An Analysis of Relationship Quality and Loyalty Between Farmers and Agribusiness Companies in the Rice Industry: Using Multi-Group Analysis

Abstract

Rice is an important agricultural product in Vietnam; however, the rice industry faces several challenges, particularly weak linkages between farmers and enterprises. The Vietnamese government has introduced the Large Field Model (LFM) program to improve cooperation between farmers and agribusiness companies. Despite these efforts, its implementation remains limited, and contract violations are common. This study investigated the factors affecting relationship quality (RQ) and loyalty (LO) between farmers and agricultural companies in the LFM, focusing on comparing cooperative and non-cooperative participating farmers. Using the Partial Least Squares–Multi-Group Analysis (PLS-MGA) method, this study assessed the different effects of factors such as price satisfaction (PS), payment terms (PTs), and support policies (SPs) on RQ and LO among two groups. The findings indicated that RQ has a stronger influence on LO in farmers with cooperative participation than those who exhibit non-cooperative participation. PS was the most significant factor influencing RQ in both groups, whereas PT was influential only for cooperative participating farmers. SP did not significantly affect RQ in either group. Finally, agricultural companies and policymakers should strengthen partnership management in agricultural supply chains by addressing gaps in support policies, developing appropriate pricing strategies, being flexible in negotiating payment terms, and improving the legal framework related to contract enforcement.

1. Introduction

In Vietnam, rice cultivation is a long-standing and important industry, sustaining the livelihoods of millions of farmers, especially in the Mekong Delta (MD) and the Red River Delta [1,2]. As one of the world’s largest rice exporters, Vietnam is important to global food security, with exports projected to exceed 7 million tons by 2024 [3]. However, Vietnam is known as a low-quality rice producer [4], and rice exports face strong competition from countries such as India and Thailand. Moreover, the Mekong Delta rice sector still faces several challenges. Firstly, most farmers work on small, fragmented plots of land, often less than 1 hectare in size [5]. This fragmentation makes adopting modern, mechanized equipment difficult, driving up production costs. Secondly, farmers often use traditional rice cultivation methods based on experience, which reduces rice quality, increases costs, and harms the environment [6]. Finally, weak and unsustainable linkages between farmers and agribusiness companies lead to many risks in the linkage process [7]. These issues affect competitiveness, the sustainable development of the sector, and farmers’ income.

In addition, recent changes in global rice demand, coupled with the increasing impact of climate change on crop yields, call for partners in the rice value chain to shift to more sustainable production methods, which would create an opportunity for Vietnam to enhance its position as a high-quality rice producer and meet the growing global demand for sustainably produced products [8]. The Vietnamese government has implemented policies to promote horizontal integration through cooperation between cooperatives and farmer groups, and vertical integration has been established through contract farming. These policies have strengthened market linkages and increased rice productivity and farmer incomes.

Since 2000, the Vietnamese government has implemented policies to encourage cooperation between rice exporting enterprises and farmers through contracts. The contract farming mechanism was officially introduced in 2002 under Decision 80/2002/QD-TTg. Despite considerable efforts, contract rice farming in the Mekong Delta (MKD) has not been successful, with numerous cases of unilateral contract violations reported by contractors and farmers [9]. In 2013, the Vietnamese government proposed the Large Field Model (LFM) program under Decision 62/2013/QD-TTg to promote sustainable contract farming while addressing challenges such as low adoption rates and frequent contract violations. This program aims to enhance the competitiveness of the rice sector by promoting better cooperation between farmers, agribusinesses, and farmer organizations. In 2018, the Vietnamese government continued to issue Decree 98/2018/ND-CP, which incentivizes farmer organizations to participate in LFM officially. Subsequently, the Vietnamese government continued to support the Vietnam Sustainable Agriculture Transformation Program (VnSAT). VnSAT provides mechanisms to encourage farmer groups to implement sustainability standards and benefit from these standards through linkages with contract companies [10]. These policies have successfully encouraged the participation of rice farmers in contract farming.

In the context of the LFM program, although the benefits of participating in LFM through contract farming are clear for farmers and agribusinesses, its adoption has been slower than expected, covering less than 10% of the total rice area in MKD [11,12]. Thus, recognizing the obstacles in promoting this program is essential for the Vietnamese government and local authorities. A study by Tuyen [13] found that contract farming in rice production faces several problems, including contract violations, side-selling, local middlemen intervention, payment and delivery delays, non-compliance with production regulations, and a lack of trust. Similarly, findings by Mujawamariya et al. [14] and Trebbin [15] indicated that contract agreements failed due to side-selling caused by a lack of honesty from farmers toward their partners. Therefore, the quality of trust in the relationship is crucial in terms of business.

Research on the relationship quality (RQ) between farmers and companies in the contract farming model is crucial to address current challenges. High-quality relationships promote a greater willingness to cooperate closely with partners [16], strengthen partnerships [17], reduce opportunistic behavior [18], and foster loyalty [19]. Additionally, as companies reduce the number of suppliers, RQ becomes a decisive factor in maintaining and developing long-term partnerships [20].

Previous studies have provided valuable insights from the suppliers’ perspective regarding the antecedents of RQ. Schulze et al. [16] examined the RQ between suppliers and agribusinesses in Germany’s dairy and pork sectors using data from 209 dairy farmers and 357 pork farmers. Their findings revealed that RQ is influenced by several factors: farmer orientation, management image, communication, price satisfaction, and moderating variables. The authors also found surprising results that the processor’s orientation toward farmers and the farmers’ perception of management competence is more significant than price satisfaction. Lastly, RQ influences the willingness to cooperate more closely with the processor.

Sun et al. [21] investigated how suppliers’ perceptions of fairness affect RQ. They used Structural Equation Modeling (SEM) and surveyed 450 agricultural product suppliers in China. Their results indicated that procedural fairness is more critical than distributive fairness in enhancing RQ.

Ha et al. [22] analyzed the factors affecting RQ between coffee farmers and local traders in Vietnam by surveying 201 farmers. The results identified five influential factors: collaboration, perceived price, profit/risk sharing, power asymmetry, and effective communication. Among these, profit/risk sharing was the most important factor affecting relationship quality, while power asymmetry had a negative impact. Finally, RQ positively affected farmers’ profit and intention to maintain relationships with local traders.

The above studies showed the various antecedents of RQ. However, factors associated with farmers’ preferences for contract attributes remain underexplored, such as (1) price [23,24,25,26,27,28,29,30], (2) support from buyers [23,24,25,31], and (3) payment [26,27,28,30,31]. Regarding the consequences of RQ, loyalty (LO) is an outcome of RQ. Various studies have focused on examining the impact of RQ on LO from the customer or firm perspective in a B2B context [32,33,34,35], but few studies have investigated the supplier’s perspective [19,36]. Agricultural companies can develop strategies to maintain and enhance positive supplier relationships by understanding and assessing supplier LO.

Although previous studies have provided valuable insights into RQ in various contexts, research on this topic within the LFM context remains limited. Most prior studies focused on risks [37], benefits associated with contract farming [38], or factors influencing the decision to enter into contracts [37,39,40,41]. Notably, Nguyen and Mai [36] examined factors influencing RQ and LO between rice farmers and food companies in the MKD using Covariance-Based Structural Equation Modeling (CB-SEM) with a single-group approach. This approach provided valuable insights. However, they did not consider potential variations among different linkages within the supply chain. The assumption of homogeneity within the sample restricts the examination of differences between various groups of farmers.

Based on these issues above, this study aims to address critical challenges in the Vietnamese rice industry by investigating the dynamics of RQ and LO between farmers and agribusiness companies within the LFM. Specifically, it explores whether there are differences in RQ and LO between farmers and agribusiness companies across two groups in the LFM: cooperative and non-cooperative participating farmers. We used the PLS-MGA method to analyze these differences. The objectives aim to address the following research questions:

(1) What factors influence RQ between farmers and agribusiness companies in the two groups?

(2) How does RQ influence LO between farmers and agribusiness companies in the two groups?

(3) Are there differences in RQ and LO between farmers and agribusiness companies in the two groups?

(4) What strategies should agribusiness companies and policymakers implement to improve collaboration and manage farmer relationships?

This study contributes to the literature by bridging the current research gap and suggesting the following policy implications: (1) exploring the heterogeneity of partnerships in agricultural supply chains; (2) providing a comprehensive framework to analyze how contract attributes such as price satisfaction, payment terms, and support policies influence RQ; and (3) proposing feasible strategies to enhance partnerships to improve contract compliance and enhance agricultural supply chain performance. These contributions can provide a basis for policymakers and agricultural businesses to develop more effective relationship management strategies, promoting collaboration and enhancing sustainability in agricultural supply chains.

2. Materials and Methods

2.1. Overview of Large Field Model

The LFM was first introduced by the Ministry of Agriculture and Rural Development during a conference held in the MKD on 26 March 2011. The conference aimed to advance the LFM by integrating Good Agricultural Practices (GAPs) to establish high-quality rice production areas for export. The LFM program was officially implemented through Decision 62/2013/QD-TTg in 2013. The LFM aims to create large production areas for high-quality agricultural products, increase competitiveness in the market, improve production efficiency, and increase income for farmers and related parties. Initially implemented in the Mekong Delta, focusing on rice fields, the program has demonstrated its benefits and expanded to various provinces across Vietnam. It is now applied to crops such as corn, sugarcane, vegetables, and tea.

There are several definitions of the LFM in the literature. Phuoc [42] defines the LFM as a production method based on establishing linkages between farmers and businesses to gather small-scale farmers into large common production areas to facilitate the application of new technologies and stabilize the output market for farmers. Nguyen et al. [38] describe LFM as a collaborative effort between businesses and small-scale farmers to create large production areas to improve efficiency and maximize profit. Under this model, farmers can receive a production and technical support loan to apply for environmentally friendly development, while contractors have a stable source of high-value rice. Thang et al. [6] defined the LFM as production organizations where businesses or cooperatives establish cooperative relationships with farmers through contract farming. This setup implements a unified production process, provides production inputs (including materials and technical support), and purchases output from farmers. The LFM is categorized based on the nature of the connections: (1) farmers signing contracts with cooperatives and/or businesses; (2) farmers contributing land and/or labor to cooperatives; and (3) farmers leasing or selling their land to cooperatives or businesses.

This study aims to analyze the differences in RQ and LO between two distinct groups within the LFM Model 1 (contract farming). LFM Model 1 was chosen for two main reasons. First, it is the most widely implemented model among the three LFM models, making it a highly representative case of regional contract farming practices [6]. Second, the two groups in this model exhibit substantial differences in resource access, bargaining power, and dependency on companies, likely resulting in distinct perceptions of RQ and LO.

Figure 1 illustrates the contract structure of cooperative and non-cooperative participating farmers.

(1) Cooperative participating farmers (Farmer–Cooperative–Business): farmers sign indirect contracts with agribusiness companies through cooperatives. The company provides production inputs via the cooperatives and commits to purchasing farmers’ output.

(2) Non-cooperative participating farmers (Farmer–Business): farmers sign contracts directly with agribusiness companies. In this arrangement, the company supplies inputs and purchases the output directly from farmers.

As shown in Figure 2, the LFM rice area fluctuated significantly from 2011 to 2022. In 2011, the LFM area was 7800 ha, accounting for only 0.19% of the total cultivated rice area. In 2021, the LFM area peaked at 380,000 ha (9.74%). However, this trend decreased to 160,000 ha (4.20%) in 2022. The variation of the LFM area over the years shows that this area is still relatively low, below 10%, and has a decreasing trend. This highlights the need to implement strategies to scale up and improve the efficiency of the LFM.

2.2. Theoretical Framework

2.2.1. Relationship Quality

Relationship quality (RQ), a concept within relationship marketing theory, was initially introduced by Dwyer et al. [43] and subsequently developed by Crosby et al. [44]. Although previous research explored RQ in various research contexts, the definition and operationalization of the concept often differ [35,44,45,46,47,48]. Most scholars agree that RQ is a higher-order construct of several distinct but interrelated dimensions. Studies by Athanasopoulou [49], Schulze and Lees [50], and Osobajo and Moore [51] reviewed 64, 42, and 122 studies, respectively. They highlighted that satisfaction, trust, and commitment are RQ’s most commonly identified dimensions [16,35,48,52]. Therefore, this study focused on these key dimensions.

Satisfaction is “a positive affective state resulting from the appraisal of all aspects of a firm’s working relationship with another firm” [53]. Satisfaction is critical in driving repurchase intentions, behavioral intentions, customer retention, and loyalty [35].

Trust is defined “as existing when one party has confidence in an exchange partner’s reliability and integrity” [54]. Trust is important in exchange relationships as it fosters constructive dialogue and collaborative problem-solving [55].

Commitment is “an enduring desire to maintain a valued relationship” [56]. It promotes long-term success in business relationships [57]. Overall, commitment promotes cooperation and increases mutual profits [58].

2.2.2. Supplier Loyalty

Mutonyi et al. [59] found that supplier loyalty enhances chain performance due to lower transaction costs and decreased opportunistic behavior among producers and buyers. Boniface et al. [60] further elaborated that attracting, managing, and maintaining supplier loyalty leads to a more stable supply and reduces post-harvest losses. Sugandini and Wendry [19] refer to supplier loyalty as the commitment and attachment of suppliers to the company. It reflects the suppliers’ desire and actions to continue working with the company without switching to a competitor.

Previous studies divided loyalty (LO) into behavioral and attitudinal LO [61,62,63]. Behavioral LO reflects repeat purchase actions. Attitudinal LO is a feeling of attachment or affection toward a product or service, expressed through preference and psychological commitment.

Furthermore, previous studies suggested that LO can be measured using four approaches: uni-dimensional, bi-dimensional, composite, and second-order [63]. The uni-dimensional approach measures LO by focusing on attitudinal or behavioral aspects [64]. The bi-dimensional approach treats attitudinal and behavioral dimensions as separate constructs [35,62]. The composite approach combines these dimensions into a single construct [59,65,66]. In the second-order approach, customer LO is identified as a reflective second-order construct, which includes attitudinal LO and behavioral LO as its first-order dimensions [63,67]

Baldinger and Rubinson [61] found that composite measures, including both behavioral attitudes and components, showed higher accuracy in predicting LO. Similarly, Rauyruen and Miller [35] emphasized that a comprehensive approach is needed to fully understand the nature of LO, including both attitudinal and behavioral components. In line with these suggestions, we proposed that LO is a composite construct integrating behavioral and attitudinal LO. Specifically, this study refers to farmers’ commitment to maintaining long-term relationships and willingness to recommend the agribusiness company to others.

2.2.3. Relationship Quality and Supplier Loyalty

The connection between RQ and LO is established through two main theories: Transaction Cost Theory (TCT) and Relational Contract Theory (RTC) [33].

TCT suggests that participants in the transaction do not possess perfect market information and have limited knowledge [68]. This information asymmetry leads to opportunistic behavior against the less informed party, leading to uncertainty due to the inability to predict the opportunistic actions of the transacting parties [68,69]. However, when reciprocity is present, the exchanging parties prioritize long-term gains over short-term gains since long-term contracts reduce transaction costs. Thus, higher RQ reduces transaction costs by minimizing uncertainty and opportunistic behavior in exchanges, promoting an environment conducive to LO.

Meanwhile, according to RCT, stakeholders develop mutual understandings through long-term relationships. This relationship is shaped by established relational norms that guide future behavior [43]. One of the important norms in RCT is reciprocity, which Macneil [70] describes as a rule regarding the correlation in the relationship. RCT emphasizes that long-term relationships governed by relational norms, such as reciprocity and satisfaction, play a critical role in fostering LO.

Figure 3 illustrates the theoretical framework of the relationship between RQ and LO, in which RQ is structured by three dimensions: satisfaction, trust, and commitment. According to TCT, a high-quality relationship helps to minimize transaction costs and opportunistic behavior. RTC suggests that relational norms such as reciprocity and trust enhance long-term cooperation between parties. RQ directly affects two aspects of LO: attitudinal LO represents emotional attachment and the desire to continue cooperation, and behavioral LO is demonstrated through continued contract signing and agreement renewal.

2.2.4. Research Hypotheses

To improve the quality of partners’ relationships, identifying the key factors influencing relationship quality is essential. As shown in

Table 1. The preferences for contract attributes.

Author	Country	Product	Contract Attributes
Ihli et al. (2022) [31]	Eastern Rwanda	Fruit	Sales mode, payment timing, input/service provision, form of contract, relation to the buyer
Tuyen et al. (2022) 1 [28]	Viet Nam	Rice	Price options, payment, delivery arrangement, input provision, input use requirements, product quality standards
Widadie et al. (2021) [30]	Indonesia	Vegetables	Price, payment, quality, sale place, quantity
Lemeilleur et al. (2020) [25]	Brazil	Coffee	Sustainable practice, technical assistance, formal contract, price premium
Al Ruqishi et al. (2020) [23]	Oman	Vegetables	Type of partner, cropping decision rights, quality specifications, technical assistance, length of contract, price
Guentang (2018) [24]	Ghana	Jatropha	Nature of contract, price agreement, support from buyer, renegotiation option
Ochieng et al. (2017) [26]	Kenya	Vegetables	Price, place of sale, form of sale, timing of sale, payment mode
Van den Broeck et al. (2017) [29]	Benin	Rice	Herbicide use, chemical fertilizer use, child labor, fairtrade premium, input provision, selling price
Schipmann et al. (2011) [27]	Thailand	Sweet pepper	Price, payment mode, input provision, relation to the trader

¹ The most important contract attributes of farmers.

, previous studies highlighted important farmer preferences for contract attributes, such as buyer support, payment, and price. These attributes influence farmers’ satisfaction and decisions regarding continued cooperation with agribusiness companies [24,28,29,31]. However, the relationship between these attributes and RQ has yet to be fully explored. Hence, we suggested that contract attributes, such as support policies, payment terms, and price satisfaction, are antecedents of RQ.

Support policies refer to the types of services supplied to farmers. Guentang [24] highlighted full support from the buyer regarding seeds, technical training, fertilizers, and pesticides. Ihli et al. [31] described service provision, including tree seedlings, fertilizers, access to credit, and training provided by the buyer. Bhagat and Dhar [71] suggested that effective support policies increase smallholder satisfaction. Nguyen and Mai [36] indicated that support policies are evaluated by the adequacy of raw material support, the level of assistance compared to other firms, and the flexibility in adapting support to meet farmers’ needs. Based on previous studies [24,31,36,71], this study identifies support policies, including input provision, technical assistance, training, and information sharing. In contexts where farmers have varying access to resources, Nguyen and Mai [36] found that SP positively affects RQ. Thus, we proposed the following hypothesis.

H1. 

Support policy (SP) positively impacts the relationship quality (RQ) between farmers and agribusiness companies according to two groups.

Due to liquidity challenges and high financial risks in the agricultural supply chain, payment relationship terms are an important factor affecting the relationship between farmers and buyers. Payment terms refer to the payment schedule and method [28]. According to Nguyen and Mai [36], payment terms are measured through the transparency of payment regulations, commitment to timely payment, and performance in adjusting payments according to farmers’ requests. Payment delays can undermine trust and decrease farmers’ willingness to participate in future contracts [37]. Moreover, payment terms considerably impact the relational risks between producers and buyers [72]. Bhagat and Dhar [71] found that efficient payment mechanisms improve the satisfaction of smallholder farmers. According to Nguyen and Mai [36], well-structured payment terms enhance RQ by aligning the interests of both buyers and suppliers. Thus, we proposed the following hypothesis:

H2. 

Payment terms (PTs) positively impact the relationship quality (RQ) between farmers and agribusiness companies according to two groups.

Economic evaluations play a critical role in assessing the quality of business relationships. These evaluations emphasize the role of economic factors, including price and financial performance [73]. Price satisfaction is a positive affective state created by price-related factors [60]. According to Schulze et al. [16], price satisfaction is measured as satisfaction with the price received and satisfaction achieved by comparing prices with other companies. Similarly, Gyau [74] argues that price satisfaction is measured by farmers’ satisfaction with the price the company pays for their products, their evaluation of the company’s prices over the years, and comparisons of prices between the company and its industry competitors. Moreover, empirical studies provide mixed evidence. Some studies prove that value influences satisfaction [75,76] or trust influences satisfaction [77,78]. Additionally, several studies indicate that price satisfaction positively influences RQ [16,74,79]; others report no significant effect [80]. In the LFM context, where farmers face fluctuating market prices and often limited market access, price satisfaction becomes an important factor influencing RQ. Based on this, the hypothesis is stated as follows:

H3. 

Price satisfaction (PS) positively impacts the relationship quality (RQ) between farmers and agribusiness companies according to two groups.

LO is an outcome of RQ between buyers and suppliers. LO is determined by the level of commitment of farmers to maintain contracts and continue long-term relationships with partners, as well as their willingness to recommend partners to other farmers and consider partners as a preferred choice in future transactions [19,34,81]. Furthermore, empirical evidence from previous studies shows a positive relationship between RQ and LO [19,36]. Based on this understanding, the following hypothesis is proposed:

H4. 

Relationship quality (RQ) positively impacts loyalty (LO) between farmers and agribusiness companies according to two groups.

2.3. Methodology

2.3.1. Partial Least Squares–Structural Equation Modeling (PLS-SEM)

PLS-SEM, initially developed by Wold [82], is a robust statistical technique designed to maximize the variance explained by independent variables using the ordinary least squares (OLS) method [83]. Unlike CB-SEM, PLS-SEM is non-parametric and does not require assumptions about the data distribution [83]. This approach is suited for complex models involving numerous observed variables and smaller sample sizes [84]. Therefore, PLS-SEM is a suitable statistical method that can be applied in various research situations without the limitations of CB-SEM [84].

Given the research context, PLS-SEM is suitable for model estimation because of the sample size, number of indicators, and data distribution. This study used SmartPLS™ version 4 software to evaluate the measurement model and test the path relationships between model constructs based on the collected data.

PLS-SEM comprises structural and measurement models [84]. The measurement model provides the relationship between latent variables and observed indicator variables. Observed indicator variables are utilized to measure each latent variable, and these measurements are tested for reliability and validity [84]. Generally, there are two primary approaches to measuring latent variables: reflective and formative measurement [85]. As shown in Figure 4, the measurement model comprises constructs ξ₁, ξ₂, and ξ₃ ($\mathrm{S}\mathrm{P},\mathrm{P}\mathrm{T},\mathrm{a}\mathrm{n}\mathrm{d}\mathrm{P}\mathrm{S})$ and η₁ and η₂ (RQ and LO), which are structured using a reflective measurement model. The measurement model is represented by two equations as follows:

$${\mathrm{X}}_{ij}={\mathsf{\lambda }}_{xij}\ast {\mathsf{\xi }}_i+{\mathsf{\delta }}_{ij}$$

(1)

$${\mathrm{Y}}_{ij}={\mathsf{\lambda }}_{yij}\ast {\mathsf{\eta }}_i+{\mathsf{\epsilon }}_{ij}$$

(2)

where ${\mathrm{X}}_{ij},{\mathrm{Y}}_{ij}$ are the observed values of the j-th (j = 1, …, 7) measurement variable for the i-th (i = 1, … 10) latent variables. ${\mathsf{\xi }}_1,{\mathsf{\xi }}_2, \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\xi }}_3 (\mathrm{S}\mathrm{P},\mathrm{P}\mathrm{T}, \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{P}\mathrm{S}) \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\eta }}_1 \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\eta }}_2 (\mathrm{R}\mathrm{Q} \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{L}\mathrm{O})$ are the true values of latent variables. ${\mathsf{\lambda }}_{xij} \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\lambda }}_{yij}$ are the factor loading representing how each observed variable relates to the latent variables. ${\mathsf{\delta }}_{ij}$ and ${\mathsf{\epsilon }}_{ij}$ are error terms representing the unobserved factors influencing the observed variables.

After evaluating latent variables in the measurement models, the structural model hypothesizes and assesses potential relationships among these latent variables [84]. As shown in Figure 4, latent variables ξ₁, ξ₂, and ξ₃ ($\mathrm{S}\mathrm{P},\mathrm{P}\mathrm{T}, \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{P}\mathrm{S})$ function exclusively as independent variables (exogenous latent variables), while η₂ (LO) serves solely as a dependent variable. η₁ (RQ) functions as both independent and dependent variables (endogenous latent variables). The structural model consists of two equations as follows:

$${\mathsf{\eta }}_1={\mathsf{\gamma }}_{x11}\ast {\mathsf{\xi }}_1+{\mathsf{\gamma }}_{x12}\ast {\mathsf{\xi }}_2+{\mathsf{\gamma }}_{x12}\ast {\mathsf{\xi }}_{\begin{array}{c}3\\ \end{array}}+{\mathsf{\zeta }}_1$$

(3)

$${\mathsf{\eta }}_2={\mathsf{\beta }}_{21}\ast {\mathsf{\eta }}_1+{\mathsf{\zeta }}_2$$

(4)

where ${\mathsf{\xi }}^1,{\mathsf{\xi }}^2, \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\xi }}^3 \left(\mathrm{P}\mathrm{T},\mathrm{P}\mathrm{S}, \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{S}\mathrm{P}\right) \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\eta }}_1 \mathrm{a}\mathrm{n}\mathrm{d} {\mathsf{\eta }}_2(\mathrm{R}\mathrm{Q} \mathrm{a}\mathrm{n}\mathrm{d} \mathrm{L}\mathrm{O})$ are the true values of latent variables. ${\mathsf{\gamma }}_{x11},{\mathsf{\gamma }}_{x12},{\mathsf{\gamma }}_{x12}, {\mathrm{a}\mathrm{n}\mathrm{d} \mathsf{\beta }}_{21}$ are the path coefficients in the structural model. ${\mathsf{\zeta }}_1$ and ${\mathsf{\zeta }}_2$ are error terms representing the unobserved factors influencing the observed variables.

2.3.2. Partial Least Squares–Multi-Group Analysis (PLS-MGA)

PLS-MGA tests for differences in parameter estimates, such as weights and path coefficients between predefined groups [86]. This technique allows the comparison of identical models between different groups [87] and facilitates the simultaneous assessment of multiple relationships [84].

Before PLS-MGA analysis, it is essential to assess the measurement invariance [88]. Measurement invariance is critical to measure constructs consistently across different groups. Without appropriate measurement invariance, there is a risk of misinterpretation and erroneous conclusions regarding group differences [89,90]. We employed the measurement invariance analysis of composite models (MICOMs) technique to evaluate measurement invariance. This involves a three-step process: (1) configural invariance, which checks whether the same pattern of factor loadings holds across groups; (2) compositional invariance, which assesses whether the relationships among constructs are consistent; and (3) equality of pooled means and variances, which examines whether the means and variances of the constructs are equivalent across groups. Achieving these steps establishes full measurement invariance and supports the pooling of data from different groups [88].

Once measurement invariance is confirmed, the Henseler PLS-MGA procedure can be utilized to compare path coefficients between groups, thereby effectively identifying significant differences [91]. However, configural and compositional invariance was established. Researchers can confirm partial measurement invariance. Partial measurement invariance allows for comparing standardized coefficients within the structural model across different groups [88,92].

2.4. Data Collection

This study used primary data collected in three provinces of the Mekong Delta: Can Tho, An Giang, and Hau Giang. These provinces are the main rice-producing regions in the MKD and have a high participation rate in the LFM. We employed a two-stage random sampling design. In the first stage, we purposively selected two communes in each province, with the first commune representing the cooperative participation group and the second representing the non-cooperative participation group. Specifically, Dong Hiep and Trung Thanh in Can Tho, Tan Phu and Vong Dong in An Giang, and Phuong Binh and Hoa An in Hau Giang were selected. In the second stage, respondents were randomly selected from a list of farmers in each area provided by local extension officers. The sample consisted of 147 cooperative participating farmers, including 60 farmers in Dong Hiep, 35 in Tan Phu, and 52 in Phuong Binh. Additionally, 153 non-cooperative participating farmers were surveyed, including 55 farmers in Trung Thanh, 55 in Vong Dong, and 43 in Hoa An.

To determine the appropriate sample size for statistical analysis, we followed the guidelines suggested by Barclay et al. [93], which recommend adhering to the “10-times rule” for PLS-SEM. According to this rule, the minimum sample size should be ten times the largest number of independent variables pointing to any latent variable in the most complex regression of the PLS path model. This ensures statistical adequacy for both the measurement and structural models. In practical terms, this equates to ensuring that the sample size is at least ten times the maximum number of arrowheads pointing to any latent variable in the model. Based on our research model, the most complex regression in the PLS path model involves three independent variables (support policy, payment terms, and price satisfaction) pointing to the latent variable RQ. Applying the “10-times rule”, the minimum required sample size would be 10 × 3 = 30.

However, Hair et al. [84] emphasized that the minimum sample size requirement in PLS-SEM should be carefully considered based on the statistical power of the estimates. Therefore, we also evaluated the sample size based on SEM recommendations. SEM analysis, including Confirmatory Factor Analysis (CFA), requires careful sample size assessment to ensure the results’ robustness and validity to ensure the sample size meets statistical requirements. However, there is no universal standard for determining the appropriate sample size in SEM, as requirements may vary depending on the specific context [94]. One approach to determining the sample size for SEM depends on the number of indicator variables per latent variable or factor. For models with three or four indicators per factor, a minimum sample size of N > 100 is generally recommended [95]. Furthermore, according to research by Hoyle [96], a sample size ranging from 100 to 200 is generally considered appropriate for SEM. Thus, the sample size of this study was appropriate, meeting both the minimum requirements of PLS-SEM and the broader recommendations of SEM to ensure the reliability of research results.

We conducted face-to-face interviews using a questionnaire between the respondent and the surveyor. The questionnaire consisted of two parts: (i) socio-demographic characteristics and (ii) components related to five construct measurements: support policies, payment terms, price satisfaction, relationship quality, and loyalty. These constructs were measured using 17 indicators. As shown in

Table 2. The description, mean, and standard deviation, as well as the reference resources of indicators.

Constructs	Indicators	Explanations	Std. Deviation	Mean	Reference Resources
Support policy (SP)	SP1	The agribusiness company (A) regularly organizes technical training courses, shares agricultural information, and provides promotional materials to farmers.	1.14	2.81	Guentang [24] Ihli et al. [31] Nguyen and Mai [36] Bhagat and Dhar [71]
	SP2	The technical production advisory support from agribusiness company (A) has improved farmers’ productivity and income.	1.11	2.64
	SP3	The agribusiness company (A) supports farmers with input materials that meet the quantity and quality requirements.	1.08	2.81
	SP4	The agribusiness company (A) is flexible in supporting farmers.	1.16	2.65
Payment terms (PTs)	PT1	The agribusiness company (A) provides suitable delivery methods.	0.47	4.09	Nguyen and Mai [36] Tuyen et al. (2022) [28]
	PT2	The agribusiness company (A) purchases on time and in the correct quantity.	0.52	4.08
	PT3	The agribusiness company (A) provides clear regulations and payment methods.	0.52	4.06
Price satisfaction (PS)	PS1	The procurement price of the business matches the product quality.	0.55	4.32	Schulze et al. [16] Gyau et al. [74]
	PS2	Compared to other companies, the procurement price of the agribusiness company (A) is reasonable.	0.55	4.29
	PS3	Farmers are satisfied with the current procurement price offered by the agribusiness company (A).	0.61	4.31
Relationship quality (RQ)	RQ1	The relationship with the company (A) meets my goals and expectations.	0.48	4.04	Schulze et al. [16] Smith [52] Rauyruen and Miller [35] Walter et al. [48]
	RQ2	I am satisfied with the relationship with the agribusiness company (A).	0.47	4.09
	RQ3	I trust that the relationship with the company will be stable and long-term.	0.51	4.10
	RQ4	The commitments between me and the agribusiness company (A) are ensured.	0.49	4.06
Loyalty (LO)	LO1	I will continue signing contracts and maintaining long-term relationships with the agribusiness company (A).	0.49	4.12	Baldinger and Rubinson [61] Rauyruen and Miller [35] Jamal and Anastasiadou [81] Sugandini and Wendry [19] Liu et al. [34]
	LO2	I will recommend the agribusiness company (A) to other farmers.	0.63	3.76
	LO3	The agribusiness company (A) is my first choice.	0.89	3.82

, the indicators employed in this study were either specifically developed for the study or adapted from existing indicators from previous studies to fit the research context, as presented in the theoretical framework section. The scale for these indicators was based on those previously tested and validated in previous literature. The scale was rated using a five-point Likert scale (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree), as referenced in previous studies [19,36,71].

Before conducting formal interviews, surveyors attended two training sessions: one before the pilot interviews and one before starting formal data collection. This study was committed to ensuring the confidentiality and anonymity of all respondents. Participation in the study was completely voluntary, and all respondent data were anonymized. In addition, this study did not collect sensitive personal information, and respondents were informed in advance about confidentiality, anonymity, and the use of data for research purposes only. Data were collected in November 2023. However, respondents were asked to provide information relevant to 2022 in the interview questionnaire. This is because Vietnam’s domestic and international rice markets experienced significant fluctuations in supply and demand, significantly affecting prices in 2023. Thus, retrospective data were necessary to ensure the reliability and accuracy of the research results. During the interview, the interviewer recorded the responses directly on a pre-prepared questionnaire, and then the data were entered into a database.

3. Results

3.1. The Measurement Model

In this study, we assessed the measurement model using 17 measurement variables through SmartPLS 4. Five latent constructs were formed with these 17 measurement variables, retaining their original names as PT, PS, SP, RQ, and LO due to the absence of disturbances among variables. However, the SP latent construct, formed from four measurement variables (SP1, SP2, SP3, and SP4), had an Average Variance Extracted (AVE) value below the recommended threshold of 0.5 [84]. Consequently, SP1 and SP4 were excluded due to their low factor loadings and insufficient contribution to the AVE. In the subsequent analysis, the measurement model was tested in the subsequent analysis with 5 latent constructs and 15 measurement variables. The results are presented below.

Reliability was assessed using Cronbach’s alpha and Composite Reliability (CR). According to Hair et al. [84], a value greater than 0.60 for these metrics indicates an acceptable level of reliability.

Table 3. The reliability assessment.

Constructs	Cronbach’s Alpha	CR	AVE
LO	0.627	0.790	0.559
PS	0.837	0.899	0.748
PT	0.824	0.891	0.732
RQ	0.803	0.869	0.625
SP	0.756	0.886	0.795

demonstrated that all constructs exceed this threshold, confirming the reliability of the measurement model. Thus, the constructs are measured consistently, ensuring the stability and consistency of the model.

Convergent validity was evaluated through Average Variance Extracted (AVE) and factor loadings. The AVE reflects the proportion of variance captured by a construct relative to the variance due to measurement error. In addition, factor loadings measure the correlation between observed variables and their underlying latent constructs, indicating how well each indicator represents its corresponding latent variable. According to Hair et al. [84], the AVE should be at least 0.6, and factor loadings should be greater than 0.5 [97]. The results in Table 3 and

Table 4. The factor loadings and VIF.

Constructs	LO	PS	PT	RQ	SP	VIF
LO1	0.821	0.211	0.239	0.222	−0.046	1.180
LO2	0.626	0.078	0.204	0.102	0.306	1.254
LO3	0.783	0.186	0.241	0.176	0.285	1.330
PS1	0.201	0.885	0.023	0.264	−0.077	2.090
PS2	0.100	0.804	0.061	0.170	−0.225	1.848
PS3	0.252	0.903	0.152	0.315	−0.150	1.971
PT1	0.278	0.138	0.826	0.093	0.054	1.925
PT2	0.259	0.101	0.949	0.182	0.214	2.409
PT3	0.269	−0.003	0.783	0.084	0.184	1.707
RQ1	0.185	0.256	0.137	0.863	−0.087	2.129
RQ2	0.089	0.155	0.076	0.686	−0.095	1.530
RQ3	0.215	0.287	0.132	0.819	−0.072	1.608
RQ4	0.223	0.227	0.131	0.783	0.092	1.557
SP2	0.205	−0.172	0.139	−0.029	0.838	1.586
SP3	0.144	−0.130	0.189	−0.048	0.943	1.586

demonstrated that both the AVE and factor loadings exceed these thresholds, confirming strong convergent validity.

Discriminant validity ensures that latent variables in a model remain distinct, thereby enhancing the model’s reliability and predictive accuracy. To assess discriminant validity in this study, we employed the Fornell–Larcker criterion and the heterotrait–monotrait ratio of correlations (HTMT) [98]. The Fornell–Larcker criterion confirms discriminant validity when the square root of the AVE for each construct, shown on the diagonal of the correlation matrix, exceeds the correlations in the corresponding rows and columns. Additionally, the HTMT approach considers discriminant validity to be established if the HTMT value is below 0.85, as suggested by Kline [99]. The results in

Table 5. The discriminant validity.

Fornell–Larcker criterion
Constructs	LO	PS	PT	RQ	SP
LO	0.748
PS	0.228	0.865
PT	0.303	0.098	0.856
RQ	0.236	0.302	0.155	0.791
SP	0.185	−0.162	0.188	−0.045	0.892
Heterotrait–monotrait ratio (HTMT)
Constructs	LO	PS	PT	RQ	SP
LO
PS	0.277
PT	0.431	0.134
RQ	0.297	0.338	0.166
SP	0.413	0.227	0.214	0.140

confirm that both methods meet the necessary criteria for discriminant validity.

The variance inflation factor (VIF) was assessed to identify potential multicollinearity issues. Kock and Lynn [100] recommend using the full collinearity test in PLS-SEM with a VIF below the threshold of 3.3 [101]. Table 4 shows that all VIF values are below this threshold, confirming the absence of collinearity problems.

3.2. The Structural Models

This study employed the MICOM technique to test measurement invariance. The first step assessed configural invariance. As the analysis and assessment of the measurement models (including reliability and validity) for all groups were completed in the previous section, configural invariance was established. The second step evaluated compositional invariance. Using permutation analysis with 5000 bootstrap samples in SmartPLS 4, this study evaluated compositional invariance. As shown in

Table 6. MICOM results.

Composite	c Value (=1)	p-Value	Compositional Invariance
LO	0.982	0.753	Yes
PS	0.988	0.279	Yes
PT	0.916	0.515	Yes
RQ	0.987	0.252	Yes
SP	0.994	0.848	Yes
Composite	Difference in the Composite’s Mean Value (=0)	p-Value	Equal Means
LO	−0.281	0.006	No
PS	−0.061	0.291	Yes
PT	−0.124	0.122	Yes
RQ	0.059	0.318	Yes
SP	−0.323	0.000	No
Composite	Composite Logarithm of the Composite’s Variance Ratio (=0)	p-Value	Variance Means
LO	−0.354	0.047	No
PS	−0.424	0.002	No
PT	−0.972	0.001	No
RQ	−1.050	0.000	No
SP	−0.662	0.000	No

, all compositional invariance correlation (c) values were close to 1 and fell within the 95% confidence interval. Hence, compositional invariance was established across all groups. The third step of the MICOM procedure examined the equality of means and variances of constructs across groups. The results from the permutation test (5000 permutations) revealed significant differences in the mean values and variances of the composites across groups. Step 3 of the MICOM procedure did not support full measurement invariance. Thus, partial measurement invariance was established between the two groups. This allows for comparing standardized coefficients in the structural model across different groups.

Table 7. The outcomes of the Structural Equation Model.

Hypothesis and Paths	Path Coefficients	Sample Mean	Standard Deviation	T Statistics	p-Value
Cooperative participation
H1: SP → RQ	−0.055	−0.057	0.116	0.475	0.635
H2: PT → RQ	0.259 **	0.280	0.110	2.346	0.019
H3: PS → RQ	0.227 **	0.232	0.091	2.494	0.013
H4: RQ → LO	0.452 ***	0.471	0.088	5.130	0.000
Non-cooperative participation
H1: SP → RQ	0.008	0.001	0.103	0.077	0.939
H2: PT → RQ	0.150	0.099	0.189	0.795	0.427
H3: PS → RQ	0.309 ***	0.311	0.077	4.019	0.000
H4: RQ → LO	0.179 *	0.211	0.104	1.726	0.084

Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

and

Table 8. A summary of the hypothesis results: cooperative vs. non-cooperative participation groups.

Hypothesis and Paths	Cooperative Participation	Non-Cooperative Participation	Difference
H1: SP → RQ	−0.055	0.008	No
H2: PT → RQ	0.259 **	0.150	Yes
H3: PS → RQ	0.227 **	0.309 ***	No
H4: RQ → LO	0.452 ***	0.179 *	Yes

Note: *** p < 0.01, ** p < 0.05, * p < 0.1.

summarize the findings of the structural equation for each group using 5000 bootstrap re-samples. The results indicated that SP did not significantly impact RQ for either group. Thus, hypothesis H1 was not supported. Additionally, PT had a significant positive impact on RQ in the cooperative participation group at the 5% significance level, supporting hypothesis H2 for this group. However, the effect was insignificant in the non-cooperative participation group, leading to the rejection of hypothesis H2.

In contrast, PS had a significant positive effect on RQ in both groups, with a significance level of 5% for the cooperative participation group and 1% for the non-cooperative participation group, providing strong support for hypothesis H3. Finally, RQ positively influenced LO in both groups, supporting hypothesis H4. Specifically, the cooperative participation group exhibited a stronger effect, with a path coefficient of 0.452 and a p-value of less than 0.001. Meanwhile, the non-cooperative participation group showed a weaker effect, with a path coefficient of 0.179 and a p-value of 0.084.

3.3. Multi-Group Analysis

The findings presented the results of MGA using two non-parametric techniques: Henseler’s bootstrap-based MGA [85] and the permutation test [102]. Each method involved 5000 bootstrap re-samples and 5000 permutations. These techniques are robust for analyzing relationships between variables across different groups, thereby enhancing the reliability and validity of the results. They are considered the most conservative approaches for PLS-SEM [87].

Henseler’s MGA directly compares group-specific results with bootstrap samples. A p-value below 0.05 or above 0.95 in this context indicates significant differences between the two groups at the 5% significance level for specific path coefficients. Similarly, in the permutation test, differences are only considered significant at the 5% level if the p-value is below 0.05.

Table 9. The results of multi-group analysis.

Hypothesis and Paths	Path Coefficients Cooperative vs. Non-Cooperative	p-Value of Henseler’s MGA	p-Value of Permutation Test
H1: SP → RQ	−0.063	0.659	0.318
H2: PT → RQ	0.109	0.731	0.239
H3: PS → RQ	−0.083	0.482	0.260
H4: RQ → LO	0.274 ***	0.025	0.005

Note: *** p < 0.01.

presents the results of the multi-group analysis when comparing the cooperative and non-cooperative participation groups. The result of H4 (RQ → LO) supported our initial expectation, demonstrating that the impact of RQ on LO differs significantly between the two groups. Specifically, a stronger effect was observed in the cooperative participation group, with a significant difference confirmed at the 1% level through both Henseler’s MGA and the permutation test.

Contrary to our initial expectations, the results indicated no differences in the effects of SP, PT, and PS on RQ between the two groups. Specifically, the path coefficient of H1 (SP → RQ) was −0.063, with a p-value of 0.659. Similarly, the path coefficient of H2 (PT → RQ) was 0.109, with a p-value of 0.731. The path coefficient of H3 (PS → RQ) was −0.083, with a p-value of 0.482.

4. Discussion and Conclusions

This study provides valuable insights into the dynamics of relationship quality (RQ) and loyalty (LO) in the Vietnamese rice supply chain, especially under the Large Field Model (LFM). Using PLS-MGA, we assessed the differential effects of price satisfaction (PS), payment terms (PTs), and support policy (SP) on RQ, as well as the influence of RQ on LO in both cooperative and non-cooperative participating farmers.

The results revealed a difference in the impact of RQ on LO between the two groups, and RQ positively impacted LO. The cooperative participation group demonstrated the highest path coefficient, indicating a stronger effect of RQ on LO. This finding supported hypothesis H4, aligning with previous studies highlighting the significant impact of RQ on LO [19,36] and supporting our initial expectation. These results further confirmed that cooperatives provide a more supportive environment to foster satisfaction, trust, and commitment, strengthening farmers’ LO toward agribusiness companies.

Regarding each group’s Structural Equation Modeling results, SP did not significantly impact RQ in either group. This finding contrasts with the findings of previous studies [36,71]. This may be due to large differences in how agribusiness companies implement support policies. For example, some agribusiness companies invest heavily in technical assistance programs and provide high-quality inputs, while others do not maintain the same level of support, leading to uneven effects. In addition, the type of companies (e.g., large firms versus small- and medium-sized firms) may influence the consistency and quality of support policies, as large companies typically have more resources to invest in these programs than medium and small companies. Finally, differences in farmers’ levels of participation in support programs complicate the relationship between the parties and affect how farmers evaluate the impact of SP on RQ. These differences can explain the rejection of hypothesis H1.

Moreover, the results showed that PT positively impacted RQ in the cooperative participation group and were consistent with previous studies [36,71]. However, PT had no significant effect in the non-cooperative participation group, leading to H2’s rejection. This difference can be attributed to the role of cooperatives as intermediaries between farmers and businesses. By increasing farmers’ bargaining power, cooperatives can negotiate more flexible and favorable payment terms, such as partial prepayments or installment payments, which help ease farmers’ financial burden. In addition, cooperatives support members in the event of problems such as delayed payments from businesses, thereby strengthening trust and increasing stability in the relationship between farmers and businesses. In contrast, non-cooperative participating farmers often receive only one-time payments when selling their products to companies and have only a minor ability to negotiate or adjust payment terms. This increases financial risk and limits the incentive to maintain long-term relationships with businesses. These findings highlighted the important role of cooperatives in improving farmers’ financial stability through payment arrangements with agribusinesses.

Given the above results, this study found that PS was identified as the most influential factor in relationship quality, providing empirical evidence to support hypothesis H3. This finding underscores the importance of pricing policies in maintaining close relationships between farmers and agribusinesses, aligning with previous studies [16,74,79]. The positive effect of PS on RQ can explained by the economic and psychological role of price. Firstly, price is a core economic factor for farmers, especially small farmers who often face financial risks. A reasonable and stable price provides a secure source of income and is a prerequisite for creating price satisfaction from farmers. Secondly, price satisfaction directly impacts perceptions of fairness, commitment, and trust. A transparent and consistent price with market value helps farmers feel good about their labor. This reduces conflict in the cooperative relationship and promotes engagement between parties. In the context of this study, the prominent role of PS can reflect the market structure, where farmers often rely heavily on purchasing companies. In markets where farmers have little bargaining power, prices reflect financial factors and represent support and recognition from companies. This explains why price satisfaction can strongly influence RQ. However, Phuong et al. [80] found that PS did not affect RQ, possibly due to differences in pricing strategies, negotiation processes, or perceptions of fairness in pricing practices.

Based on the findings, our results suggest that pricing policies play a more important role than support programs, especially in uneven support implementation settings. The effectiveness of these support policies can depend on firm size, consistency in firm implementation, and farmer participation. In contrast, price satisfaction was identified as having the greatest influence on relationship quality, reflecting its dual role in ensuring financial security and reinforcing perceptions of commitment and trust. These results challenged previous assumptions from other studies [36,71] that support that policies consistently contribute to relationship quality across different contexts. This underscored the need for agribusiness companies to prioritize pricing strategies that ensure transparency and stability.

This study has several recommendations for agribusiness companies. Firstly, agribusiness companies should prioritize competitive pricing strategies that align with farmers’ production costs and market conditions. This is particularly important for non-cooperative farmers, who need more negotiating power in price discussions. Secondly, agribusiness companies should regularly assess the effectiveness of their support policies. This evaluation process should involve identifying and addressing shortcomings through farmer surveys, followed by appropriate amendments, supplements, or introducing new policies. Lastly, agribusiness companies should be more flexible in payment terms to provide financial support for non-cooperative participating farmers. This study also proposes recommendations for policymakers. Firstly, policymakers should strengthen the legal framework to protect the interests of farmers and enterprises to minimize the risk of contract breaches and side-selling. This includes clearly defining the rights and obligations of the parties involved and establishing an effective dispute-resolution mechanism. Strengthening the legal framework will help create a safe and transparent environment for agricultural contracts. Secondly, policymakers should provide financial support packages, including preferential loans and subsidies for cooperatives. These will assist cooperatives in enhancing their management and operational capacity, attracting farmers to participate and ultimately improving productivity and income for farmers. Finally, policymakers should prioritize providing information and organizing technical training programs for farmers. These initiatives can facilitate the adoption of efficient and sustainable production techniques and optimize production scales.

These policies provide concrete directions for Vietnam and serve as a reference significance for other countries with similar agricultural contexts. A well-established and well-monitored legal framework protects stakeholders and reduces the risk of contract breaches. This is a prerequisite for promoting trust and cooperation in agricultural supply chains, especially in countries with weak legal systems. In addition, providing financial support packages helps to enhance the capacity of cooperatives and enables them to act as effective intermediaries between farmers and companies. Finally, providing information and training programs helps farmers reduce their dependence on inefficient traditional production methods.

Future research could focus on exploring the factors that influence the effectiveness of support policies on relationship quality. This could include examining differences in how these policies are implemented across agricultural enterprises, the role of the enterprise size, and the degree of interaction between farmers and companies. The research directions will help to clarify the impact of support policies on relationship quality and provide a basis for improving the effectiveness of support policies in practice. Additionally, future studies could examine the impact of collaborative governance structures on relationship quality and loyalty and investigate the role of digital platforms in improving relationship quality. Such studies can provide valuable insights into strengthening partnerships in agricultural supply chains.