, policymakers need to strengthen farmers’ behavioral attitude through publicity, guidance, and institutional safeguards, and provide public services equivalent to the function of homestead security to alleviate the dependence on homesteads. On the other hand, they need to set up a mechanism for monitoring and participation, and group incentives to strengthen subjective norm, thus enhancing farmers’ intentions to revitalize.

(2) For farmers with shallow wealth capital differentiation, policymakers need to promote the accumulation of reputational capital of farmers by enhancing their non-farm employment ability and social network resources. On the one hand, use short videos and other new media to disseminate information on revitalization benefits to shape the positive behavioral attitude of farmers and to enhance the intentions of farmers to revitalize.

(3) For farmers’ differentiation of wealth capital and reputation capital, policymakers can strengthen subjective norm through administrative assessment pressure transfer, community honor incentives, and regular supervision of village rules and regulations to drive the formation of farmers’ intentions to revitalize.

6.3. Shortcomings and Prospects

The formation of farmers’ intentions to revitalize is a complex, systematic project, and the influencing factors identified in this study are not comprehensive enough due to the limitations of the level of case detail and the number of samples. In the future, more conditional configuration should be further incorporated into more conditional elements in order to enhance the coverage of the case grouping and the validity of the analysis.