Discrepancies between farmers' willingness and behavior: an analysis of participation in cooperative economic organizations

Abstract

Introduction:

Although most respondents express a strong willingness to join cooperatives, a substantial behavioral gap remains. Specifically, while 82.5% of surveyed farmers reported willingness to participate, only 27.4% actually engaged in cooperative activities. This highlights a significant intention–behavior discrepancy in rural cooperative participation, which warrants systematic investigation.

Methods:

Using survey data from 530 rural households in Guizhou Province, this study applies multinomial logistic regression and Interpretive Structural Modeling (ISM) to explore determinants and hierarchical structures influencing the intention–behavior gap in cooperative participation.

Results:

Regression results show that cooperative support for livelihood security and market expansion significantly reduces willingness–behavior discrepancies, while political leadership and cadre encouragement play important mediating roles. ISM analysis identifies political leadership and legal awareness as deep institutional drivers shaping behavioral realization. A paradoxical short-term effect is observed: higher legal awareness correlates with increased behavioral deviation, as informed farmers may perceive institutional risks. Over the long term, embedded within effective governance structures, legal awareness enhances confidence and supports intention–behavior alignment.

Discussion:

By integrating regression and ISM, this study extends the Theory of Planned Behavior (TPB) by incorporating institutional mechanisms, advances methodological approaches, and provides practical policy recommendations to narrow farmers' intention–behavior gaps.

1 Introduction

China's rural revitalization strategy has become a cornerstone of national policy, aiming to address the “Three Rural Issues” (agriculture, rural areas, and farmers) while advancing agricultural modernization. Within this framework, reforming the agricultural organizational system is regarded as a crucial field of institutional innovation (Song et al., 2024). Cooperative economic organizations—particularly specialized farmers‘ cooperatives—have emerged as key vehicles for integrating resources, sharing risks, and improving market access. Formed voluntarily on the basis of mutual assistance, these organizations are expected to enable the transition from fragmented smallholder farming to modern agriculture (Valentinov and Iliopoulos, 2013; Jiang, 2022). However, many cooperatives remain constrained by small scale, weak incentives, poor governance, and low profitability. These challenges not only hinder internal development but also produce a persistent mismatch between farmers' willingness to join and their actual participation (Shylendra and Veerashekharappa, 2010; Zhou, 2022; Yang et al., 2023). Such misalignments—whether expressed willingness without participation or compelled participation without genuine intent—undermine organizational sustainability and broader revitalization efforts (Stoll et al., 2015). Nevertheless, recent evidence indicates that cooperatives can enhance resilience and reduce vulnerability by improving income, food security, and social capital (Liu et al., 2023).

Empirical studies on farmers' cooperative participation can be grouped into three strands. The first focuses on willingness, highlighting factors such as cooperative awareness, access to capital and technology, and market conditions (Luo, 2020; Wang et al., 2021). The second examines actual participation, emphasizing determinants such as personal traits (e.g., age, education; Wang and Cui, 2022), household resources (Abate, 2018), and policy support (Li et al., 2021). The third explores the relationship between willingness and behavior, showing that discrepancies are shaped by subjective norms, behavioral attitudes, and perceived behavioral control (Chang et al., 2021; Li and Yin, 2021; Mitter et al., 2019). Related studies in environmental behavior (Cai et al., 2021) and product marketing (Wang and Liu, 2021) also confirm that both objective factors (e.g., household characteristics, institutional environment) and subjective factors (e.g., perceptions, attitudes) contribute to intention—behavior gaps. Cooperative participation has further been linked to sustainable agricultural practices, including farmland quality protection (Duan and Luo, 2024).

Despite these contributions, three critical gaps remain. First, while intention—behavior discrepancies are widely recognized, few studies have systematically measured their magnitude or typologies. Most research simply asks whether a gap exists, with limited attention to heterogeneity across gender, age, or education (Lin and Yu, 2023). Second, analyses often emphasize either individual traits (Dowling et al., 2020) or external environments (Dudley and Xie, 2020), with little integration of psychological and institutional perspectives. The Theory of Planned Behavior (TPB), which provides a robust framework for linking attitudes, norms, perceived behavioral control, and behavior, has been underutilized in cooperative research. Third, institutional dimensions such as political leadership and legal awareness are increasingly noted in policy discourse, yet their structural roles in shaping intention—behavior alignment remain underexplored. Meanwhile, innovative forms such as agro ecological cooperative supermarkets are emerging as mechanisms for sustainable food distribution (Sanz-Cañada et al., 2024), further underscoring the need to connect cooperative governance with sustainable food systems.

To address these gaps, this study employs TPB as its theoretical foundation to analyze discrepancies between farmers' willingness and actual participation in cooperative organizations. Using survey data from five hundred and thirty rural households in Guizhou Province, a multinomial logistic regression model quantifies the magnitude and determinants of intention–behavior deviations. Interpretive Structural Modeling (ISM) is then applied to reveal hierarchical relationships among influencing factors.

This study makes three key contributions. First, it extends TPB to the cooperative context, demonstrating how farmers' cognitive processes interact with institutional structures to shape participation. Second, it integrates multinomial logistic regression with ISM, offering a methodological framework that captures both direct determinants and structural pathways of intention—behavior discrepancies. Third, it provides actionable policy insights for narrowing participation gaps, strengthening cooperative governance, and promoting sustainable farmer engagement—contributing to more resilient and sustainable rural food systems.

2 Theoretical analysis and research hypothesis

The Theory of Planned Behavior (TPB) provides a robust framework for explaining why farmers‘ willingness to join cooperatives often fails to translate into actual behavior (Ajzen, 1991; Yadav and Pathak, 2017; Archila-Godinez et al., 2022). While willingness is essential for initiating action, behavioral realization is further shaped by attitudes, subjective norms, and perceived behavioral control, as well as the strength of willingness itself (Ruan, 2019; Josef, 2013). Despite its explanatory power, TPB has been underutilized in studies of cooperative participation, leaving the mechanisms of intention—behavior discrepancies insufficiently explored. Applying this framework to rural cooperatives allows us to identify how psychological and institutional factors interact to shape farmers' decisions, thereby offering new insights into governance and sustainable food systems.

2.1 Behavioral attitude

Behavioral attitude reflects farmers' evaluation of whether joining cooperatives improves their livelihoods, strengthens market opportunities, or enhances income stability. Empirical evidence from China shows that cooperative membership is associated with higher household income and welfare (Ito et al., 2012; Bachke et al., 2019), and with safer and more standardized production practices (Ji et al., 2019). These benefits reinforce the importance of positive attitudes in narrowing willingness—behavior gaps.

• H1: Farmers with more positive behavioral attitudes are associated with a higher likelihood of transforming willingness into cooperative participation, thereby reducing behavioral deviation.

2.2 Subjective norm

Subjective norms capture the influence of peers, kinship ties, and institutional actors on farmers' decisions. In rural China, contractual and institutional arrangements between cooperatives and buyers shape trust and obligations, thereby affecting participation behavior (Jia et al., 2011). Beyond China, evidence shows that leadership and collective action arrangements strongly influence participation in agri-environmental schemes (Sander et al., 2024). However, the effectiveness of these social pressures depends heavily on governance quality, as poorly designed interventions can undermine trust and reduce engagement.

• H2: Stronger subjective norms are associated with a higher likelihood that willingness will be converted into cooperative participation, while weak or inconsistent norms exacerbate behavioral deviation.

2.3 Perceived behavioral control

Perceived behavioral control refers to farmers' judgment of whether they have sufficient resources and face manageable constraints when considering participation. Access to extension services and cooperative membership is associated with reduced perceived constraints and greater technology adoption, thereby enhancing household welfare (Wossen et al., 2017). Similarly, meta-analytic evidence indicates that resource availability—such as credit and assets—plays a decisive role in enabling smallholder entry into high-value markets (Ola et al., 2020). These findings suggest that perceived control arises from both internal household capacity and external institutional conditions.

• H3: Higher perceived behavioral control is associated with a higher likelihood of willingness being translated into cooperative participation, whereas resource constraints and institutional barriers are associated with greater intention–behavior discrepancies.

2.4 Critical synthesis and research implications

Although attitudes, norms, and perceived control are central to TPB, empirical findings remain inconsistent. First, while most studies agree that economic benefits are key drivers of positive attitudes (Ito et al., 2012; Ji et al., 2019; Bachke et al., 2019), scholars debate whether short-term income gains (Verhaegen and Van Huylenbroeck, 2001) or long-term resilience and risk-sharing benefits (Abigaba et al., 2022) are more decisive. Second, subjective norms are widely recognized as important (Jia et al., 2011; Sander et al., 2024), yet their durability is contested: some research emphasizes lasting leadership effects, while others point to fragile trust when interventions are poorly designed. Third, for perceived behavioral control, studies diverge on whether intra-household resources (Liu, 2023) or external barriers such as market and institutional constraints (Barrett et al., 2012; Wossen et al., 2017) dominate.

By synthesizing these debates, this study positions behavioral attitude, subjective norms, and perceived behavioral control as core explanatory variables, while incorporating political leadership and legal awareness as contextual institutional drivers. Crucially, willingness is conceptualized as the mediating pathway through which these determinants influence cooperative participation, while perceived behavioral control may also exert a direct effect on behavior. This dual focus extends TPB by embedding it within multi-level governance structures, thereby enhancing its explanatory power.

Ultimately, clarifying the mechanisms of intention—behavior gaps provides pathways to strengthen cooperative governance, sustain farmer engagement, and foster the resilience of rural food systems (Gaitán-Cremaschi et al., 2022; Schoneveld, 2022; Soriano et al., 2023). As illustrated in Figure 1, willingness mediates the effects of attitudes, norms, and perceived control on cooperative behavior, while political leadership and legal awareness act as contextual drivers shaping these pathways. In the figure, solid arrows represent direct effects, dashed arrows denote contextual influences, and willingness serves as the key mediator within the TPB framework.

Figure 1

3 Research design

3.1 Sample selection and data source

This study draws on a questionnaire survey conducted in rural areas of Guizhou Province, China. Guizhou was selected for two reasons. First, local governments promoted grassroots cooperatives relatively early, and political leadership has played an important role in their development (Yu and Zhang, 2021). Second, Guizhou's agricultural value-added growth has ranked among the highest in China, reflecting strong momentum in its agricultural economy. By June 2023, the province had sixty four thousand three hundred registered farmers' cooperatives with a cumulative capital of CNY 0.15 trillion, representing year-on-year increases of 0.11% in number and 16.89% in total investment. These conditions provide a favorable institutional and economic foundation for cooperative development.

To ensure representativeness, the questionnaire was adapted to regional variations in economic development and rural characteristics (Zang and Xu, 2023). A total of five hundred and fifty questionnaires were randomly distributed across all prefecture-level cities. After excluding invalid responses—defined as those with more than 20% missing data, logical inconsistencies, or patterned answers (e.g., uniform responses)—five hundred and thirty valid samples were retained, yielding an effective response rate of 96.4%.

The survey instrument comprised five sections: (1) farmers' willingness and actual participation in cooperatives, (2) demographic characteristics, (3) attitudes toward cooperatives, (4) subjective norms, and (5) perceived behavioral control. Reliability and validity analyses confirmed the robustness of the instrument. Cronbach's α was 0.739, indicating acceptable internal consistency. The KMO measure was 0.825, and Bartlett's test yielded χ² = 7653.76 (p < 0.001, df to be supplemented), confirming the suitability of the data for factor analysis. All AVE values exceeded 0.5, indicating good discriminant validity. CFA results showed excellent model fit (RMSEA = 0.023, CFI = 0.939, TLI = 0.928), surpassing the conventional thresholds of 0.9.

Potential nonresponse bias was minimized by the high response rate. An early–late respondent test (Armstrong and Overton, 1977) revealed no significant demographic differences (age, gender, education). Missing values accounted for less than 3%; mean imputation was applied (Little and Rubin, 2019), and robustness checks using list wise deletion yielded consistent results.

Table 1 reports the sample characteristics: 67.5% of respondents were male, 53.6% engaged in non-agricultural employment, 34% were members of the Communist Party of China, and 47.2% were aged 36–50. Moreover, 40.4% had completed junior high school, and 76.6% reported good health.

3.2 Variable selection and descriptive statistics

• (1) Dependent variables

The primary focus of this paper is the deviation between farmers‘ willingness and behavior to participate in cooperative economic organizations, termed willingness—behavior deviation. Drawing on existing research on farmers' participation behavior and willingness (e.g., Charatsari et al., 2020; Wei and Ruan, 2022; Cammarata et al., 2024), and aligning with the research objectives and questionnaire structure, we operationalized this construct through a three-stage measurement protocol. First, we aggregated farmers‘ willingness scores from five validated Likert-scale items in our questionnaire (Cronbach's α = 0.739). Second, we implemented a contingency matrix analysis contrasting dichotomized willingness and verified participation status. Willingness scores higher than the mean were coded as 1, and scores at or below the mean as 0, generating four divergence categories: (1) consistent match (willingness = 1, behavior = 1); (2) behavioral deviation (willingness = 1, behavior = 0); (3) willingness deviation (willingness = 0, behavior = 1); and (4) inconsistent match (willingness = 0, behavior = 0). This mean-split dichotomization approach has been widely applied in studies of rural households and cooperatives in China, ensuring comparability with prior research and offering a straightforward way to distinguish groups with higher and lower willingness. Nevertheless, we acknowledge that dichotomization may oversimplify the heterogeneity of farmers' attitudes and behaviors. To reflect this, we have explicitly noted the limitation in the revised manuscript and suggested that future research could adopt more refined approaches—such as tertiles, quartiles, continuous measures, or latent class analysis—to capture nuanced variations in willingness. Moreover, while our robustness checks in Section 4.2 focused on alternative model specifications (e.g., multinomial probit, binary logistic regressions), the consistent results across models indirectly strengthen confidence in the reliability of our findings.

• (2) Independent variables

Based on the Theory of Planned Behavior, drawing on existing research, this paper examines three primary types of explanatory variables: (1) Behavioral attitudes, assessed across three dimensions—farmers' evaluations of the benefits derived from cooperative economic organizations in ensuring basic livelihood (Lv et al., 2022), increasing income and expanding market channels (Shen and Chen, 2020). (2) Subjective norms, are represented by three aspects—Political Leadership Bodies promoting farmers' welfare improvement and wealth accumulation (Ma and Li, 2023), and village committee cadres encouraging farmers' participation in cooperative economic organizations. (3) Perceived behavioral control, encompassing three dimensions—understanding of laws and policies (Hansson et al., 2012), ability to manage market risks (Chen and Zhang, 2022), and availability of labor resources (Lv et al., 2022). These variables are measured using a 5-point Likert scale, where higher values indicate stronger perceptions or influences.

• (3) Control variables

Individual characteristics of farm households will be included as control variables (Larcher et al., 2019), encompassing gender, age, education level, party membership status, health condition, and involvement in migrant labor. To mitigate potential biases in farmers' behavior and willingness to participate in cooperative economies, this study assigns values to the control variables based on data obtained from the survey. Detailed variable settings and specific descriptions are provided in the Appendix.

3.3 Model setting

• (1) Multivariate logistic model

In this paper, the willingness and behavior deviation serves as a dependent variable, denoted as Y, which is a multi categorical variable ranging from 0 to 3. Drawing from the Theory of Planned Behavior, this paper incorporates nine specific metric variables that capture behavioral attitudes, subjective norms, and perceived behavioral control as independent variables. These are also included in the model as controls for farmers' individual characteristics. Given the multi-categorical nature of willingness and behavior deviation, a multivariate logistic model is employed for parameter estimation. To address potential heteroskedasticity issues, robust estimation methods are applied throughout parameter estimation. The specific model formulation is outlined as follows:

Where α is the intercept, β and η are the estimated parameters of the independent and control variables, respectively. X are independent variables, which include three main categories, i.e., farmers' evaluations of the benefits derived from cooperative economic organizations in ensuring basic livelihood (life_support), increasing income (increase_income), and expanding market channels (expand_market); Political Leadership Body promoting farmers' welfare improvement (life_standard) and wealth accumulation (rich), and village committee cadres encouraging farmers' participation in cooperative economic organizations (encouragement); farmers' perceptions of law(law), risk(risk), and labor(people). Z is the control variable, which mainly includes gender (gender), age (age), education level (edu), health condition(health), whether they are Party members (Party), and out-of-home labor (home_work) of the farm household. i and j are the number of explanatory and control variables,. μ is the error term. The probability of generating behavioral and willingness deviation in farmers' participation in cooperative economic organizations is:

Equation 2, where e is the base of the natural logarithm, is estimated as:

• (2) ISM model

The Interpretive Structural Model (ISM) is a framework utilized to analyze internal structures and hierarchical relationships within complex systems, commonly applied to explore directed correlations and hierarchies among factors in economic and social contexts (Xian et al., 2024), among determinants of willingness-behavior divergence, following rigorous comparison with alternative system analysis methods (DEMATEL, ANP). It is crucial to delineate these hierarchical relationships among factors influencing bias in willingness to act, as this helps pinpoint key determinants of such bias. The Interpretive Structural Model (ISM) is a framework utilized to analyze internal structures and hierarchical relationships within complex systems, commonly applied to explore directed correlations and hierarchies among factors in economic and social contexts (Xian et al., 2024). It is crucial to delineate these hierarchical relationships among factors influencing bias in willingness to act, as this helps pinpoint key determinants of such bias. Consequently, this paper employs the ISM model to thoroughly investigate the hierarchical relationships among factors significantly impacting willingness and behavior deviation.

Our implementation protocol comprised six methodologically grounded stages: (1) Expert-guided identification of contextual determinants from field interviews and literature synthesis. (2) Structured pairwise comparison using VAXO framework (Valence-Antecedent-X-Outcome). (3) Reachability matrix construction with consistency checks. (4) Hierarchical partitioning via iterative conical matrix decomposition. (5) Cross-impact matrix multiplication (MICMAC) for driver/dependence classification. (6) Dynamical validation through focus group triangulation

Assuming that there are k factors that have a significant impact on the willingness and behavior deviations, S₀is used to denote the degree of willingness and behavior of farmers to participate in the cooperative economic organizations, and is used to denote these factors, and according to Equation (4), the constituents of each of the adjacency matrices A can be determined.

The reachability matrix M of the factors influencing the willingness and behavior deviation can be expressed as:

Where,2 ≤ λ ≤ k, I denote the unit matrix of willingness and behavioral bias influencing factors and the matrix is implemented by Boolean algorithm. According to Equation (5) the influencing factors included in each stratum can be determined:

Where P(S_i) denotes the set of all factors that can be reached from influencing factor S_i as a starting point.

Calculations based on equations (4), (5), (6) give the influence factors contained in each level. Connecting each level of influencing factors of willingness to act bias with directed edges gives a hierarchical structure diagram.

4 Empirical analysis

4.1 Measurement results of willingness and behavior deviation

This study examines whether significant differences exist between farmers' willingness and actual participation in cooperative economic organizations across individual characteristics. Overall, the alignment between stated willingness and actual behavior is relatively low. Notably, only farmers with no formal education reach a consistent match rate above 30% (33.3%). For all other subgroups, alignment ranges between 12.9% (farmers with a bachelor's degree or above) and 25.0% (farmers aged 66 and above), indicating generally low consistency (see Table 2 for detailed percentages). The discrepancy is particularly pronounced among farmers under 20 years old, whose willingness–behavior deviation exceeds 40%.

Subgroup comparisons reveal further heterogeneity. Females exhibit lower consistency than males, though the difference is not statistically significant (χ² = 5.477, p > 0.1; χ² = 33.182, p < 0.001). Clear age-related differences emerge (χ² = 33.849, p < 0.001; χ² = 78.141, p < 0.001): alignment is lowest among farmers aged 21–35, while those under 20 show the highest inconsistency. In terms of education, farmers with high school or vocational training report the highest consistency, whereas those with a bachelor's degree or above show the most inconsistency—a statistically significant gap (χ² = 25.251, p < 0.05; χ² = 78.090, p < 0.001). Health also matters: farmers in good health demonstrate the highest consistency rates, with significant group differences (χ² = 21.699, p < 0.001; χ² = 120.022, p < 0.001). Moreover, non-migrant farmers display greater alignment than those engaged in off-farm work, and the difference is significant (χ² = 14.581, p < 0.05; χ² = 24.713, p < 0.001). Finally, political affiliation plays a role, as Party members report significantly higher consistency between willingness and behavior compared with non-members (χ² = 3.556, p > 0.1; χ² = 16.494, p < 0.001).

These results highlight the multifaceted factors contributing to willingness—behavior discrepancies and carry important theoretical and practical implications. Theoretically, the study underscores the intricate interplay between individual characteristics and behavioral outcomes, offering new insights into the participation dynamics of cooperative economic organizations. Practically, the findings inform the development of targeted interventions aimed at reducing these discrepancies, particularly among subgroups—such as younger, less-educated farmers or those engaged in off-farm employment—that exhibit larger gaps. Tailored strategies should address the unique barriers and motivations of these groups. Additionally, improving farmers' health and promoting political engagement appear to be critical pathways to enhance alignment between willingness and behavior. By effectively narrowing this gap, cooperative economic organizations can bolster participation rates and support sustainable rural development.

A comprehensive analysis reveals significant differences in farmers' willingness and actual behavior to participate in cooperative economic organizations. Firstly, there is a notable deviation between farmers' expressed willingness and their behaviors, which is primarily characterized by a tendency to overstate willingness. This discrepancy often results in strong declarations of intent but weak follow-through, with farmers expressing a desire to participate but failing to take concrete action. Additionally, the development of the cooperative economy is influenced by various external factors, meaning that even when farmers demonstrate strong willingness, practical constraints may hinder behavioral change. As a result, while farmers may state their willingness to engage in cooperative economic organizations, their actual participation often does not align, reflecting a significant gap between intention and behavior. Secondly, another prominent discrepancy is observed in the mismatch between farmers' expressed willingness and their actual involvement in these organizations. This phenomenon is partly driven by speculative policy-making and the formation of “shell societies,” where organizations are superficially established without genuine participation (Zhang and Run, 2020). Furthermore, many cooperative economic organizations tie participation to performance evaluations, leading to forced involvement even when farmers lack sincere willingness. This deviation not only reduces the operational efficiency of professional cooperative economic organizations but also undermines their long-term sustainability and healthy development. Finally, a small subset of farmers, such as those with no formal education, exhibit little to no deviation between their willingness and behavior, indicating that for certain groups, alignment between intention and action is more likely.

Importantly, these descriptive findings also reveal clear subgroup heterogeneity across gender, age, education, health, employment status, and political affiliation. While this study does not explicitly model interaction terms or conduct subgroup-specific regressions, the observed variations underscore the need for future analyses to further examine heterogeneous effects across farmer groups.

4.2 Measurement results of the influencing factors of willingness and behavior deviation

In this paper, the multinomial logistic model is used for parameter estimation across Models 1–3 to analyze the factors influencing the likelihood of behavioral deviation, willingness deviation, consistent match, and inconsistent match. The results of parameter estimation for each variable are presented in Table 3. The overall significance of the model is confirmed through tests on the regression coefficients of the independent variables and their robust standard errors. To further interpret substantive effects, the marginal effects of significant independent variables are reported in Table 4.

• (1) The influence of behavioral attitudes on willingness and behavior deviation

Cooperative economic organizations play an important role in reducing the gap between farmers‘ willingness and behavior to participate, especially by improving basic livelihoods and expanding agricultural product sales channels. The marginal effect analysis shows that each unit increase in cooperatives supporting farmers' livelihoods reduces the likelihood of willingness deviation by 4.4%. This indicates that stronger livelihood support helps narrow the gap between expressed willingness and actual participation. Similarly, each unit increase in cooperatives‘ efforts to expand market sales channels raises the probability of consistent alignment by 5.4%, while reducing behavioral deviation by 4.9% and willingness deviation by 6.6%. These findings demonstrate that cooperatives' market linkage functions not only increase participation but also reduce mismatches between willingness and behavior.

Taken together, these results suggest that cooperatives influence farmers‘ perceptions and decisions, beyond serving as economic facilitators. They help build trust, enhance farmers' sense of security, and strengthen the translation of willingness into actual participation.

• (2) The influence of subjective norms on willingness and behavior deviation

Party organizations in enhancing farmers' living standards, along with encouragement by village committee cadres for farmers to engage in cooperative economic organizations, significantly influences farmers' willingness and behavioral to participate in these organizations. The marginal effect analysis reveals that with each increment of support from Party organizations, the likelihood of consistent match between farmers' willingness and behavior increases by 6.1%. Conversely, for each level of encouragement provided by village committee cadres, there is a 6.5% increase in the probability of behavioral deviation, a 12.5% decrease in the probability of willingness deviation, and an 8.1% increase in the probability of inconsistent match.

The analysis uncovers a dual institutional paradox in rural governance interventions: while political leadership bodies enhance intention-behavior alignment (6.1%, p < 0.05) through institutional trust mechanisms, village cadres' efforts yield contradictory effects—reducing willingness divergence by 12.5% yet increasing behavioral deviation (6.5%) and mismatch likelihood (8.1%, p < 0.01). This exemplifies a compounded agency dilemma where cadres' dual roles as state agents and community members trigger three interlocking mechanisms: performative compliance through social conformity pressure, cognitive dissonance from mandated participation and moral hazard amplification in complex networks. The stronger normative effect on willingness (−12.5%) versus weaker behavioral impacts (+6.5%) challenges principal-agent models' material incentive focus, revealing grassroots governance as both a policy conduit and paradox generator-where macro-level institutionalization success coexists with micro-level implementation contradictions.

This paradoxical finding can be explained by the dual nature of cadre “encouragement” in rural governance. On the one hand, village cadres‘ persuasion often carries implicit administrative pressure or is linked to performance assessments, which may prompt farmers to express willingness even when their actual capacity or motivation to participate is limited. This results in a form of performative compliance: farmers indicate higher willingness but fail to follow through with concrete action, thereby increasing behavioral deviation. On the other hand, such encouragement may be perceived by farmers as “soft coercion,” generating resistance or skepticism toward cooperative participation. While normative influence reduces willingness deviation by reinforcing farmers' stated intentions, the lack of corresponding economic benefits, trust, or resource support undermines behavioral realization. Consequently, encouragement without substantive institutional or material backing paradoxically widens the gap between stated willingness and actual behavior, highlighting the tension between symbolic mobilization and genuine participation in grassroots governance.

• (3) The Influence of perceived behavioral control on willingness and behavior deviation

The extent of farmers' comprehension of legal policies and their capacity to manage market risks significantly correlates with their willingness and behavioral deviations to participate in cooperative economic organizations. According to the marginal effect analysis, with each level of increased understanding of legal policies among farmers, there is a 7.1% rise in the probability of behavioral deviation and a 5.5% decrease in the likelihood of inconsistent match. This paradoxical short-term effect suggests that farmers with greater legal literacy may become more aware of institutional loopholes, procedural burdens, or enforcement gaps, which raises hesitation and increases the risk of behavioral deviation, particularly in cases where willingness is not matched by action. At the same time, ISM results highlight that legal awareness operates as a deep structural driver: when combined with effective governance support, stronger legal understanding can gradually enhance trust in institutions and facilitate long-term intention—behavior alignment. With each increment in farmers' tolerance for market risk, there is a 4.4% increase in the likelihood of consistent match. Additionally, there is a 2.8% decrease in the probability of behavioral deviation. These findings indicate that enhancing farmers' tolerance for market risk contributes to mitigating the likelihood of deviation between expressed willingness and actual behavior.

The findings unveil a counterintuitive cognitive-institutional paradox in participatory decision-making: enhanced legal policy literacy, while theoretically posited to reduce divergence, actually increases behavioral deviation likelihood by 7.1% per comprehension level alongside 5.5% reduced mismatch probability. This suggests a hyper-awareness trap where policy-savvy farmers cognitively decouple normative aspirations from practical constraints through institutional skepticism - a mechanism aligning with (Akerlof 2020) concept of “frictional institutionalism.” Conversely, market risk tolerance demonstrates behavioral anchoring effects: each risk-acceptance increment yields 4.4% greater intention-behavior congruence and 2.8% lower behavioral deviation operationalizing (Slovic 2016) risk-as-feelings paradigm through a cognitive buffer mechanism that mediates affective responses to uncertainty. The inverse effects of knowledge (+7.1% deviation) versus risk tolerance (−2.8% deviation) challenge rational choice assumptions, revealing two distinct pathways - calculative hesitation versus affective resilience-that reconceptualize behavioral economics frameworks for institutional participation.

These results are consistent with the TPB framework, as they indicate that willingness mediates the effects of behavioral attitudes, subjective norms, and perceived behavioral control on actual participation. This mediating role of willingness helps explain why farmers may express high willingness but show divergent behaviors in practice (see Figure 1).“

• (4) The influence of individual characteristics of farmers on willingness and behavior deviation

Farmers‘ age, health condition, migrant labor situation, and education level significantly influence the deviation between willingness and behavior. Specifically, increasing age tends to reduce the likelihood of deviation of farmers' willingness and behavior. However, higher levels of education increase the probability of inconsistent match by 3%. Additionally, when farmers engage in migrant labor, the probability of consistent match decreases by 6.8%, while the likelihood of willingness deviation increases by 6.9%.

• (5) Robustness Test

To verify the stability of our results, we conducted additional robustness checks. As shown in Table 5, replacing the baseline multinomial logistic model with a Multinomial Probit specification yields consistent findings, confirming that our results are not sensitive to estimation method. Furthermore, binary logistic regressions were performed to separately compare each deviation type with the consistent match group. The outcomes, presented in Table 6, show similar significance and direction of effects. Together, these checks demonstrate that the empirical findings are robust across alternative model specifications.

To further verify the robustness of the model, the willingness scores were dichotomized via a median split: observations with scores above the median were coded as 1 (strong willingness), and those equal to or below the median were coded as 0 (weak willingness). The regression results reported in Table 7 show that the signs and levels of significance of most key variables remain consistent with the baseline model. In particular, life_standard, law, people, edu, and Party remain statistically significant with expected directions, while variables such as risk and age continue to be insignificant. The overall model fit is satisfactory (Wald chi2 = 167.29, pseudo R² = 0.418), indicating that the empirical findings are robust to this alternative operationalization.

4.3 Measurement results of the ISM model

This study applies the ISM model to further analyze the logical structure and hierarchical relationships among factors significantly influencing farmers‘ willingness–behavior discrepancies. Based on the results of the multinomial logistic regression, six key determinants were identified, denoted as S_i(I = 1,2,…,6). Specifically, these include: (1) cooperatives guaranteeing the basic livelihood of farmers; (2) cooperatives expanding agricultural product market channels; (3) political leadership bodies actively helping farmers improve their living standards; (4) village cadres encouraging farmers to participate in cooperatives; (5) farmers' level of understanding of relevant laws and policies; and (6) farmers‘ ability to withstand market risks. Drawing upon the theoretical framework and expert consultations, the logical relationships among these six determinants and farmers' willingness–behavior discrepancies in cooperative participation were established, as illustrated in Figure 2.

Figure 2

Through Figure 1, equations (4) and (5), utilizing MATLAB enables the derivation of the adjacency matrix A and reachability matrix M among the influencing factors. Subsequently, this allows for determining the hierarchical levels of each influencing factor. Starting with the highest level, denoted as the levelL₁ = {S₀}, subsequent levels„ are determined sequentially using a method to rank the factors contained within the matrixL₁, L₂, L₃, L₄. The results are illustrated in Figure 3.

Figure 3

The analysis shows that the factors can be grouped into three levels, with each level providing the conditions for the one above it. At the first level are direct factors, including cooperatives guaranteeing farmers‘ basic livelihoods and village cadres encouraging participation. The second level consists of intermediate factors, namely the ability to withstand market risks and the expansion of agricultural product sales channels. The third level encompasses deep-rooted structural drivers, specifically the level of legal and policy awareness and the support of political leadership bodies. Together, these levels form a hierarchical structure influencing farmers' intention–behavior discrepancies in cooperative participation, as illustrated in Figure 4.

Figure 4

Based on these results, two main explanatory pathways emerge. The first is related to income security and wealth generation. Farmers‘ primary motivation for participation is to reduce risks, expand markets, and increase income. When cooperatives succeed in raising incomes, willingness–behavior discrepancies are reduced. In this process, political leadership plays a crucial role by strengthening policy interpretation, organizing propaganda, and providing institutional support to stabilize farmers' willingness. The second pathway concerns social status enhancement. Cooperative participation represents both a form of self-governance and a means of gaining collective recognition. When farmers‘ contributions are acknowledged, their willingness–behavior discrepancies are reduced. Effective leadership and clear policy guidance enhance belonging and motivation, thereby reducing behavioral deviations. Ultimately, the underlying mechanism for reducing farmers' willingness–behavior deviation lies in the Party's leadership, which leverages the exemplary role of cadres, disseminates key information, and addresses practical concerns, thereby ensuring effective policy implementation.

Importantly, integrating regression and ISM results provides a coherent interpretation of the findings. Regression analysis identified livelihood support, market expansion, cadre encouragement, legal awareness, and risk tolerance as significant determinants of willingness—behavior gaps. ISM clarifies their hierarchical interdependencies: legal awareness and political leadership act as deep structural drivers, conditioning intermediate factors such as market expansion and risk tolerance, which in turn shape direct factors like livelihood support and cadre encouragement. This layered perspective demonstrates that regression-identified determinants do not operate in isolation but function within a hierarchical system of causation. The integration of regression and ISM results thus enhances the explanatory depth and coherence of the empirical findings.

5 Implications and conclusion

5.1 Summary of key empirical findings

This study classifies farmers' participation in cooperative economic organizations into four behavioral types: consistent match, behavioral deviation, willingness deviation, and inconsistent match. Based on a multinomial logistic regression using survey data from five hundred and thirty rural households in Guizhou Province, the results reveal that behavioral attitudes (e.g., livelihood security, market access), subjective norms (e.g., political leadership, village cadres), and perceived behavioral control (e.g., legal awareness, risk tolerance) exhibit differentiated effects across deviation categories. Notably, cooperative support for expanding product markets significantly reduces both behavioral and willingness deviations. Party membership and good health status are positively associated with intention—behavior alignment. The ISM analysis further uncovers a three-tiered structure of influence: foundational conditions (political leadership, legal understanding), intermediate enablers (market access, risk tolerance), and proximal drivers (cadre mobilization, livelihood support).

5.2 Theoretical contributions: a multi-layered behavioral framework

This study extends the Theory of Planned Behavior (TPB) in two interrelated ways. First, it operationalizes a fourfold typology of intention—behavior gaps, moving beyond traditional binary classifications to allow for a more nuanced analysis of heterogeneous behavioral patterns (Li et al., 2021; Zhang et al., 2013). This typology illustrates the multiple ways in which farmers' stated willingness may either align with or diverge from actual participation, thereby enriching TPB's capacity to capture discrepancies.

Second, the study integrates TPB with a hierarchical Interpretive Structural Modeling (ISM) framework to explain how macro-institutional structures—such as party-led governance and legal frameworks—translate into individual behavioral outcomes. For instance, empirical evidence from our analysis shows that political leadership and legal awareness, as deep structural drivers, shape farmers' risk perceptions and resource allocation, which in turn indirectly strengthen the influence of cadre encouragement on cooperative participation. This example demonstrates how macro-level governance structures condition micro-level behavioral realization, highlighting the mediating role of governance actors, particularly village cadres, in facilitating the conversion of willingness into actual participation (Wang and Deng, 2024; Jiang, 2022).

Together, these contributions embed TPB within a multi-level institutional context, extending its explanatory scope. Moreover, the combined TPB—ISM framework provides a replicable analytical tool for examining intention—behavior inconsistencies in other forms of rural collective action, such as farmers' associations, agri-environmental schemes, or community-based land governance. By situating behavioral dynamics within broader governance systems, this framework also offers transferable insights for fostering sustainable participation and strengthening the resilience of rural food systems.

5.3 Policy implications: institutional pathways for behavioral alignment

Building on the TPB—ISM framework, this study proposes four institutional pathways to bridge the intention—behavior gap in cooperative participation. To provide more practical guidance, these pathways are organized by priority, discuss feasibility issues, and highlight concrete implementation steps.

• (1) Enhanced Market Coordination (short-term priority). Expanding cooperatives‘ role in improving market access should be the first priority. Mechanisms such as joint risk-sharing schemes between cooperatives and enterprises, collective marketing platforms, and preferential procurement channels can directly reduce farmers' market uncertainty and behavioral inertia (Deng et al., 2020; Liang et al., 2024). These interventions are feasible in the short term, as they build on existing market linkages.

• (2) Reconfigured Incentive Systems (short-term to medium-term). Transparent benefit-sharing mechanisms and symbolic incentives (e.g., public recognition, trust-building activities) are critical to sustaining farmers' intrinsic motivation. Implementation should focus on context-specific models, such as dividend allocation linked to contribution levels, or village-level recognition schemes. Resource constraints may limit uniform application, but pilot programs can help adapt incentives to local realities.

• (3) Adaptive Governance Design (medium-term). Replacing rigid, top-down assessments with participatory, demand-responsive governance models enhances both perceived behavioral control and institutional legitimacy. Feasibility depends on building trust between cadres and members. Examples include participatory budgeting in cooperative operations or member councils with decision-making power.

• (4) Capacity-Building Platforms (long-term). Strengthening cooperatives as hubs for technical training, policy communication, and digital integration addresses deeper structural barriers. Digital platforms for cooperative training and e-governance tools can gradually improve farmers' skills and access to resources, though sustained investment and institutional support are required.

Taken together, these pathways offer actionable entry points for rural policy interventions. By sequencing priorities, acknowledging resource and adaptation constraints, and providing concrete implementation strategies, this framework helps policymakers design more effective and context-sensitive measures to align farmers' stated willingness with actual cooperative participation (Li, 2023).

5.4 Concluding remarks

Farmers‘ participation in cooperatives is not an isolated decision but a process embedded in the interaction between individual cognition and institutional context. This study identifies two dominant behavioral logics—economic utility and symbolic identity—that jointly shape farmers' participation choices. The integrated TPB–ISM framework demonstrates how these motivations interact with multi-layered institutional mechanisms to either facilitate or constrain behavioral transformation. By bridging individual intentions with broader governance structures, this framework provides a transferable model for diagnosing intention—behavior discrepancies in rural collective action and contributes to sustainable food system governance.

5.5 Limitations and future research

This study has several limitations that constrain interpretation and suggest directions for future research.

• (1) Data and research design.

The analysis is restricted to Guizhou Province, limiting external validity; effects may differ in provinces with stronger institutional support or weaker market environments. In addition, reliance on cross-sectional data prevents identification of temporal dynamics in willingness—behavior relationships and constrains causal inference. Future research should employ multi-province or national datasets and adopt longitudinal or quasi-experimental designs to better capture causal mechanisms and behavioral evolution.

• (2) Measurement and model specification.

Informal social structures—such as kinship ties and village culture—were not systematically measured, which may confound the role of subjective norms. The regression models emphasized main effects and did not test interaction terms or subgroup-specific analyses, potentially overlooking heterogeneity across gender, age, education, health, and migration status. Robustness checks were limited to dichotomization thresholds and multicollinearity tests, leaving alternative specifications and omitted-variable bias underexplored. Future studies should incorporate indicators of social capital, include interaction effects and subgroup models, and expand robustness checks to reinforce reliability.

• (3) Potential biases and theoretical implications.

Willingness and participation were measured through self-reports, which are susceptible to social desirability bias and may inflate willingness or overstate alignment rates. Although anonymity and partial record cross-checks mitigated this risk, stronger validation requires behavioral measures, administrative records, or indirect questioning techniques. Moreover, unobserved household characteristics—such as entrepreneurial orientation or cooperative experience—may jointly shape TPB constructs and participation, raising endogeneity concerns. This highlights a theoretical implication: existing designs may overestimate the mediating role of willingness while underestimating the influence of informal norms or structural constraints. Addressing this requires instrumental-variable methods, propensity-score approaches, or panel models.

In sum, addressing these limitations through multi-site panel surveys, extended robustness tests, and qualitative triangulation (e.g., ethnographic fieldwork, participatory rural appraisal) will not only enhance methodological rigor but also situate TPB analysis within broader governance and cultural contexts. Doing so will improve the explanatory scope of behavioral models and strengthen the policy relevance of cooperative research for advancing sustainable participation and resilience in rural food systems.

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