Next Article in Journal
Hydrochemical Characteristics and Drinking Water Quality Assessment of Phreatic Groundwater in the Northwest of the Sichuan Basin, SW China
Next Article in Special Issue
Comprehensive Assessment of Water Resource Carrying Capacity Based on Improved Matter–Element Extension Modeling
Previous Article in Journal
Upgrading Existing Water Distribution Networks Using Cluster-Based Optimization Techniques
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Water
Volume 17
Issue 7
10.3390/w17071073
water-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

A Study on the Influencing Factors and Multiple Driving Paths of Social Integration of Reservoir Resettlers: An Empirical Analysis Based on SEM and fsQCA

by
Lili Diao
1,
Jiachuan Chen
2,
Jihao Chen
3 and
Zhaoxian Su
4,*
1
School of Public Administration, Hohai University, Nanjing 211100, China
2
Development Research Center of the Ministry of Water Resources of P.R. China, Beijing 100038, China
3
College of Humanities and Development Studies, China Agricultural University, Beijing 100193, China
4
School of Public Administration, North China University of Water Resources and Electric Power, Zhengzhou 450046, China
*
Author to whom correspondence should be addressed.
Water 2025, 17(7), 1073; https://doi.org/10.3390/w17071073
Submission received: 14 February 2025 / Revised: 29 March 2025 / Accepted: 30 March 2025 / Published: 3 April 2025
(This article belongs to the Special Issue Water Resources, Economic Development and Environment Carrying Capacity)
Browse Figures
Versions Notes

Abstract

:
This study systematically analyzes the factors influencing the social integration of reservoir resettlers, aiming to provide a theoretical basis and policy recommendations for enhancing their social integration. Grounded in social capital theory and social cognitive theory, the influencing factors are categorized into five dimensions: social norms, social trust, social networks, self-efficacy, and outcome expectations. Structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) are employed to analyze field survey data and uncover the mechanisms through which these factors influence social integration. The results indicate that social norms, social trust, and social networks positively affect resettlers’ self-efficacy and outcome expectations, which, in turn, have a positive impact on their social integration. The fsQCA results further identify five configurations, which are consolidated into three driving types: the internal-external driving path, the proactive integration path, and the capital-enabled path. By integrating the perspectives of social capital and social cognition and employing both SEM and fsQCA methodologies, this study provides valuable insights for policy-making related to the social integration of reservoir resettlers.

1. Introduction

Large- and medium-scale water conservancy and hydropower projects have generated significant benefits in irrigation, power generation, water supply, flood control and ecological conservation. While promoting national economic and social development, these projects have also resulted in a substantial number of reservoir resettlers. To date, China has constructed nearly 100,000 water conservancy and hydropower hubs, leading to a total of 25.17 million resettlers receiving post-relocation support across 2518 counties and 155,200 administrative villages [1]. Reservoir resettlers are non-voluntary migrants who often face challenges due to the underdeveloped economic conditions of their original locations, changes in their living environments, depletion of livelihood capital, transformation of production methods, and disruption of social networks. As a result, they must adapt to shifts in production modes, changes in employment environments, unfamiliar cultural and educational customs, and disruptions in employment patterns. If these resettlers encounter difficulties in integrating into their host communities, it may not only hinder the improvement of their sustainable livelihood capacity but also constrain the high-quality economic and social development of the region. Therefore, exploring the pathways to social integration for reservoir resettlers is an urgent and pressing issue.
The social integration of reservoir resettlers encompasses multiple dimensions, which include economic integration, cultural acceptance, behavioral adaptation, identity recognition, and political integration. For decades, many policies supporting reservoir resettlers have been implemented, leading to overall improvements in their social integration. However, due to the complexity of the resettlement work and the social integration process itself, some reservoir resettlers are still experiencing some problems in the process of social integration with the local community, which merits further research and attention [2]. Existing studies offer various explanations for this phenomenon, primarily from three perspectives. Firstly, some research focuses on governance approaches to integration. Studies have identified social support [3], improving public health services [4], and the development of social organizations [5] as effective strategies for promoting social integration and sustainable development among resettlers. Secondly, some studies concentrate on measuring the level of social integration in degrees. Guo developed a social integration index, classified into psychological recognition, social acceptance, economic integration, and cultural integration [6], while Shen et al. constructed a five-dimensional indicator system covering physiological adaptation, social adaptation, identity recognition, economic integration, and psychological integration in their research [7]. Thirdly, research has examined the factors influencing social integration. Studies have found that multiple factors interact to shape the integration process [8], with key determinants including post-relocation support policies [9], social governance approaches [10], and the social security system [11]. Additionally, infrastructure, social interaction, economic and living environments, duration of residence, occupation types, monthly income, and household composition significantly impact social integration [12,13]. Although previous studies have widely explored the social integration of reservoir resettlers, focusing on topics such as social support, the enhancement of social networks, and the removal of group boundaries [14,15], few have examined the factors affecting the integration process from micro-level. There is also a lack of research employing a configurational approach to analyze multiple integration pathways, and studies have not sufficiently explored the specific pathways through which resettlers achieve social integration. Furthermore, in practice, the integration process is often hindered by insufficient social capital and limited cognitive capacity, yet these aspects have not received adequate attention.
To address these gaps, this study constructs an analytical framework for the social integration of reservoir resettlers based on social capital theory and social cognitive theory. SEM is employed to validate the model and examine the “net effect” influence pathways, while fsQCA is used to explore the complex interactions among multiple factors and the mechanisms shaping social integration. This approach identifies multiple equivalent configurational pathways leading to social integration among reservoir resettlers. The marginal contributions of this study are as follows: first, by integrating the social capital theory and the social cognitive theory, this study constructs an analytical framework for social integration, overcoming the limitations of previous research that relied on a single theoretical perspective. Second, it identifies multiple equivalent configurational pathways to social integration, providing a diversified strategic foundation for policy formulation.
The remainder of this study is structured as follows: Section 2 constructs a theoretical conceptual model of social integration for reservoir resettlers based on social capital theory and social cognitive theory. Section 3 provides a detailed description of the research sample selection, data sources, questionnaire design, and variable measurement methods. Section 4 verifies the influences of different hypotheses on the resettlers’ social integration. Section 5 discusses the relationships between variables and their effects on social integration. Finally, conclusions and policy recommendations are presented.

2. Theoretical Analysis and Research Hypotheses

2.1. Theoretical Analysis

2.1.1. Social Cognitive Theory (SCT)

According to A. Bandura, human cognitive factors and their interactions with the environment and behavior form a dialectical and well-integrated system, where behavior is influenced not only by individual cognition but also by environmental constraints. These three elements exist in a dynamic and reciprocal relationship of continuous interaction and mutual influence [16]. Specifically, individual cognition is goal-driven, as individuals form expectations and action plans based on their assessments of themselves and their environment. Through self-regulation processes, individuals actively, rather than passively, influence their environment in an effort to achieve their goals. During this process, individuals engage in continuous self-reflection, which shapes their self-efficacy and further adjusts their behavior. Moreover, individuals anticipate the outcomes of their actions, forming outcome expectations that influence their behavioral choices and persistence. Given that social cognitive theory emphasizes that behavioral outcomes are primarily determined by both the environment and cognition, it has been widely used to explain migrant social integration [17]. However, due to the relatively vague definition of the “environment” in SCT, it often fails to fully explain the realities of specific populations, particularly reservoir resettlers, whose social environment undergoes abrupt changes due to relocation. Since cognition and environmental factors alone cannot sufficiently account for their behavioral patterns, it is necessary to integrate other theoretical perspectives to further explain the complex processes of social integration among reservoir resettlers.

2.1.2. Theory of Social Capital (TSC)

TSC primarily examines the total sum of resources that social organizations can access through social relationship networks to achieve their goals. Bourdieu, from a social networks perspective, argues that social capital is an aggregate of actual or potential resources, inseparable from institutionalized relational networks. He emphasizes the ability of individuals to access resources through their social status and network positions, and points out that social capital can be converted into economic and cultural capital, collectively constituting symbolic capital [18]. Coleman, focusing on the functionality of social structures, proposes that social capital is a resource embedded in social relations, such as trust and norms. By facilitating information flow, cooperation, and collective action, it reduces transaction costs and enhances individual action efficacy. He particularly emphasizes the role of social capital in solving public problems and social reproduction [19]. Putnam, expanding TSC from a political science perspective, explicitly defines social capital as networks of civic engagement, norms of reciprocity, and social trust at the community level. He argues that it improves the efficiency of democratic governance, promotes cooperation in public affairs, and enhances social cohesion through horizontal connections [20]. Social capital is a form of capital distinct from physical and human capital, with social norms, social networks, and social trust as its core elements [21]. Therefore, it can be used to explain and predict the social integration outcomes of reservoir resettlers. Research indicates that the social environment exerts a deeper and broader influence on social integration compared to the natural environment [22]. The quality of social environment in which reservoir resettlers find themselves is essentially the amount of social capital they possess. Consequently, some studies have combined TSC and SCT to form an extended model, where social capital acts as a “complementary predictor” of the environment.
In summary, SCT and TSC offer unique and complementary contributions to ex-plaining the social integration process of reservoir resettlers. TSC focuses on structural resources within social networks (such as social norms, social trust, and social networks) and emphasizes the supportive role of the external environment in the integration of resettlers, creating objective conditions for social integration. In contrast, SCT starts from individual psychological mechanisms and focuses on intrinsic cognitive factors such as self-efficacy and outcome expectations. The complementarity between the two theories lies in the fact that TSC addresses SCT’s lack of attention to environmental resources, explaining how external social structures lay the foundation for integration through resource empowerment. Meanwhile, SCT supplements the limitations of TSC regarding individual agency, elucidating how resettlers transform external resources into proactive integration behaviors through cognitive processes, which synergistically drive the integration process. Thus, this integrated perspective encompasses both structural resources and individual agency, providing a multi-layered explanation for the social integration of reservoir resettlers.

2.2. Research Framework

SCT emphasizes an interactional perspective based on triadic mutuality and highlights that mutual relationships do not necessarily imply symmetrical bidirectional influences. In contrast, TSC focuses on the positive impact of social capital stock on individual behavioral outcomes, without directly considering internal cognition. Therefore, combining these different theoretical perspectives tends to offer a more comprehensive approach to understanding the mechanisms behind social integration in reservoir resettlement. In this context, this study attempts to construct and validate a model of factors influencing the social integration of reservoir resettlers, integrating both SCT and TSC, as is shown in Figure 1.

2.3. Research Hypotheses

2.3.1. Outcome Expectations and Social Integration

Outcome expectations involve individuals’ subjective predictions and expectations regarding the outcomes and feedback of actions they are about to take in a specific context [23]. Research indicates that outcome expectations significantly influence the individuals’ social integration process, with positive outcome expectations being proven to enhance a person’s sense of social belonging and promote harmonious interactions with the surrounding environment [24]. When individuals expect to gain recognition, friendship, and support in a new environment, they are more likely to actively integrate into new communities and participate in social activities, thus accelerating the social integration process. In the context of reservoir resettlement, resettlers often face numerous uncertainties and challenges when confronted with a new environment and culture. However, when resettlers hold positive outcome expectations about integrating into the new environment, these expectations become a powerful driving force for their adaptation to the new environment and promotion of social integration. Thus, the following hypothesis is proposed:
H1. 
Outcome expectations have a positive effect on the social integration of reservoir resettlers.

2.3.2. Self-Efficacy and Social Integration

According to SCT, self-efficacy plays a key role in promoting an individual’s social adaptation and integration process. Individuals with high self-efficacy are generally more confident in their social skills and problem-solving abilities, which leads them to participate more actively in social activities and makes it easier for them to establish and maintain interpersonal relationships, thereby fostering social integration [25]. Conversely, individuals with low self-efficacy may avoid social situations because of a lack of confidence, reducing social interactions and thus hindering social integration. Particularly in multicultural or new environments, self-efficacy has been confirmed as a crucial factor predicting successful social adaptation and integration among migrant groups [26]. Consequently, the following hypothesis is proposed:
H2. 
Self-efficacy positively affects the resettlers’ social integration.

2.3.3. Social Norms, Self-Efficacy, and Outcome Expectations

According to the SCT, social norms, as an important external influence on individual behavior, are regarded as a powerful force shaping individual cognition and behavioral responses. Social norms not only regulate social interactions but also significantly impact individuals’ perceptions of self-efficacy and the generation of outcome expectations. Relevant studies have demonstrated that behaviors that align with social norms tend to enhance an individual’s self-efficacy, which means that when individuals perceive their actions to be consistent with societal expectations, they are more likely to believe in their ability to perform such behaviors [27]. Social norms provide a behavioral reference framework, and adherence to them in a new environment can bolster individuals’ confidence and support, thereby influencing their positive outcome expectations [28]. For reservoir resettlers, adhering to social norms that facilitate their social integration enhances their self-efficacy in the new environment, helping them adapt to the new life. By positively adapting to the social norms of the resettlement area, reservoir resettlers can also form more positive and rational outcome expectations, thereby promoting social integration. Thus, the following hypotheses are proposed:
H3. 
Social norms have a positive impact on the outcome expectations of reservoir resettlers.
H4. 
Social norms have a positive impact on the self-efficacy of reservoir resettlers.

2.3.4. Social Trust, Self-Efficacy and Outcome Expectations

According to SCT, behavior is deeply influenced by an individual’s intrinsic psychological state. Social trust, as a core social psychological structure, is regarded as an important factor influencing individual behavioral decisions. Research has shown that social trust has a positive effect on individuals’ self-efficacy [29]. Social trust not only provides individuals with a sense of psychological security but also enhances their confidence in successfully executing specific tasks. Although it may be affected by specific situational factors, in most cases, an individual’s level of social trust demonstrates consistency across different contexts. A high-trust society minimizes uncertainty in social communications, leading individuals to be more optimistic about outcome expectations. Additionally, social trust boosts individuals’ social identity and sense of belonging, further reinforcing positive outcome expectations [30]. Therefore, a higher level of social trust will enhance the self-efficacy of reservoir resettlers in a new environment, making them more confident in facing the various challenges brought about by resettlement. The level of trust that resettlers have in the resettlement area directly affects their expectations for their future life. High levels of trust help them expect to find stable jobs, establish good social relationships, and accelerate their integration into the local society. Therefore, the following hypotheses are proposed:
H5. 
Social trust positively impacts the outcome expectations of reservoir resettlers.
H6. 
Social trust positively affects the self-efficacy of reservoir resettlers.

2.3.5. Social Networks, Self-Efficacy, and Outcome Expectations

Positive feedback, role models, and social support within social networks can significantly boost individuals’ self-efficacy. Conversely, exclusion, negative evaluations, or resource scarcity within social networks may weaken self-efficacy, leading to a lack of confidence when facing challenges [31]. Relevant studies have indicated that social networks have a positive impact on individuals’ self-efficacy, which means that the stronger and more positive the social network is, the higher the individual’s self-efficacy could be [32]. Furthermore, the flow of information and interactive relationships within social networks provide individuals with a foundation for building confidence and shaping their expectations. For reservoir resettlers, a strong and positive social network can greatly enhance their self-efficacy, enabling them to improve their expectations for the future by accessing information and resources through newly formed connections. Therefore, we propose the following hypotheses:
H7. 
Social networks positively affect the outcome expectations of reservoir resettlers.
H8. 
Social networks positively affect the self-efficacy of reservoir resettlers.

3. Research Design and Data Collection

3.1. Questionnaire Design

This study employed a five-point Likert scale (1 = strongly disagree to 5 = strongly agree), with items derived from mature scales used in authoritative domestic and international journals, verified for reliability and validity, and adapted to the characteristics of the resettlement population. Referencing Ladbury et al.’s expectancy behavior theory [24], the psychological models of Constantino et al., and other relevant research [33,34,35], outcome expectations were measured by assessing resettlers’ expectations regarding service improvements, income growth, and cultural adaptation. Adapting the self-efficacy models of Zander et al. and Shiau et al. [36,37], integrating them with Jugert et al.’s collective efficacy theory [38] and Wu et al.’s research [39], this study evaluates self-efficacy by measuring resettlers’ confidence in adapting to the environment, restoring livelihoods, and integrating into the culture. Based on Mongrain’s theoretical research [40], social norm studies by Farrow et al. [41], Helferich et al.’s analytical framework [42], and Liang’s research [43], social norms examines the regulatory effects of policy support, local agreements, and social morality on behavior. Social trust was measured by evaluating trust in village officials, residents, and relatives/friends, citing Wang et al.’s social capital research [44], Sønderskov and Dinesen’s institutional trust theory [45], and the social trust research of Schilke [46] and Tilt and Gerkey [47]. Based on gender network analysis and dynamic models of social capital [48,49,50,51], social networks were quantified by the number of households visited during festivals, the likelihood of seeking help in times of difficulty, and the closeness of relationships with relatives and friends. The dependent variable, social integration, was assessed through satisfaction with infrastructure, income stratification, and cultural familiarity, reflecting a multidimensional integration state, with theoretical foundations drawn from the “production-livelihood-security-integration” framework and related migrant integration studies [14,52,53,54]. The detailed content is shown in Table 1.

3.2. Data Collection

The data collection was conducted as part of a third-party evaluation project for the post-resettlement support of reservoir resettlers. The study focused on 10 sample counties in two provinces in central China, which were affected by a large-scale water conservancy project in central China. The resettlement populations in these 10 sample counties have comparable educational levels, professional skills, and overall resettlement status, making them highly representative. The 10 sample counties collectively resettled 35,477 households and 149,780 individuals, accounting for 49.91% and 48.50% of the total households and individuals resettled due to a large-scale water conservancy project, respectively. This study collected a total of 1100 questionnaires. After excluding invalid responses due to missing data, logical contradictions, and incomplete information, a final total of 1036 valid questionnaires were obtained, resulting in an effective response rate of 94.2%.
This study employed the triangulation method for data collection on reservoir resettlers, ensuring research reliability and validity through multi-source data and multi-stage verification. The survey team consisted of researchers from universities, social workers, and local resettlement officials, implementing a three-stage cross-validation process to ensure data quality: the first stage involved standardized household surveys; in the second stage, the survey team selected 15% of key sample households for semi-structured in-depth interviews while synchronously collecting economic records and subsequent support documentation from resettled families; in the third stage the survey team organized group discussions, inviting resettlement representatives, grassroots officials, and heads of social organizations for data verification and contextual interpretation. Detailed descriptions are shown in Table 2. The study strictly adhered to social science research ethics, with all respondents’ information being anonymized, and the original data stored independently by a third-party organization, ensuring that the research process could be traceable and complied with personal information protection requirements.

3.3. Methodology

SEM is a statistical approach based on hypothesized relationships among variables, integrating factor analysis and path analysis to examine structural relationships between latent variables and observed indicators. Its core functions include quantifying the effects of abstract concepts, testing multiple pathways, and evaluating model plausibility through fit indices. SEM excels in handling linear assumptions and measurement errors, making it suitable for verifying the statistical significance of theoretical frameworks. However, it cannot capture multifactorial interactions or nonlinear relationships.
fsQCA is a set-theory-based method that identifies complex causal relationships by analyzing how multiple condition combinations jointly produce outcomes. Its distinctive features include accommodating partial membership states of variables, uncovering equifinal pathways, and transcending conventional linear assumptions to explain asymmetric causality. fsQCA is particularly effective for exploring multivariate interaction scenarios, though it cannot quantify the independent effects of single variables and requires theoretical presuppositions to construct condition combinations.
In summary, this study employs an integrated SEM-fsQCA approach: SEM verifies the direct effects among variables of reservoir resettlers’ social integration to validate theoretical hypotheses, while fsQCA investigates the complex relationships formed through multifactorial interactions that shape social integration, revealing multiple equifinal configuration pathways. The former addresses “whether individual factors matter”, whereas the latter explains “which combinations work”. This dual approach avoids the oversimplification of linear assumptions while encompassing the diversity of real-world causality, collectively constructing a comprehensive analytical system that spans from simple causation to multivariate interaction.

4. Results and Analysis

4.1. Reliability and Validity Tests

This study used the statistical software SPSS 26.0 to perform descriptive analysis and reliability and validity tests on the sample data. Reliability was assessed by calculating the Cronbach’s alpha values of the model variables and their composite reliability (CR). Discriminant validity was tested by checking if the square root of the average variance extracted (AVE) for each factor was greater than the Pearson correlation coefficient values of other factors. As shown in Table 3 and Table 4, the Cronbach’s α values for all latent variables were greater than 0.85, and the CR values were greater than 0.8, indicating high reliability of the scale. The standardized factor loadings (SFL) for all items were greater than 0.7, and the AVE values were greater than 0.6, indicating good convergent validity. The square root of the AVE for each variable was greater than the correlation coefficients with other latent variables in the same row and column, indicating good discriminant validity.

4.2. SEM

This study utilized Amos 23.0 to conduct confirmatory factor analysis (CFA) on the variables, and the CFA model is shown in Figure 2. As indicated in Table 5, the model’s Chi-square value is 175.623, with 124 degrees of freedom (DF), resulting in a Chi-square-to- DF ratio of 1.416, which is well below the threshold of 3, indicating a high fit between the model and the data. The fit indices Chi-Minimum (CMIN)/DF = 1.416, GFI = 0.964, CFI = 0.991, TLI = 0.989, and the root mean square error of approximation (RMSEA) = 0.028 all meet the standard criteria, showing good model fit. Additionally, the goodness-of-fit index (GFI) is 0.964, and the adjusted goodness-of-fit index (AGFI) is 0.951, both significantly exceeding the 0.9 benchmark, suggesting that the model structure is reasonable and well-fitted. The root mean square error of approximation (RMSEA) is 0.028, far below the critical value of 0.08, further confirming the excellent fit of the model. Moreover, the comparative fit index (CFI) and Tucker–Lewis index (TLI) are 0.991 and 0.989, respectively, both well above the 0.9 standard, demonstrating a very high fit between the model and the data.
As shown in Table 6, social norms, social trust, and social networks positively impact self-efficacy and outcome expectations. Self-efficacy and outcome expectations positively influence social integration. Specifically, social norms significantly impact self-efficacy (β = 0.169, p < 0.001) and outcome expectations (β = 0.192, p < 0.001), confirming the paths through which social norms influence self-efficacy and outcome expectations. Outcome expectations significantly affect social integration (β = 0.177, p < 0.001), and self-efficacy also positively impacts social integration (β = 0.127, p < 0.05). Social trust exerts a significant positive influence on self-efficacy (β = 0.294, p < 0.001) and outcome expectations (β = 0.304, p < 0.001), indicating that the hypotheses regarding the paths through which social trust affects self-efficacy and outcome expectations hold true. Social networks significantly impact self-efficacy (β = 0.22, p < 0.001) and positively affect outcome expectations (β = 0.116, p < 0.05), confirming the paths through which social networks influence self-efficacy and outcome expectations. Based on the path analysis results, a single sufficient condition for achieving the resettlers’ social integration is identified. However, single sufficient conditions rarely exist in practice; what is more common are single necessary conditions. Therefore, fsQCA software is needed to test the necessary conditions that contribute to the social integration of reservoir resettlers.

4.3. fsQCA Testing

The results of the SEM above have confirmed the linear relationships between social norms, social networks, social trust, self-efficacy, outcome expectations, and social integration. However, these results do not allow for the exploration of the “interaction effects” among the conditional variables. To validate and supplement the empirical findings, the fsQCA method is further employed to systematically examine the interaction and possible combinatorial relationships between key factors and internal generative factors that trigger social integration, the interactive relationships among internal generating factors, and the possible combinations of these relationships. This approach uses configurational thinking to explain the multiple concurrent causal patterns leading to resettlers’ social integration. Therefore, the combination of fsQCA and SEM not only effectively addresses the limitations of each individual method, but also enhances the reliability and validity of the results.

4.3.1. Data Calibration

This study adopted the approach of Ragin and Strand [55], using scores of 1 (strongly disagree), 3 (neutral), and 5 (strongly agree) to represent complete non-affiliation (0.05), the crossover point (0.5), and complete affiliation (0.95), respectively. Considering that the fsQCA 3.0 software automatically ignores samples with a value of 0.5 after data calibration, the value of 0.5 after calibration was manually adjusted to 0.501 to ensure the validity of all data and the integrity of the configurational analysis results.

4.3.2. Necessity Analysis

According to Schneider and Wagemann’s study [56], if the consistency of a causal condition exceeds 0.9, that condition can be considered a necessary condition. There is no recommended minimum value for the coverage score, but variables or combinations with a coverage score close to 0 are considered to have negligible influence on the outcome variable. As shown in Table 7, the consistency values of the causal conditions are all below the critical value of 0.9, which means that they are not necessary conditions. This result reflects the presence of “multiple concurrent causes” in the social integration of reservoir resettlers, necessitating a configurational analysis of the causal conditions to examine the combined effects of these conditions.

4.3.3. Sufficiency Analysis of Condition Configurations

Fiss suggested that the minimum frequency threshold should be 1 [57]. Based on the sample size of this study, the acceptable number of cases is set to 5. Ragin proposed that the consistency threshold should not be lower than 0.8, with most studies setting it between 0.8 and 0.9, and a few scholars using 0.75 as the consistency threshold, based on the range proposed by Ragin in 2017. This study sets the consistency threshold to 0.8, based on the standards from numerous cases and practical testing. As shown in Table 8, there are five configurations corresponding to the social integration of reservoir resettlers, with an overall consistency of 0.834, which exceeds the theoretical threshold of 0.8. The consistency for each individual antecedent configuration is also greater than 0.8, indicating that these five antecedent configurations are sufficient conditions for achieving the social integration of reservoir resettlers. The overall coverage rate is 0.540, meaning that these five configurations effectively explain 54.03% of the real-world cases. Overall, these five different configurations can adequately explain the process of social integration among reservoir resettlers, and the five configurations in Table 8 can be considered as sufficient combinations of social capital and social cognition influencing the social integration of reservoir resettlers.
Based on the core conditions of the five configurations, three driving paths for achieving social integration among reservoir resettlers can be summarized:
(1) Internal-external driving path: This path revolves around the interaction of “social networks × outcome expectations” and includes configurations S1, S2, and S3. Reservoir resettlers can acquire accurate information about their new environment through social networks, which helps them to understand local cultural customs and social norms. This helps them gradually to adjust their cognitive frameworks, accept, and adapt to the new cultural and social environment. This cognitive adjustment and adaptation are important indicators of psychological social integration. Furthermore, the strengthening of social networks provides resettlers with more social opportunities and resource support, contributing to an improved outcome expectation, which in turn facilitates adaptation. Positive outcome expectations enhance the resettlers’ intention and ability to participate in social networks, allowing them to actively expand and maintain their social connections. This, in turn, further strengthens the social network. The mutual positive influence between social network strengthening and outcome expectation improvement jointly drives the process of social integration. By constructing extensive and deep social connections and promoting resettlers’ psychological adaptation and cognitive adjustment, both factors work together to advance the social integration process in the new environment. This process not only reflects the crucial role of social networks and outcome expectations in promoting social integration, but also highlights the close logical relationship and joint action mechanism between the two influencing factors.
(2) Proactive integration path: This path operates around the interaction of “social trust × self-efficacy × outcome expectations” and is represented by configuration S4. Under this path, enhanced social trust provides resettlers with a positive social environment, laying the external conditions necessary for active economic, cultural, and psychological integration. Meanwhile, improvement of self-efficacy boosts resettlers’ confidence and initiative in social interactions, further promoting the establishment of social trust. At the same time, positive outcome expectations also motivate resettlers to understand and embrace the cultural customs of the new environment with greater initiative, making it easier for them to establish shared values and behavioral norms with local residents, which in turn strengthens social trust. The combined effect of building social trust, enhancing self-efficacy, and improving outcome expectations forms the active integration path for reservoir resettlers’ social integration. This path promotes the social integration process of resettlers in the new environment by establishing stable psychological expectations, stimulating intrinsic motivation, facilitating psychological adaptation, and enhancing social interactions. It not only reflects the crucial role of social trust and social cognition as key elements, but also reveals the logical relationship and joint action mechanism between them.
(3) Capital-enabled Path: This path revolves around the interaction of “social norms × social trust × social networks” and is represented by configuration S5. The capital-enabled driving path, led by social capital, illustrates the effect of the external social environment on resettlers’ social integration, highlighting the advantages and characteristics of social capital. Due to its structural advantages, social capital can create a multi-level social environment through elements such as social networks, social trust, and social norms, providing resettlers with extensive access to integration resources. In contrast, social cognition pertains to an individual’s perception and understanding of the environment, but its influence is relatively limited and cannot match the structural effects of social capital. Furthermore, social capital is dynamic and timely. It constantly improves and accumulates during interactions between resettlers and the host community, allowing it to respond promptly to resettlers’ needs and challenges. Governments and societies can enhance support for resettlers within a relatively short period by adjusting the structure of social networks, raising levels of social trust, and refining social norms. While social cognition also evolves with accumulated experience, inherent social cognition requires a longer period for substantial change. In contrast, financial and monetary support should be provided accurately, as generous income support can adversely affect the resettlers’ incentives, potentially slowing down their integration in the host society [58].Therefore, social capital, with its unique structural advantages and dynamic timeliness, exerts a greater influence on promoting the social integration of reservoir resettlers, surpassing the influence of social cognition.

5. Discussion

This study integrates SCT with TSC to deconstruct the formation logic of social integration among reservoir resettlers, providing more precise measurement of the path coefficients and configuration analysis of social norms, social networks, social trust, self-efficacy, and outcome expectations in relation to the social integration of these resettlers. The findings indicate that resettlers’ social integration is positively influenced by social norms, social networks, social trust, self-efficacy, and outcome expectations, which aligns with the studies of Zhang [59] and Tomul [60]. As the elements of social capital continue to improve, social norms provide a good order to support the social integration of reservoir resettlers, social networks offer opportunities and platforms for integration, and social trust strengthens interpersonal relationships among resettlers. On top of these external capital factors, the enhancement of self-efficacy further boosts the initiative and enthusiasm of resettlers toward social integration. The study also reveals that social norms exert the weakest positive impact on resettlers’ social integration, which is consistent with Rhodes [61], who found that the overall effect of social norms on behavioral outcomes was often minimal. This may be due to the fact that the formulation of social norms tends to lag behind actual needs for social integration, and their enforcement is constrained by multiple factors, such as residents’ personal intention, cultural background, and economic interests. These factors may lead to a disconnect between the norms and actual behavior, thus potentially producing negative guidance in relation to social cognition.
Additionally, social cognition and social capital impact the social integration of reservoir resettlers in both direct and combinatory ways. On the one hand, they directly impact integration through the “social capital/social cognition → social integration” pathway. On the other hand, they indirectly affect integration through three configuration paths: the internal-external driving path, the proactive integration path, and the capital-enabled path. Social trust and outcome expectations are key factors influencing the social integration of reservoir resettlers across various configuration paths, which aligns with the findings of Ziller et al. [62]. Only when resettlers genuinely trust society and its members can they enhance their initiative and enthusiasm. Due to the vulnerability of resettlers’ livelihoods and the uncertainty of the social environment, they tend to over-estimate the risks and difficulties they may encounter in a new setting. This often leads to a reluctance to be the “first mover” in new situations, further hindering their social integration process.
This study also attempts to explore the complex reasons behind the social integration issues of reservoir resettlers. Resettlers often struggle to establish robust social networks in the short term, exhibiting limited participation in community governance and public affairs. Cultural differences also necessitate considerable time for genuine coexistence and integration with host communities [63]. Compared to variables like social trust, social networks, and outcome expectations, social norms and self-efficacy among reservoir resettlers emerge as critical yet understudied dimensions which require prioritized attention. Three key reasons account for this research gap: Firstly, while existing theories consistently emphasize the primacy of social norms and self-efficacy in resettlers’ integration studies, their actual impacts are frequently overshadowed by other factors in empirical research. Social norms often operate as invisible forces resistant to direct quantification, whereas resettlers’ self-efficacy may experience temporary inflation due to short-term economic subsidies––a phenomenon that fails to reflect long-term psychological adaptation, ultimately compromising research accuracy. Secondly, prevailing measurements of self-efficacy risk oversimplification. Most studies equate it narrowly with economic adaptation capacity, neglecting its multidimensional manifestations in cultural adaptation, political participation, and other domains. This economic-centric approach overlooks how cultural dissonance undermines efficacy perceptions, rendering support programs ineffective in cultivating sustainable developmental capacities. Thirdly, the research methods for studying the social norms and self-efficacy of reservoir resettlers need to be improved. Current findings predominantly rely on static data analyses that cannot capture the dynamic nature of social norms and self-efficacy. For example, in the early stages of resettlement, resettlers may doubt themselves due to conflicts in social norms. However, as their skills improve and social networks expand, they gradually build confidence and actively adapt to new norms. Such evolutionary relationships require longitudinal tracking that conventional methods fail to deliver.

6. Conclusions

6.1. Summary

This study integrates the theoretical perspectives of social capital and social cognition, employing SEM and fsQCA methods to examine the factors influencing social integration of reservoir resettlers and the configuration-driven pathways. The findings of this study hold significant value for the formulation of policies aimed at supporting the social integration of reservoir resettlers in the future.
The study finds that social norms, social trust, and social networks positively influence the self-efficacy and outcome expectations of reservoir resettlers, while both self-efficacy and outcome expectations have a positive impact on their social integration. However, none of these factors—social norms, social trust, social networks, self-efficacy, or outcome expectations—can act as a necessary condition for social integration on their own. Instead, they all depend on their interaction with other elements to collectively shape the integration process. Furthermore, the configuration analysis identifies five key configurations that drive social integration, which can be categorized into three main driving paths: the internal-external driving path, the proactive integration path, and the capital-enabled path. These findings validate the notion of “diverse paths leading to the same outcome”, illustrating the complexity of factors influencing the integration process.

6.2. Recommendations

On the basis of the aforementioned research findings, the following recommendations are proposed:
Firstly, social capital construction should be reinforced, and the resettled support network should be improved. The local government should establish community mutual aid platforms to organize cultural activities and skill exchanges that involve both resettlers and local residents, thereby expanding their social networks. Policy implementation processes should be disclosed transparently and publicly, such as regularly announcing subsidy distribution information, which aims at reducing information asymmetry and enhancing resettlers’ trust in the government and community. Clear and easy-to-understand village rules and regulations should be formulated to specify the rights and obligations of resettlers. Furthermore, by promoting successful integration cases, resettlers can be guided to actively adapt to local norms, improving the quality of social capital and laying a stable foundation for integration.
Secondly, social cognitive abilities should be enhanced to boost resettlers’ adaptation confidence. Adaptive training should be conducted in resettlement communities to help resettlers learn the local language and customs, alleviate anxiety, and build confidence in their adaptability. Considering resettlers’ existing skills and local industry demands, the local government should provide practical vocational training in areas such as modern agricultural technology and domestic services, to ensure resettlers acquire a livelihood capability and foster positive expectations for their future. Additionally, successful cases of resettlers’ entrepreneurship or employment should be highlighted to encourage more resettlers to actively participate in community activities, converting external resources into practical actions through role model promotion.
Finally, differentiated policies should be formulated to match diverse integration paths. A dynamic monitoring mechanism should be established to regularly assess changes in resettlers’ needs. In the initial phase, priority should be given to ensuring basic living support such as housing and healthcare. In the medium phase, a volunteer service points system should be implemented to encourage participation in community governance. In the later phase, targeted support such as career planning for young people or recreational activities for the elderly should be provided. Resources from civil affairs, human resources, and education departments should be integrated to avoid policy duplication or blind spots.

6.3. Limitations and Prospects

However, the study has some limitations. Due to the complex interplay of factors affecting resettlers’ social integration, the proposed framework model only incorporates SCT and TSC, omitting other potential influencing factors. Moreover, the integration process is influenced by numerous other variables. Methodologically, while this study employs SEM and fsQCA for analysis, future research could include observational and experimental methods to further measure and explore the social integration of reservoir resettlers.

Author Contributions

Conceptualization, L.D. and Z.S.; methodology, J.C. (Jiachuan Chen); software, L.D. and J.C. (Jihao Chen); validation, L.D. and J.C. (Jiachuan Chen); formal analysis, Z.S.; investigation, L.D. and J.C. (Jihao Chen); resources, J.C. (Jiachuan Chen); data curation, J.C. (Jiachuan Chen) and J.C. (Jihao Chen); writing––original draft preparation, L.D.; writing––review and editing, L.D.; visualization, J.C. (Jiachuan Chen); supervision, L.D.; project administration, J.C. (Jihao Chen); funding acquisition, L.D. All authors have read and agreed to the published version of the manuscript.

Funding

This work was financially supported by the Social Science planning project of Henan Province (2024CJJ241).

Data Availability Statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Acknowledgments

We gratefully acknowledge the anonymous reviewers for their insightful comments on and suggestions for this paper.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Xu, Y.; Shi, G.; Dong, Y. Effects of the Post-Relocation Support Policy on Livelihood Capital of the Reservoir Resettlers and Its Implications—A Study in Wujiang Sub-Stream of Yangtze River of China. Sustainability 2022, 14, 2488. [Google Scholar] [CrossRef]
  2. Li, Q.; Xu, Y.; Zhao, X.; Xie, J.; Jiao, T.; Su, Z. Research on the Livelihood Capital and Livelihood Strategies of Resettlement in China’s South-to-North Water Diversion Middle Line Project. Front. Sustain. Food Syst. 2024, 8, 1396705. [Google Scholar] [CrossRef]
  3. Feng, L.; Yang, W.; Yun, J.; Zhang, Y. The Path of Social Integration of Resettlers in Poverty Alleviation Relocation: A Case Study of Dongchuan from Yunnan Plateau Mountainous Areas. J. Rural Stud. 2024, 110, 103381. [Google Scholar] [CrossRef]
  4. Klarenbeek, L. Relational Integration: From Integrating Resettlers to Integrating Social Relations. J. Ethn. Migr. Stud. 2024, 50, 233–256. [Google Scholar] [CrossRef]
  5. Yang, C.; Xu, H. Direct and Spillover Effects: How Do Community-Based Organizations Impact the Social Integration of Passive Resettlers? Sustainability 2024, 16, 4530. [Google Scholar] [CrossRef]
  6. Guo, X.; Li, X.; Li, L.; Wang, X. Impact of Social Integration and Government Support on Ecological Imresettlers’ Vulnerability to Poverty. Environ. Ecol. Stat. 2023, 30, 61–79. [Google Scholar] [CrossRef]
  7. Shen, H.; Xia, L. Research on Social Integration Structure and Path of Floating Population Based on Structural Equation Model: Evidence from China. Soc. Indic. Res. 2023, 165, 607–632. [Google Scholar] [CrossRef]
  8. Pan, Z.; Li, Y.; Jia, Z.; Zhou, Z. Community Support as a Driver for Social Integration in Ex-Situ Poverty Alleviation Relocation Communities: A Case Study in China. Hum. Soc. Sci. Commun. 2024, 11, 1206. [Google Scholar] [CrossRef]
  9. Huang, L.; Huang, J.; Wang, W. The Sustainable Development Assessment of Reservoir Resettlement Based on a BP Neural Network. Int. J. Environ. Res. Public Health 2018, 15, 146. [Google Scholar] [CrossRef]
  10. Rousseau, J.-F.; Orange, D.; Habich-Sobiegalla, S.; Nguyen, V.T. Socialist Hydropower Governances Compared: Dams and Resettlement as Experienced by Dai and Thai Societies from the Sino-Vietnamese Borderlands. Reg. Environ. Change 2017, 17, 2409–2419. [Google Scholar] [CrossRef]
  11. Yu, B.; Xu, L.; Wang, X. Ecological Compensation for Hydropower Resettlement in a Reservoir Wetland Based on Welfare Change in Tibet, China. Ecol. Eng. 2016, 96, 128–136. [Google Scholar] [CrossRef]
  12. Zhu, D.; Jia, Z.; Zhou, Z. Place Attachment in the Ex-Situ Poverty Alleviation Relocation: Evidence from Different Poverty Alleviation Migrant Communities in Guizhou Province, China. Sust. Cities Soc. 2021, 75, 103355. [Google Scholar] [CrossRef]
  13. Ru, Z.; Long, D.; Jing, L.; Yang, M.; Wen, X. Ecological Resettlers’ Socio-Spatial Integration in Yinchuan City, China. PLoS ONE 2022, 17, e0275853. [Google Scholar] [CrossRef]
  14. Shangguan, Z.; Wang, M.Y.; Huang, J.; Shi, G.; Song, L.; Sun, Z. Study on Social Integration Identification and Characteristics of Resettlers from “Yangtze River to Huaihe River” Project: A Time-Driven Perspective. Sustainability 2020, 12, 211. [Google Scholar] [CrossRef]
  15. Fearnside, P. Chinas 3 Gorges Dam—Fatal Project or Step Toward Modernization. World Dev. 1988, 16, 615–630. [Google Scholar] [CrossRef]
  16. Bandura, A. Social Cognitive Theory: An Agentic Perspective. Annu. Rev. Psychol. 2001, 52, 1–26. [Google Scholar] [CrossRef]
  17. Cicourel, A.V. Collective Memory, A Fusion of Cognitive Mechanisms and Cultural Processes. Rev. Synth. 2015, 136, 309–328. [Google Scholar] [CrossRef]
  18. Bourdieu, P. The Logic of Practice; Stanford University Press: Redwood City, CA, USA, 1990; p. 112. [Google Scholar]
  19. Coleman, J.S. Foundations of Social Theory; Harvard University Press: Cambridge, MA, USA, 1994; pp. 300–531. [Google Scholar]
  20. Putnam, R.D. Bowling Alone: The Collapse and Revival of American Community; Simon and Schuster: New York City, NY, USA, 2000; pp. 208–253. [Google Scholar]
  21. Tian, J.; Zheng, X.; Sun, Y. Fostering Public Climate Change Discussions from a Social Interaction Perspective. Front. Psychol. 2023, 14, 1258150. [Google Scholar] [CrossRef]
  22. Huang, R.; Qian, Q.K.; Liu, G.; Li, K.; Visscher, H.J.; Fu, X.; Wang, W. Multi-Level Social Capital Effects on Residents: Residents’ Cooperative Behavior in Neighborhood Renewal in China. Land Use Pol. 2025, 148, 107383. [Google Scholar] [CrossRef]
  23. Yen, Y.-S. Perceived Social Support Moderates the Relationships between Variables in the Social Cognition Model. Behav. Inf. Technol. 2016, 35, 479–489. [Google Scholar] [CrossRef]
  24. Ladbury, J.L.; Hinsz, V.B. How the Distribution of Member Expectations Influences Cooperation and Competition in Groups: A Social Relations Model Analysis of Social Dilemmas. Pers. Soc. Psychol. Bull. 2018, 44, 1502–1518. [Google Scholar] [CrossRef] [PubMed]
  25. Kim, Y.K.; Maleku, A.; Lim, Y.; Kagotho, N.; Scott, J.; Ketchum, M.M. Financial Challenges and Capacity among African Refugees in the Southern USA: A Study of Socio-Demographic Differences. Br. J. Soc. Work 2022, 52, 1529–1551. [Google Scholar] [CrossRef]
  26. Gong, Y.; Zhang, R.; Yao, K.; Liu, B.; Wang, F. A Livelihood Resilience Measurement Framework for Dam-Induced Displacement and Resettlement. Water 2020, 12, 3191. [Google Scholar] [CrossRef]
  27. Mannberg, A.; Sjogren, T. Conflicting Identities and Social Pressure: Effects on the Long-Run Evolution of Female Labour Supply. Oxf. Econ. Pap.-New Ser. 2015, 67, 269–290. [Google Scholar] [CrossRef]
  28. Bicchieri, C.; Dimant, E. Nudging with Care: The Risks and Benefits of Social Information. Public Choice 2022, 191, 443–464. [Google Scholar] [CrossRef]
  29. Gwebu, K.L.; Wang, J.; Zifla, E. Can Warnings Curb the Spread of Fake News? The Interplay between Warning, Trust and Confirmation Bias. Behav. Inf. Technol. 2022, 41, 3552–3573. [Google Scholar] [CrossRef]
  30. Patterson, M.S.; Amo, C.E.; Prochnow, T.; Heinrich, K.M. Exploring Social Networks Relative to Various Types of Exercise Self-Efficacy within CrossFit Participants. Int. J. Sport Exerc. Psychol. 2022, 20, 1691–1710. [Google Scholar] [CrossRef]
  31. Whitaker, R.M.; Colombo, G.B.; Turner, L.; Dunham, Y.; Doyle, D.K.; Roy, E.M.; Giammanco, C.A. The Coevolution of Social Networks and Cognitive Dissonance. IEEE Trans. Comput. Soc. Syst. 2022, 9, 376–393. [Google Scholar] [CrossRef]
  32. Yuan, Y.; Du, J.; Yang, K.; Ge, Y.; Ma, Y.; Mao, H.; Xiang, M.; Wu, D. Relationship between Horizontal Collectivism and Social Network Influence among College Students: Mediating Effect of Self-Monitoring and Moderating Effect of Self-Efficacy. Front. Psychol. 2024, 15, 1424223. [Google Scholar] [CrossRef]
  33. Constantino, M.J.; Arnkoff, D.B.; Glass, C.R.; Ametrano, R.M.; Smith, J.Z. Expectations. J. Clin. Psychol. 2011, 67, 184–192. [Google Scholar] [CrossRef]
  34. Mergel, I.; Edelmann, N.; Haug, N. Defining Digital Transformation: Results from Expert Interviews. Gov. Inf. Q. 2019, 36, 101385. [Google Scholar] [CrossRef]
  35. Zhao, Y.; Yang, C.; Shi, G.; Xu, Y.; Mei, X.; Yan, D.; Zhang, H.; Dong, Y.; Lowell, Z. From Order to Reorder: Assessment of the Living Environment of Hydropower Resettlement for Just Energy Transition in China. Energy Rep. 2024, 11, 5668–5681. [Google Scholar] [CrossRef]
  36. Zander, L.; Brouwer, J.; Jansen, E.; Crayen, C.; Hannover, B. Academic Self-Efficacy, Growth Mindsets, and University Students’ Integration in Academic and Social Support Networks. Learn. Individ. Differ. 2018, 62, 98–107. [Google Scholar] [CrossRef]
  37. Shiau, W.-L.; Yuan, Y.; Pu, X.; Ray, S.; Chen, C.C. Understanding Fintech Continuance: Perspectives from Self-Efficacy and ECT-IS Theories. Ind. Manag. Data Syst. 2020, 120, 1659–1689. [Google Scholar] [CrossRef]
  38. Jugert, P.; Greenaway, K.H.; Barth, M.; Büchner, R.; Eisentraut, S.; Fritsche, I. Collective Efficacy Increases Pro-Environmental Intentions through Increasing Self-Efficacy. J. Environ. Psychol. 2016, 48, 12–23. [Google Scholar] [CrossRef]
  39. Wu, Y.M.; Kreitewolf, J.; Kronick, R. The Relationship between Wellbeing, Self-Determination, and Resettlement Stress for Asylum-Seeking Mothers Attending an Ecosocial Community-Based Intervention: A Mixed-Methods Study. Int. J. Environ. Res. Public Health 2023, 20, 7076. [Google Scholar] [CrossRef]
  40. Mongrain, P. With a Little Help from My Friends? The Impact of Social Networks on Citizens’ Forecasting Ability. OEr. J. Polit. Res. 2023, 62, 1320–1346. [Google Scholar] [CrossRef]
  41. Farrow, K.; Grolleau, G.; Ibanez, L. Social Norms and Pro-Environmental Behavior: A Review of the Evidence. Ecol. Econ. 2017, 140, 1–13. [Google Scholar] [CrossRef]
  42. Helferich, M.; Thøgersen, J.; Bergquist, M. Direct and Mediated Impacts of Social Norms on Pro-Environmental Behavior. Glob. Environ. Change 2023, 80, 102680. [Google Scholar] [CrossRef]
  43. Liang, B.; Shi, G.; Sun, Z.; Wang, Y.; Zhang, B.; Xu, Y.; Dong, Y. Impact Assessment of the Implementation Effect of the Post-Relocation Support Policies of Rural Reservoir Resettlers’ Livelihoods in Energy Transition. Water 2023, 15, 3129. [Google Scholar] [CrossRef]
  44. Wang, Y.; Gao, M.; Zuo, J.; Bartsch, K. Social Capital and Social Integration after Project-Induced Displacement and Resettlement: Exploring the Impact on Three Life Stages in the Three Gorges Project. Soc. Sci. J. 2023, 60, 56–74. [Google Scholar] [CrossRef]
  45. Sønderskov, K.M.; Dinesen, P.T. Trusting the State, Trusting Each Other? The Effect of Institutional Trust on Social Trust. Polit. Behav. 2016, 38, 179–202. [Google Scholar] [CrossRef]
  46. Schilke, O.; Reimann, M.; Cook, K.S. Trust in Social Relations. Annu. Rev. Sociol. 2021, 47, 239–259. [Google Scholar] [CrossRef]
  47. Tilt, B.; Gerkey, D. Dams and Population Displacement on China’s Upper Mekong River: Implications for Social Capital and Social–Ecological Resilience. Glob. Environ. Change 2016, 36, 153–162. [Google Scholar] [CrossRef]
  48. Quetulio-Navarra, M.; Znidarsic, A.; Niehof, A. Gender Perspective on the Social Networks of Household Heads and Community Leaders after Involuntary Resettlement. Gend. Place Cult. 2017, 24, 225–246. [Google Scholar] [CrossRef]
  49. Brady, W.J.; Wills, J.A.; Jost, J.T.; Tucker, J.A.; Van Bavel, J.J. Emotion Shapes the Diffusion of Moralized Content in Social Networks. Proc. Natl. Acad. Sci. USA 2017, 114, 7313–7318. [Google Scholar] [CrossRef]
  50. Yan, Y.; Guan, J. Social Capital, Exploitative and Exploratory Innovations: The Mediating Roles of Ego-Network Dynamics. Technol. Forecast. Soc. Change 2018, 126, 244–258. [Google Scholar] [CrossRef]
  51. Hong, X.; Chen, S.; Sun, J. Role of Non-Governmental Organizations in Post-Relocation Support of Reservoir Migrations in China: A Just Transition Perspective. Front. Environ. Sci. 2023, 11, 1339953. [Google Scholar] [CrossRef]
  52. Peng, Z.; Xiao, X.; Lv, Y.; Guan, X.; Wang, W. A Large-Scale Investigation of the Status of Out-Resettlers from the Three Gorges Area Based on the Production-Living-Social Security-Social Integration-Satisfaction Perspective. Sustainability 2022, 14, 15613. [Google Scholar] [CrossRef]
  53. Galesic, M.; Barkoczi, D.; Berdahl, A.M.; Biro, D.; Carbone, G.; Giannoccaro, I.; Goldstone, R.L.; Gonzalez, C.; Kandler, A.; Kao, A.B.; et al. Beyond Collective Intelligence: Collective Adaptation. J. R. Soc. Interface 2023, 20, 20220736. [Google Scholar] [CrossRef]
  54. Landesmann, M.; Leitner, S.M. Various Domains of Integration of Refugees and Their Interrelationships: A Study of Recent Middle Eastern Refugee Inflows in Austria. Eur. J. Popul. 2025, 41, 6. [Google Scholar] [CrossRef] [PubMed]
  55. Ragin, C.C.; Strand, S.I. Using Qualitative Comparative Analysis to Study Causal Order—Comment on Caren and Panofsky (2005). Sociol. Methods. Res. 2008, 36, 431–441. [Google Scholar] [CrossRef]
  56. Schneider, C.Q.; Wagemann, C. Set-Theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis; Strategies for Social Inquiry; Cambridge University Press: Cambridge, UK, 2012; pp. 1–350. [Google Scholar]
  57. Fiss, P.C. Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Acad. Manag. J. 2011, 54, 393–420. [Google Scholar] [CrossRef]
  58. Azarnert, L.V. Immigration, Fertility, and Human Capital: A Model of Economic Decline of the West. Eur. J. Political Econ. 2010, 26, 431–440. [Google Scholar] [CrossRef]
  59. Zhang, H.; Cao, Z.; Zhou, K.; Ji, Y. Research on the Relationship between Social Capital and Sustainable Livelihood: Evidences from Reservoir Resettlers in the G Autonomous Prefecture, China. Front. Environ. Sci. 2024, 11, 1358386. [Google Scholar] [CrossRef]
  60. Tomul, E.; Taslidere, E.; Almış, S.; Açıl, E. The Moderation Effect of Economic, Social and Cultural Status on Mediating Role of Adaptability and Intercultural Sensitivity in the Relationship between Self-Efficacy and Attitudes towards Imresettlers in Türkiye. Curr. Psychol. 2024, 43, 19264–19282. [Google Scholar] [CrossRef]
  61. Rhodes, N.; Shulman, H.C.; McClaran, N. Changing Norms: A Meta-Analytic Integration of Research on Social Norms Appeals. Hum. Commun. Res. 2020, 46, 161–191. [Google Scholar] [CrossRef]
  62. Ziller, C.; Schlösser, T.; Schück, K.; Partos, K. Local Ties That Bind: The Role of Perceived Neighborhood Cohesion and Disorder for Imresettlers’ National Identification. Soc. Sci. Res. 2023, 112, 102803. [Google Scholar] [CrossRef]
  63. Peng, Q.; He, W.; Kong, Y.; Shen, J.; Yuan, L.; Stephen Ramsey, T. Spatio-Temporal Analysis of Water Sustainability of Cities in the Yangtze River Economic Belt Based on the Perspectives of Quantity-Quality-Benefit. Ecol. Indic. 2024, 160, 111909. [Google Scholar] [CrossRef]
Water 17 01073 g001
Figure 1. Theoretical model construction. Note: + indicates a positive impact.
Figure 1. Theoretical model construction. Note: + indicates a positive impact.
Water 17 01073 g001
Water 17 01073 g002
Figure 2. Model path diagram. Note: *, *** denote significance at 0.05 and 0.001 levels, respectively.
Figure 2. Model path diagram. Note: *, *** denote significance at 0.05 and 0.001 levels, respectively.
Water 17 01073 g002
Table 1. Variable measurement question items.
Table 1. Variable measurement question items.
VariablesMeasurement IndicatorsMeasurement ItemsMeasurement StandardReferences
Outcome expectations
(OE)		OE1Living locally helps to enjoy better services.completely disagree = 1; disagree = 2; fairly agree = 3; agree = 4;
completely agree = 5		Ladbury & Hinsz [24]
Constantino et al. [33]
Mergel et al. [34]
Zhao et al. [35]
OE2Living locally helps increase income.
OE3Adapting to local cultural customs contributes to the improvement of life quality.
Self-efficacy
(SE)		SE1I’m confident that I’ll be able to adapt to the local living environment.Zander et al. [36]
Shiau et al. [37]
Jugert et al. [38]
Wu et al. [39]
SE2I believe that I am able to restore my livelihood locally.
SE3I’m confident that I’ll fit in with the local culture and customs.
Social norms (SNM)SNM1The policies and supporting systems introduced by the government are conducive to the integration of resettlers into local life.Mongrain [40]
Farrow et al. [41]
Helferich et al. [42]
Liang et al. [43]
SNM2Village regulations and civil agreements contribute to harmonious coexistence among villagers.
SNM3Social morality helps reservoir resettlers receive assistance.
Social trust
(ST)		ST1Village officials are trustworthy.Wang et al. [44]
Sønderskov & Dinesen [45]
Schilke [46]
Tilt & Gerkey [47]
ST2Local residents are trustworthy.
ST3Relatives and friends are trustworthy.
Social network (SNK)SNK1The number of visits to relatives and friends during the Spring FestivalFive categories of actual data, from lowest to highest, 1–5, are assigned as follows: 0 = 1; 1–5 = 2; 6–10 = 3; 11–15 = 4; 16 and above = 5.Quetulio-Navarra et al. [48]
Brady et al. [49]
Yan & Guan [50]
Hong et al. [51]
SNK2The likelihood of receiving help from relatives and friends when encountering difficultiesdefinitely impossible = 1; impossible = 2; possible = 3; probable = 4; totally probable = 5
SNK3Relationships with relatives and friendsextremely distant = 1; distant = 2; fairly close = 3; close = 4;
extremely close = 5
Social integration (SI)SI1Satisfaction with local infrastructurecompletely disagree = 1; disagree = 2; fairly agree = 3; agree = 4; completely agree = 5Shangguan et al. [14]
Peng et al. [52]
Galesic et al. [53]
Landesmann & Leitner [54]
SI2The average annual household incomeFive categories of actual data, from lowest to highest, 1–5, are assigned as follows: Less than ¥20,000 = 1; ¥20,000–40,000 = 2; ¥50,000–60,000 = 3; ¥70,000–80,000 = 4; Above ¥80,000 = 5.
SI3The degree of familiarity with local customs and traditionscompletely unfamiliar = 1;
unfamiliar = 2; fairly familiar = 3; familiar = 4; completely familiar = 5
Table 2. Summary of data collection methods.
Table 2. Summary of data collection methods.
Collection MethodsPrimary SourcesDetailed Descriptions
In-depth interviewsOfficials of the county resettlement management bureau, staff of resettlement areas, and representatives of resettlersThe interviews were conducted in three stages, covering the core content of the questionnaire. A total of 12 resettlement officials were interviewed (including 3 heads of the county resettlement bureau), 9 community workers, and representatives of 25 resettled families (including young and middle-aged individuals as well as the elderly). Additionally, 6 heads of social organizations were interviewed. The interviews were transcribed into approximately 58,000 words.
Questionnaire surveyReservoir relocation families and local residentsThe study utilized stratified random sampling in resettlement areas under the post-resettlement support evaluation project, with cross-validation to guarantee data reliability.
Non-participant observationPublic activities in resettlement communities, and daily interaction scenariosThrough longitudinal participatory observation, the research team documented migrant integration processes by engaging with 27 community events (e.g., deliberative meetings, cultural celebrations, vocational workshops), systematically recording ethnographic data in field journals.
Internal documentsCounty resettlement management bureau, resettlement community committee, and social organizations(1) Policy documents: including The 14th Five-Year Plan for Post-Resettlement Support of Reservoir Migrants and other related support policies;
(2) Administrative archives: including livelihood records of resettlement households and community assistance documentation;
(3) Meeting minutes: Including thematic meetings like “resettlers’ integration progress conference” and “interdepartmental coordination sessions”.
External public informationGovernment official website, academic databases, and media reports(1) Policy texts: provincial resettlement regulations and central government policies on reservoir resettlement support;
(2) Research reports: social integration assessment studies published by universities and think tanks;
(3) Media coverage: in-depth case studies of resettlers in mainstream news outlets.
Table 3. Descriptive statistics.
Table 3. Descriptive statistics.
VariablesMeasurement IndicatorsSFLOverall MeanStandard Deviation
OEOE10.883.9011.22
OE20.78
OE30.779
SESE10.9214.1271.064
SE20.805
SE30.768
SNMSNM10.9424.0971.113
SNM20.793
SNM30.78
STST10.9173.8191.131
ST20.776
ST30.772
SNKSNK10.9444.1461.067
SNK20.771
SNK30.77
SISI10.9243.9351.195
SI20.826
SI30.837
Table 4. Result of reliability and validity testing.
Table 4. Result of reliability and validity testing.
Cronbach’s αCRAVEOESESNMSTSNKSI
OE0.8760.85480.66320.814
SE0.8680.87190.69540.2570.834
SNM0.8750.87840.70820.4020.3110.842
ST0.8630.86340.67970.4510.430.3840.824
SNK0.8650.87010.69280.3850.370.4110.4290.832
SI0.9040.89760.74550.0820.1440.0040.0190.0160.863
Table 5. Overall model fit results.
Table 5. Overall model fit results.
Fitting CoefficientStatisticsOptimality Criterion ValuesFit Situation
Chi-square value175.623-
DF124-
Chi-square/DF1.416<3excellent
GFI0.964>0.9excellent
AGFI0.951>0.9excellent
RMSEA0.028<0.08excellent
CFI0.991>0.9excellent
TLI0.989>0.9excellent
Table 6. Path Analysis Results.
Table 6. Path Analysis Results.
Pathsstd.UnStd.S.E.C.R.pEstablished or Not
Self-efficacy→social integration0.1270.1450.0582.5260.012established
Outcome expectations→social integration0.1770.250.069−3.621***established
Social norms→self-efficacy0.1690.1520.0463.326***established
Social norms→outcome expectations0.1920.1410.037−3.757***established
Social trust→self-efficacy0.2940.3060.0545.714***established
Social trust→outcome expectations0.3040.2550.043−5.87***established
Social networks→self-efficacy0.220.2180.0494.416***established
Social networks→outcome expectations0.1160.0930.0392.3990.016established
Note: *** denotes significance at 0.001 levels.
Table 7. Necessity analysis.
Table 7. Necessity analysis.
Condition
Variables		Outcome VariablesCondition
Variables		Outcome Variables
ConsistencyCoverageConsistencyCoverage
OE0.7490.641~OE0.4920.750
SE0.7670.695~SE0.5140.713
SNM0.8200.669~SNM0.4290.719
ST0.7770.702~ST0.4950.689
SNK0.8680.678~SNK0.3820.702
Table 8. Analysis of causal condition configuration results.
Table 8. Analysis of causal condition configuration results.
S1S2S3S4S5
OE⬤⬤⬤⬤
SE ●⬤ ⊗
SNM ● ⊗ ⬤
ST⊗ ⬤⬤
SNK⬤⬤⬤ ⬤
Consistency0.8610.8640.8890.9090.864
Raw coverage0.3290.3590.3620.2400.349
Unique coverage0.0180.0020.0020.0120.107
Solution consistency0.834
Solution coverage0.540
Notes: ⬤ means the presence of a core condition, ⊗ denotes the absence of a core condition or the absence of a peripheral condition, ● represents the presence of a peripheral condition, and ⊗ signifies the absence of edge conditions.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Diao, L.; Chen, J.; Chen, J.; Su, Z. A Study on the Influencing Factors and Multiple Driving Paths of Social Integration of Reservoir Resettlers: An Empirical Analysis Based on SEM and fsQCA. Water 2025, 17, 1073. https://doi.org/10.3390/w17071073

AMA Style

Diao L, Chen J, Chen J, Su Z. A Study on the Influencing Factors and Multiple Driving Paths of Social Integration of Reservoir Resettlers: An Empirical Analysis Based on SEM and fsQCA. Water. 2025; 17(7):1073. https://doi.org/10.3390/w17071073

Chicago/Turabian Style

Diao, Lili, Jiachuan Chen, Jihao Chen, and Zhaoxian Su. 2025. "A Study on the Influencing Factors and Multiple Driving Paths of Social Integration of Reservoir Resettlers: An Empirical Analysis Based on SEM and fsQCA" Water 17, no. 7: 1073. https://doi.org/10.3390/w17071073

APA Style

Diao, L., Chen, J., Chen, J., & Su, Z. (2025). A Study on the Influencing Factors and Multiple Driving Paths of Social Integration of Reservoir Resettlers: An Empirical Analysis Based on SEM and fsQCA. Water, 17(7), 1073. https://doi.org/10.3390/w17071073

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop