Linking supply chain collaboration, decision-making, and risk management to smallholder farm performanceAbstract
Paper aims This study aims to examine how supply chain collaboration and supply chain decision-making influence smallholder farm performance through supply chain risk management (SCRM) in corn farming under conditions of high uncertainty.
Originality This study integrates relational (collaboration) and managerial (decision-making) capabilities within a Resource-Based Theory (RBT) framework, positioning SCRM as a mediating operational capability that links supply chain capabilities to performance in smallholder agricultural systems.
Research method A quantitative approach was employed using survey data from 380 smallholder corn farmers in Gorontalo Province, Indonesia. The data were analyzed using Partial Least Squares Strucral Equation Modeling (PLS-SEM) to assess both direct and indirect relationships among the constructs.
Main findings The results show that supply chain collaboration has a positive and significant effect on farm performance, while decision-making has no direct significant effect. However, both collaboration and decision-making significantly influence SCRM, which in turn strongly improves performance. SCRM partially mediates the relationship between collaboration and performance and fully mediates the relationship between decision-making and performance.
Implications for theory and practice This study extends RBT by demonstrating how supply chain capabilities are transformed into performance outcomes through risk management in smallholder agriculture. Practically, improving farm performance requires strengthening collaboration and enhancing SCRM capabilities, while ensuring that decision-making processes are effectively aligned with risk management practices.
Keywords:
Supply chain collaboration; Supply chain decision-making; Supply chain risk management; Farm performance; Smallholder agriculture
1. Introduction
Organizational performance is a critical determinant of sustainability and competitiveness, particularly in sectors dominated by small-scale producers such as agriculture. Smallholder farming plays a vital role in supporting rural livelihoods, food security, and regional economic development in developing countries (Abdul-Rahaman & Abdulai, 2020; Mishra et al., 2024). In Indonesia, particularly in regions such as Gorontalo Province, smallholder corn farmers contribute significantly to agricultural production but operate under structurally vulnerable conditions characterized by limited access to resources, weak market integration, and high exposure to environmental and market uncertainties (Rusliyadi, 2026).
Agricultural supply chains are inherently exposed to multiple and interconnected sources of risk, including climate variability, demand fluctuations, price volatility, operational disruptions, and logistical uncertainty (Routroy & Behera, 2017; Wang et al., 2024). Recent research on food supply chains further demonstrates that these risks rarely occur in isolation. A systematic review by Medeiros et al. (2025b) identifies a broad range of food supply chain risks, including disruption, forecasting, operational, environmental, and logistics risks, while emphasizing the importance of proactive, reactive, and concurrent mitigation strategies. Complementarily, Medeiros et al. (2025a) demonstrate that risks within food supply chains are interdependent, with natural and macro-level risks exerting substantial influence on demand, supply, and operational risks. In smallholder contexts, these risks are further exacerbated by fragmented supply chain structures and limited coordination among actors, resulting in inefficiencies in production and distribution processes (Ho et al., 2023; Plakantara & Karakitsiou, 2025). As a consequence, the mere presence within supply chains does not automatically guarantee improved farm performance, highlighting the need for more effective and adaptive supply chain management strategies (Alkandi et al., 2025; Green Junior et al., 2012).
In response to these challenges, supply chain risk management (SCRM) has emerged as a critical mechanism for enhancing resilience and improving performance outcomes. SCRM involves the identification, assessment, and mitigation of risks that may disrupt the flow of products, information, and resources across the supply chain (Fan & Stevenson, 2018; Kaluku et al., 2025). Recent studies emphasize that effective SCRM contributes not only to operational stability but also to long-term supply chain viability under disruption conditions (Chu et al., 2020; Colicchia & Strozzi, 2012; Gurtu & Johny, 2021). Recent evidence published in Production further highlights the importance of connecting risk management with underlying supply chain capabilities. Al-kharabsheh et al. (2026), for example, identify supply chain collaboration as a critical enabler of effective risk management and emphasize the roles of information sharing, resource sharing, collaborative communication, and joint knowledge creation in supporting sustainable supply chain outcomes. This perspective suggests that SCRM should not be viewed solely as a standalone practice but also as an operational capability whose effectiveness depends on the relational and managerial capabilities available within the supply chain. However, how such capabilities enable SCRM and ultimately translate into performance outcomes remains insufficiently understood, particularly in smallholder agricultural systems.
Recent literature highlights that supply chain capabilities, such as collaboration and decision-making, play a crucial role in managing risks and enhancing performance. Supply Chain Collaboration (SCC) facilitates information sharing, joint planning, and trust among actors, which are essential for reducing uncertainty and improving coordination efficiency (Cao & Zhang, 2011; Osei & Asante-Darko, 2024; Zhuo et al., 2025). Furthermore, relational competencies have been shown to strengthen supply chain resilience and performance by enabling coordinated responses to disruptions (Bakhshi Movahed et al., 2023). On the other hand, Supply Chain Decision-Making (SCDM) capability enables supply chain actors to respond effectively to dynamic and uncertain environments through timely, flexible, and informed actions (Koot et al., 2021; Manuj & Sahin, 2011). Despite their recognized importance, limited studies have integrated these capabilities into a unified framework to explain how they jointly influence SCRM and Farm Performance (FP), particularly in developing country contexts.
To address this gap, this study adopts Resource-Based Theory (RBT) as the primary theoretical foundation. RBT posits that organizational performance is determined by the effective deployment of valuable, rare, inimitable, and non-substitutable resources and capabilities (Barney et al., 2011; Hitt et al., 2016). In this study, SCC is conceptualized as a relational capability that enhances access to information and coordination among supply chain actors. Meanwhile, SCDM is conceptualized as a managerial capability that encompasses the ability to evaluate alternatives, allocate resources, and coordinate responses across the supply chain under conditions of uncertainty. These capabilities contribute to the development of SCRM as an operational capability, which ultimately improves FP as the desired outcome.
Although prior studies have advanced the understanding of supply chain risk management and its relationship with organizational performance across different settings (Kaluku et al., 2025; Manhart et al., 2020), empirical evidence from smallholder agriculture remains limited. Recent studies in Production have contributed important insights into risk identification and mitigation in food supply chains (Medeiros et al., 2025a, b) and into the role of collaboration in supporting effective supply chain risk management (Al-kharabsheh et al., 2026). However, these developments leave an important question unresolved: how relational and managerial capabilities jointly translate into farm performance through risk management in resource-constrained smallholder supply chains. In particular, limited empirical research has simultaneously examined supply chain collaboration and decision-making as distinct antecedent capabilities, positioned SCRM as the operational mechanism connecting these capabilities to performance, and tested these relationships within a unified Resource-Based Theory framework. This gap is especially relevant in developing agricultural regions characterized by fragmented coordination, resource constraints, and high exposure to environmental and market uncertainty. Accordingly, this study develops a resource–capability–performance framework to examine how SCC and SCDM influence SCRM and FP, including the mediating role of SCRM. Accordingly, this study aims to address the following research questions
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How does SCC influence SCRM and FP?
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How does SCDM affect SCRM and FP?
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Does SCRM mediate the relationship between SCC, SCDM, and FP?
This study contributes to the literature in several important ways. First, rather than examining supply chain collaboration, decision-making, and risk management as independent determinants of performance, this study develops an integrated resource–capability–performance framework grounded in Resource-Based Theory (RBT). Specifically, supply chain collaboration is conceptualized as a relational capability, supply chain decision-making as a managerial capability, and supply chain risk management as an operational capability through which resources are transformed into performance outcomes. Second, the study explains the mechanism through which relational and managerial capabilities generate value under conditions of uncertainty by demonstrating the mediating role of SCRM. Third, the study provides empirical evidence from smallholder corn farming, a context characterized by resource constraints, fragmented supply chains, and high exposure to risk that remains underrepresented in the supply chain management literature.
2. Theory and hypothesis
2.1. Resource based theory
Resource-Based Theory constitutes a fundamental perspective in strategic management that explains how organizations achieve and sustain superior performance through the effective utilization of resources and capabilities. Rooted in the Resource-Based View (RBV), RBT posits that organizational performance is determined by resources that are valuable, rare, inimitable, and non-substitutable (VRIN) (Barney, 1991; Barney et al., 2021). More recent developments emphasize that it is not merely the possession of resources, but their strategic deployment and transformation into capabilities that generate economic value and competitive advantage (Barney et al., 2021).
Within this perspective, firm resources include tangible and intangible assets such as knowledge, information, relationships, and organizational processes, while capabilities refer to the firm’s ability to integrate and utilize these resources effectively (Tate, 1992). Importantly, RBT highlights that resources alone do not create value unless they are configured and deployed through appropriate capabilities (Kozlenkova et al., 2014). This distinction is particularly relevant in dynamic and uncertain environments, where the ability to adapt and respond to disruptions becomes critical for maintaining performance (Teece, 2007).
In the context of supply chain management, RBT provides a useful lens to understand how internal and relational resources contribute to operational capabilities such as SCRM. Agricultural supply chains, especially those involving smallholder farmers, are characterized by high levels of uncertainty and vulnerability to disruptions, making the effective management of resources and capabilities essential (Kafi et al., 2025). From this perspective, SCC can be conceptualized as a relational capability that enables supply chain actors to mobilize information, trust, and coordination across interorganizational relationships (Mahmud et al., 2021). Meanwhile, SCDM represents a managerial capability that enables timely, flexible, and informed responses to dynamic conditions (Hao & Demir, 2023).
These resources and capabilities collectively contribute to the development of SCRM as an operational capability. SCRM reflects the ability of supply chain actors to identify, assess, and mitigate risks, thereby ensuring the continuity of supply chain activities and improving system resilience (Fan & Stevenson, 2018). In turn, effective SCRM enhances FP by reducing disruptions, improving efficiency, and stabilizing production and income outcomes.
Accordingly, this study adopts a resource–capability–performance framework grounded in RBT. SCC and SCDM are conceptualized as key resources and capabilities that are hypothesized to support the development of SCRM, which is in turn proposed to generate value in the form of improved FP. This framework guides the formulation of the research hypotheses and provides a lens for examining how smallholder farmers may leverage internal and relational strengths to manage supply chain risks and achieve sustainable performance outcomes.
2.2. Supply chain collaboration, supply chain decision-making and farm performance
Farm performance in smallholder agricultural systems is influenced not only by external market conditions but also by the ability of supply chain actors to effectively utilize their internal and relational resources. From the perspective of Resource-Based Theory (RBT), organizational performance is driven by the strategic deployment of valuable resources and capabilities that enhance efficiency, adaptability, and competitiveness (Barney et al., 2021).
Supply chain collaboration facilitates coordination among supply chain actors through information sharing, joint planning, and mutual problem-solving. Collaboration improves supply chain visibility and reduces uncertainty, allowing actors to respond more effectively to fluctuations in demand and supply conditions (Cao & Zhang, 2011). Empirical evidence suggests that higher levels of collaboration are associated with improved operational efficiency, reduced transaction costs, and enhanced performance outcomes (Zhuo et al., 2025). In smallholder farming systems, collaboration with traders, cooperatives, and institutional actors facilitates access to market information, stabilizes distribution channels, and ultimately contributes to improved farm productivity and income.
In addition to collaboration, supply chain decision-making determines how effectively actors respond to dynamic and uncertain environments. Decision-making capability reflects the ability to make timely, flexible, and informed decisions regarding production planning, input utilization, and market participation. Prior studies indicate that effective decision-making enhances responsiveness and improves performance by enabling better alignment between supply and demand (Koot et al., 2021; Manuj & Sahin, 2011). In the context of smallholder agriculture, where uncertainty is high and resources are limited, the ability to make adaptive decisions is essential for minimizing losses and maximizing output.
From an RBT perspective, prior literature suggests that both collaboration and decision-making are strategic resources and capabilities that can directly contribute to value creation when effectively deployed (Barney et al., 2021; Cao & Zhang, 2011). Based on this theoretical reasoning, this study proposes that these capabilities may enable smallholder farmers to improve operational efficiency, reduce uncertainty, and enhance overall farm performance. However, whether this direct relationship holds empirically in the smallholder agricultural context remains an open empirical question that this study aims to examine. It should be noted that the empirical results, presented in later sections, partially challenge this assumption, suggesting that the relationship between supply chain capabilities and performance may be more nuanced than a simple direct pathway. Based on the above arguments, the following hypotheses are proposed:
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H1. SCC has a positive effect on FP.
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H2. SCDM has a positive effect on FP.
2.3. Supply chain risk management and farm performance
Supply chain risk management has become a critical capability for improving performance, particularly in environments characterized by high uncertainty such as agricultural supply chains. SCRM refers to the systematic process of identifying, assessing, and mitigating risks that may disrupt the flow of products, information, and resources across the supply chain (Fan & Stevenson, 2018). In smallholder farming systems, where exposure to environmental variability, market volatility, and coordination challenges is high, effective risk management plays a crucial role in maintaining operational stability and performance outcomes.
From the perspective of Resource-Based Theory, SCRM can be conceptualized as an operational capability that enables organizations to transform resources into value-generating outcomes. While resources such as collaboration and decision-making provide the foundation, it is through capabilities like SCRM that these resources are effectively deployed to manage uncertainty and enhance performance (Kozlenkova et al., 2014). This aligns with the view that capabilities are essential for integrating and reconfiguring resources in response to dynamic environmental conditions (Teece, 2007).
Empirical studies have demonstrated that effective SCRM contributes to improved operational efficiency, adaptability, and resilience. For instance, SCRM enhances the ability of supply chain actors to anticipate disruptions, respond proactively, and recover from adverse events, thereby ensuring continuity in production and distribution processes (Ho et al., 2015; Tang, 2006). Additionally, risk management practices have been shown to reduce operational costs, improve service levels, and stabilize performance outcomes under uncertain conditions (Manuj & Mentzer, 2008). Recent evidence published further reinforces the performance-oriented role of risk management. Leite et al. (2024) show that explicitly linking supply risks to supply chain performance criteria can support the prioritization of mitigation actions and help organizations reduce the performance consequences of difficult-to-manage risks.
In the context of smallholder corn farming, effective SCRM enables farmers to better manage production risks, price fluctuations, and supply uncertainties. By identifying potential risks and implementing appropriate mitigation strategies, farmers can minimize losses, optimize resource utilization, and improve productivity and income stability. Consequently, SCRM not only reduces vulnerability but also creates value by enhancing overall farm performance. Based on the above arguments, the following hypothesis is proposed:
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H3. SCRM has a positive effect on FP.
2.4. Supply chain collaboration, supply chain decision-making and supply chain risk management
Effective supply chain risk management depends on the availability of collaborative relationships and decision-making capabilities that enable supply chain actors to respond to uncertainty. Collaboration enhances visibility and coordination across the supply chain, while decision-making capability supports timely and appropriate responses to emerging risks.
Supply chain collaboration enhances the ability of supply chain actors to manage risks by improving information sharing, coordination, and trust. Collaborative relationships increase visibility across the supply chain, allowing actors to identify potential disruptions earlier and respond more effectively (Cao & Zhang, 2011). Prior studies have shown that collaboration strengthens supply chain resilience and risk mitigation by facilitating joint problem-solving and coordinated responses to uncertainty (Bakhshi Movahed et al., 2023). Recent evidence from Production further supports this capability-based relationship. Al-kharabsheh et al. (2026) identify supply chain collaboration as a critical enabler of effective risk management, emphasizing information sharing, resource sharing, collaborative communication, and joint knowledge creation as important mechanisms through which collaborative relationships strengthen supply chain responses to uncertainty. In agricultural supply chains, collaboration among farmers, traders, and institutions is particularly important for reducing information asymmetry and improving the management of demand and supply uncertainties.
In addition to collaboration, supply chain decision-making represents a critical managerial capability that influences how risks are identified and managed. Effective decision-making enables supply chain actors to assess risks accurately, select appropriate mitigation strategies, and respond to dynamic conditions in a timely manner. Studies indicate that decision-making capability enhances supply chain responsiveness and flexibility, which are essential for managing disruptions and uncertainty (Hao & Demir, 2023). In smallholder farming systems, the ability to make adaptive decisions is crucial due to limited resources and high exposure to environmental and market risks.
Both SCC and SCDM contribute to the development of SCRM as an operational capability. Collaboration provides access to shared information and coordinated actions, while decision-making ensures that this information is effectively utilized to manage risks. Together, these capabilities enable supply chain actors to identify vulnerabilities, implement mitigation strategies, and maintain continuity in supply chain operations. Based on the above arguments, the following hypotheses are proposed:
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H4. SCC has a positive effect on SCRM.
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H5. SCDM has a positive effect on SCRM.
2.5. The mediating roles of supply chain risk management
While supply chain collaboration and supply chain decision-making have been shown to directly influence farm performance, their impact is often not immediate and may depend on the presence of intermediate capabilities that translate these resources into tangible outcomes. Supply chain risk management serves as an important mechanism through which supply chain capabilities are translated into performance outcomes. Effective risk management enables supply chain actors to identify, assess, and mitigate disruptions, thereby enhancing operational stability and performance.
SCRM represents an operational capability that enables supply chain actors to identify, assess, and mitigate risks, thereby ensuring continuity and stability in supply chain activities (Fan & Stevenson, 2018). As highlighted in prior studies, the effectiveness of SCRM depends on the availability of relevant resources, such as access to information and the ability to make adaptive decisions (Chu et al., 2020). This rather suggests that SCRM itself requires SCC and SCDM as enabling inputs to function effectively, without these relational and managerial foundations, risk management practices may lack the informational basis and adaptive capacity needed to generate performance improvements.
SCC enhances SCRM by facilitating information sharing, transparency, and coordinated actions among supply chain actors. Through collaboration, actors can better anticipate potential disruptions, share risk-related information, and jointly implement mitigation strategies (Cao & Zhang, 2011). This improved coordination strengthens the effectiveness of SCRM, which in turn contributes to better performance outcomes.
Similarly, SCDM strengthens SCRM by enabling timely and informed responses to uncertainty. Effective decision-making allows supply chain actors to evaluate risks, select appropriate strategies, and adapt to changing conditions (Hao & Demir, 2023). In smallholder farming systems, where uncertainty is high and resources are limited, decision-making capability plays a crucial role in ensuring that risk management practices are implemented effectively.
Empirical studies have demonstrated that SCRM improves operational efficiency, reduces disruptions, and enhances resilience, all of which contribute to improved performance outcomes (Kaluku et al., 2025). Therefore, SCRM functions as the operational capability that translates relational and managerial capabilities into performance outcomes. Through risk identification, assessment, and mitigation activities, collaborative relationships and decision-making processes are converted into concrete actions that reduce uncertainty and improve farm performance. Based on these arguments, this study proposes that SCRM mediates the relationships between SCC, SCDM, and FP. Accordingly, the following hypotheses are proposed:
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H6. SCRM mediates the relationship between SCC and FP.
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H7. SCRM mediates the relationship between SCDM and FP.
Figure 1 delineates the conceptual framework derived from prior research.
3. Research design and methodology
This study adopts a quantitative, explanatory research design to empirically examine the relationships among supply chain collaboration, supply chain decision-making, supply chain risk management, and farm performance in smallholder corn farming. The research model is grounded in Resource-Based Theory (RBT), which emphasizes the role of strategic resources and capabilities in shaping performance outcomes (Barney, 1991). The research design aims to test both direct and indirect relationships among the constructs, with particular emphasis on the mediating role of SCRM in the relationship between supply chain collaboration, decision-making, and farm performance. This explanatory approach is appropriate for examining causal relationships and validating the theoretical mechanisms proposed in the conceptual model.
The constructs and measurement indicators are adapted to reflect the operational realities of smallholder corn farmers in Gorontalo Province, Indonesia. These farmers operate within agricultural supply chains characterized by high uncertainty, limited access to resources, and fragmented coordination among actors. Accordingly, the measurement items capture farmers’ capabilities in building collaborative relationships, making adaptive decisions under uncertainty, managing supply chain risks, and achieving stable production and income outcomes. By employing a structured survey design and quantitative data analysis, this study aims to provide empirical evidence on how resource-based capabilities are transformed into performance outcomes through supply chain risk management in smallholder agricultural systems.
Partial Least Squares Structural Equation Modeling (PLS-SEM) was employed to test the proposed model. PLS-SEM was selected over covariance-based SEM (CB-SEM) for several reasons. First, PLS-SEM is particularly appropriate for predictive and explanatory research where the primary goal is to assess the explanatory power of the model rather than confirm a pre-specified theory (Hair et al., 2019). Second, PLS-SEM performs well with non-normal data distributions, which is common in survey-based agricultural studies. Third, the method accommodates complex models involving multiple constructs and mediating relationships, making it consistent with the objectives of this study.
3.1. Sampling
The respondents in this study are smallholder corn farmers in Gorontalo Province, Indonesia. The unit of analysis is the farming household, as farmers act as primary decision-makers in production, marketing, and supply chain activities. A total of 380 valid responses were collected through a structured survey. This sample size exceeds the minimum requirement for PLS-SEM analysis and ensures adequate statistical power. Data were screened for completeness and consistency, and only valid responses were included in the analysis. Purposive sampling was employed to select respondents who (1) are actively engaged in corn farming, (2) participate in marketing or distribution activities, and (3) are involved in decision-making processes.
Most respondents are within the productive age range and have several years of farming experience. The majority operate at a smallholder scale and are embedded in fragmented supply chains characterized by limited coordination and high exposure to risk. Overall, the sample provides a reliable basis for examining the relationships among supply chain collaboration, decision-making, supply chain risk management, and farm performance.
3.2. Measures
All constructs in this study were measured using validated scales adapted from prior research to ensure content and construct validity. The study employs four main constructs: supply chain collaboration (SCC), supply chain decision-making (SCDM), supply chain risk management (SCRM), and farm performance (FP).
Supply chain collaboration (SCC) was measured using items adapted from Cao & Zhang (2011), capturing key dimensions such as information sharing, joint planning, and coordination among supply chain actors. These items reflect the extent to which farmers engage in collaborative relationships to improve supply chain effectiveness. Supply chain decision-making (SCDM) was measured using indicators adapted from Manuj & Sahin (2011), focusing on the ability to make timely, flexible, and informed decisions under uncertain conditions. The measurement captures farmers’ capability to respond to changes in production, market conditions, and resource availability. Supply chain risk management (SCRM) was measured using items adapted from Fan & Stevenson (2018), which assess the extent to which farmers identify, evaluate, and mitigate risks within the supply chain. These items reflect key processes such as risk identification, risk assessment, and implementation of mitigation strategies. Farm performance (FP) was measured using both financial and non-financial indicators adapted from Chowdhury et al. (2019). The measurement includes aspects such as productivity, income stability, and operational efficiency, which are relevant to smallholder farming contexts.
All items were measured using a five-point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The measurement instruments were adapted to reflect the operational realities of smallholder corn farmers, ensuring relevance to agricultural supply chain conditions characterized by uncertainty, limited resources, and fragmented coordination. The complete list of questionnaire items for all constructs is provided in Appendix A.
3.3. Validity and reliability
As illustrated in Table 1, the reliability and construct validity of the four constructs, namely supply chain collaboration, supply chain decision-making, supply chain risk management, and farm performance, were assessed using Cronbach’s alpha, composite reliability (rho_a), and average variance extracted (AVE).
As presented in Table 1, all indicator loadings exceed the recommended threshold of 0.70, indicating that the measurement items adequately represent their respective constructs. The Cronbach’s alpha values range from 0.876 to 0.923, while composite reliability values range from 0.877 to 0.924, confirming satisfactory internal consistency. Furthermore, all AVE values exceed the recommended threshold of 0.50, ranging from 0.642 to 0.685, indicating adequate convergent validity. Overall, the measurement model satisfies the recommended criteria for reliability and convergent validity and is therefore suitable for subsequent structural model analysis.
Discriminant validity was assessed using the Fornell and Larcker criterion by comparing the square root of the average variance extracted (AVE) for each construct with the correlations between constructs. As shown in Table 2, the square root of AVE for each construct is greater than its corresponding inter-construct correlations. This indicates that each construct is empirically distinct from the others, confirming that the discriminant validity of the measurement model is satisfactory (Fornell & Larcker, 1981).
In addition to the Fornell–Larcker criterion, discriminant validity was further assessed using the Heterotrait–Monotrait Ratio (HTMT). As shown in Table 3, all HTMT values ranged from 0.616 to 0.799, remaining below the recommended threshold of 0.90 (Henseler et al., 2015). These results further confirm the discriminant validity of the measurement model.
Because all data were collected through a single survey instrument, the potential for common method bias was considered. To minimize this risk, respondents were assured of anonymity and confidentiality, and measurement items were adapted from previously validated scales. In addition, collinearity diagnostics were examined, with VIF values ranging from 1.799 to 3.224, suggesting that collinearity issues are unlikely to affect the estimated relationships among the constructs. Therefore, common method bias is unlikely to substantially influence the study findings.
Model fit was assessed using the standardized root mean square residual (SRMR). The SRMR values were 0.067 for the saturated model and 0.071 for the estimated model, both of which are below the recommended threshold of 0.08. These results indicate an acceptable level of model fit and suggest that the proposed model adequately represents the observed data.
4. Analysis and results
This section presents the empirical findings from the PLS-SEM analysis conducted on data collected from smallholder corn farmers in Gorontalo Province, Indonesia. The analysis aims to examine the relationships among supply chain collaboration, supply chain decision-making, supply chain risk management, and farm performance. The results are presented in a structured manner to test the proposed hypotheses. First, the direct effects of supply chain collaboration and supply chain decision-making on farm performance are examined. Second, the effect of supply chain risk management on farm performance is analyzed. Third, the relationships between supply chain collaboration, supply chain decision-making, and supply chain risk management are evaluated. Finally, the mediation analysis is conducted to assess the indirect effects of supply chain collaboration and decision-making on farm performance through supply chain risk management.
4.1. Effect of SCC and SCDM on FP
As shown in Table 4, the results indicate that supply chain collaboration (SCC) has a positive and statistically significant effect on farm performance (FP) (β = 0.399, t = 4.418, p < 0.05). This finding supports H1. In contrast, the effect of supply chain decision-making (SCDM) on farm performance is not statistically significant (β = -0.105, t = 1.250, p > 0.05). Therefore, H2 is not supported.
As shown in Table 5, Effect sizes (f2) were examined to assess the relative contribution of each exogenous construct to the endogenous constructs. The results indicate that supply chain collaboration has a medium effect on farm performance (f2 = 0.194) and a large effect on supply chain risk management (f2 = 0.349). Supply chain decision-making exhibits small effects on both farm performance (f2 = 0.079) and supply chain risk management (f2 = 0.093). In contrast, supply chain risk management demonstrates a large effect on farm performance (f2 = 0.413), indicating its substantial role in explaining performance variation among smallholder farmers.
As presented in Table 6, predictive relevance was assessed using the PLSpredict procedure. The results indicate positive Q2predict values for all endogenous constructs, with values of 0.625 for supply chain risk management and 0.674 for farm performance. Since all Q2predict values are greater than zero, the model demonstrates satisfactory predictive relevance and possesses adequate predictive capability for the endogenous constructs.
4.2. Effect of SCRM on FP
As shown in Table 4, supply chain risk management (SCRM) has a positive and statistically significant effect on farm performance (FP) (β = 0.542, t = 5.257, p < 0.05). This result supports H3. The magnitude of the path coefficient indicates a relatively strong relationship compared to other direct effects in the model. The t-statistic exceeds the critical value of 1.96, and the p-value is below 0.05, confirming the statistical significance of the relationship.
4.3. Effect of SCC and SCDM on SCRM
As shown in Table 4, supply chain collaboration (SCC) has a positive and statistically significant effect on supply chain risk management (SCRM) (β = 0.401, t = 2.863, p < 0.05). This result supports H4. Similarly, supply chain decision-making (SCDM) also shows a positive and statistically significant effect on SCRM (β = 0.298, t = 2.059, p < 0.05), supporting H5. The t-statistics for both relationships exceed the critical value of 1.96, and the p-values are below 0.05, indicating that both effects are statistically significant. In terms of magnitude, the effect of SCC on SCRM is slightly stronger than that of SCDM.
4.4. Mediating effect of SCRM
To examine the mediating role of supply chain risk management (SCRM) between supply chain collaboration (SCC), supply chain decision-making (SCDM), and farm performance (FP), this study evaluates the mediation effect based on the significance of both direct and indirect relationships using PLS-SEM.
The mediation results are presented in Table 4. The findings indicate that the indirect effect of SCC on FP through SCRM is positive and statistically significant (β = 0.218, p < 0.05). This suggests that SCRM mediates the relationship between SCC and FP, thereby supporting H6. Given that the direct effect of SCC on FP remains significant, SCRM can be interpreted as a partial mediator in this relationship. Similarly, the results show that the indirect effect of SCDM on FP through SCRM is positive and statistically significant (β = 0.162, p < 0.05), supporting H7. Although the direct effect of SCDM on FP is not significant, the indirect effect through SCRM is significant. This indicates that SCRM fully mediates the relationship between SCDM and FP. Furthermore, a bootstrapping procedure was conducted with 5,000 resamples to assess the significance of the indirect effects. The results confirm that both indirect paths are statistically significant, as indicated by t-statistics greater than 1.96 and p-values below 0.05.
Table 7 summarizes the results of hypothesis testing in this study, which examines the relationships among SCC, SCDM, SCRM, and FP in the context of smallholder corn farming in Gorontalo Province, Indonesia. The results indicate that SCC has a positive and significant effect on FP, supporting H1. In contrast, SCDM does not have a significant effect on FP, and therefore H2 is not supported. SCRM is found to have a positive and significant effect on FP, supporting H3. Furthermore, both SCC and SCDM have significant positive effects on SCRM, supporting H4 and H5. In terms of mediation, SCRM is found to partially mediate the relationship between SCC and FP, supporting H6. In addition, SCRM fully mediates the relationship between SCDM and FP, supporting H7.
Figure 2 presents the estimated PLS-SEM model with standardized path coefficients. The structural model shows that supply chain collaboration (SCC) has a positive effect on both supply chain risk management (SCRM) (β = 0.401) and farm performance (FP) (β = 0.399). Supply chain decision-making (SCDM) has a positive effect on SCRM (β = 0.298), but its direct effect on FP is negative and not statistically significant (β = -0.105). Furthermore, SCRM has a strong positive effect on FP (β = 0.542), indicating its central role within the structural model. The model also shows that SCC and SCDM jointly explain the variance of SCRM, while SCC, SCDM, and SCRM collectively explain the variance of FP. The R-squared values indicate that the model explains 44.4% of the variance in SCRM and 60.4% of the variance in FP. Overall, the structural model illustrates the relationships among the constructs and highlights the relative strength of the path coefficients in the proposed framework.
5. Discussion
This study develops a research model grounded in Resource-Based Theory (RBT) to examine the relationships among supply chain collaboration, supply chain decision-making, supply chain risk management, and farm performance in smallholder corn farming. The findings reflect the specific context of smallholder farmers in Gorontalo Province, Indonesia, and therefore should be interpreted with consideration of local agricultural, economic, and institutional conditions.
The results indicate that supply chain collaboration has a significant positive effect on farm performance, highlighting the importance of relational resources in improving agricultural outcomes. In smallholder farming systems, collaboration among farmers, traders, and intermediaries facilitates access to market information, reduces uncertainty, and enhances coordination. These findings are consistent with the RBT perspective, which emphasizes that relational resources can create value when effectively utilized within the supply chain. This finding is also consistent with recent evidence published in Production. Al-kharabsheh et al. (2026) show that supply chain collaboration positively contributes to sustainability outcomes and identify collaboration as an important enabler of effective risk management. Although their study focuses on manufacturing firms and sustainability outcomes rather than smallholder farm performance, the two studies converge in showing that collaborative relationships can create value by improving coordination and strengthening responses to supply chain uncertainty.
In contrast, supply chain decision-making does not show a significant direct effect on farm performance. As revealed by the questionnaire items, the SCDM construct captures farmers' ability to make timely decisions, evaluate alternatives, and allocate resources under uncertainty. However, in the smallholder context, these individual decision-making activities are frequently constrained by structural factors such as incomplete market information, limited bargaining power, and fragmented supply chain coordination. As a result, decision-making capability may only generate tangible performance improvements when it is channeled through operational risk management practices, as supported by the significant full mediation of SCRM between SCDM and FP . In smallholder agricultural contexts, decisions are often constrained by external factors such as price volatility, climate uncertainty, and limited access to resources. As a result, the effectiveness of decision-making may depend on the presence of supporting capabilities that enable its implementation.
From a Resource-Based Theory perspective, this finding suggests that managerial capabilities do not automatically generate performance advantages unless they are translated into operational capabilities. Although farmers may possess the ability to evaluate alternatives and make decisions under uncertainty, these capabilities alone may not directly influence productivity or income outcomes. In resource-constrained agricultural environments, the benefits of decision-making are realized only when decisions are converted into concrete risk management actions, such as anticipating disruptions, mitigating production risks, and responding to market uncertainty. The significant indirect effect through SCRM supports this interpretation by indicating that decision-making contributes to performance primarily through its role in enabling effective risk management practices. Therefore, the findings reinforce the view that managerial capabilities create value not in isolation but through their deployment in operational processes.
The results further demonstrate that supply chain risk management plays a critical role in improving farm performance. As an operational capability, SCRM enables farmers to identify, assess, and mitigate risks related to production, market conditions, and supply chain disruptions. This finding aligns with RBT, which highlights the importance of transforming resources into capabilities that directly contribute to performance outcomes. Moreover, the mediation analysis reveals that SCRM serves as an important mechanism linking supply chain capabilities to performance. The partial mediation observed in the relationship between collaboration and performance indicates that collaboration can directly improve outcomes while also enhancing performance through risk management. The full mediation effect further indicates that decision-making capability alone is insufficient to improve farm performance. Rather, its value emerges when decision outcomes are embedded in risk management practices that guide operational responses to uncertainty. This finding highlights the importance of operational capability development in transforming managerial capabilities into tangible performance outcomes. This result is consistent with recent evidence in Production linking risk management to supply chain performance. Leite et al. (2024) demonstrate that understanding how supply risks affect performance criteria can support more effective mitigation priorities and reduce the performance consequences of difficult-to-manage risks. Similarly, the systematic review by Medeiros et al. (2025b) shows that food supply chains face multiple forms of disruption and that proactive, reactive, and concurrent mitigation strategies are important for strengthening resilience, efficiency, and security. In the present smallholder context, these broader risk-management principles are reflected in the strong positive effect of SCRM on farm performance.
These findings reflect the structural characteristics of smallholder corn farming in Gorontalo, where supply chains are often fragmented and coordination among actors is limited. Farmers typically rely on intermediaries for market access and face significant uncertainty related to weather conditions and price fluctuations. In this context, collaboration becomes a critical resource for accessing information and coordinating activities, while risk management serves as a key capability for stabilizing production and income. Furthermore, the results highlight that farm performance is not solely determined by individual farmer capabilities but is also influenced by the broader supply chain environment. Disruptions at any point in the supply chain, such as delays in distribution or price instability, can directly affect farmers’ outcomes. This interpretation is consistent with Medeiros et al. (2025a), who demonstrate that risks in food supply chains are interdependent rather than isolated and that macro-level and natural risks can influence demand, supply, and operational risks. Such interdependencies are particularly relevant to smallholder farming because farmers have limited control over many upstream and downstream disruptions, reinforcing the need for coordinated risk identification and mitigation across the broader supply chain. Conversely, improved coordination and risk management across the supply chain can enhance overall performance. Rather than providing universally generalizable conclusions, this study emphasizes the importance of context-specific supply chain dynamics in smallholder agriculture. The findings contribute to a better understanding of how resource-constrained farmers can leverage collaboration and risk management capabilities to improve performance under conditions of uncertainty. These insights may also be relevant for other agricultural systems in developing regions that share similar structural characteristics, such as limited resources, fragmented supply chains, and high exposure to risk.
5.1. Theoretical contribution
The primary theoretical contribution of this study lies in explaining how different types of supply chain capabilities interact to generate performance outcomes in smallholder agricultural systems. While prior studies have typically examined collaboration, decision-making, and risk management as separate antecedents of performance, this study conceptualizes them as interrelated capabilities operating within a resource–capability–performance chain. By positioning supply chain risk management as the operational capability through which relational and managerial capabilities are converted into performance outcomes, the study provides a more integrated explanation of capability deployment under uncertainty.
This study contributes to the literature by extending the application of Resource-Based Theory (RBT) in the context of agricultural supply chains, particularly smallholder farming systems. While prior research has emphasized the role of resources and capabilities in improving organizational performance, limited studies have examined how relational and managerial capabilities interact to influence performance through risk management mechanisms, especially in smallholder agriculture. First, this study contributes to the supply chain management literature by identifying key drivers of farm performance from both relational and capability perspectives. Specifically, it demonstrates that supply chain collaboration and supply chain risk management play significant roles in enhancing farm performance. This finding supports previous studies suggesting that organizational performance is influenced by the ability to manage disruptions and maintain operational stability under uncertain conditions (Dolgui & Ivanov, 2021). Second, this study refines the understanding of capability development within the RBT framework by distinguishing between direct and indirect effects of supply chain capabilities. The results show that supply chain collaboration has both direct and indirect effects on farm performance, while supply chain decision-making does not have a direct effect but operates indirectly through supply chain risk management. This finding provides new insight into the role of managerial capability, suggesting that decision-making contributes to performance only when it is effectively translated into operational capabilities such as risk management. This distinction also complements the capability-based discussion developed in Production. Barhmi (2023) demonstrates that supply chain capabilities can influence performance through both direct and indirect mechanisms, showing how risk management, robustness, and resilience capabilities interact in generating agility performance under disruption conditions. Although Barhmi (2023) conceptualizes SCRM as a dynamic capability and robustness and resilience as operational capabilities, the present study adopts a different capability configuration by positioning collaboration as a relational capability, decision-making as a managerial capability, and SCRM as the operational mechanism linking these antecedent capabilities to farm performance. The present findings therefore extend this capability-based discussion to smallholder agriculture and show that different antecedent capabilities may follow distinct value-creation pathways: collaboration generates both direct and SCRM-mediated performance effects, whereas decision-making generates performance benefits only through SCRM.
Third, this study contributes to the literature by positioning supply chain risk management (SCRM) as a central capability that links resources to performance outcomes. By demonstrating both partial and full mediation effects, the study highlights the importance of SCRM as a mechanism through which relational and managerial capabilities are transformed into value. This finding enriches existing research on supply chain risk management, which has often treated it as an independent construct rather than an integrative capability within the resource-capability-performance framework. Finally, this study provides empirical evidence from the context of smallholder corn farming in a developing region, which remains underrepresented in the supply chain literature. By focusing on a resource-constrained and high-uncertainty environment, this study extends the applicability of RBT beyond traditional firm-level analysis to agricultural systems characterized by fragmented structures and limited coordination.
5.2. Managerial implications
The findings of this study provide several important managerial implications for improving farm performance in smallholder corn farming. First, the results highlight the critical role of supply chain collaboration in enhancing farm performance. Smallholder farmers should strengthen collaborative relationships with key supply chain actors such as traders, cooperatives, and input suppliers. Improving information sharing, coordination, and trust can help farmers reduce uncertainty, access market information, and improve distribution efficiency. Strengthening collaboration mechanisms, both formal and informal, can therefore directly contribute to better productivity and income outcomes.
Second, although supply chain decision-making does not show a direct effect on farm performance, its significant indirect effect through supply chain risk management indicates that decision-making should be aligned with risk management practices. Farmers need to improve their ability to make adaptive and timely decisions, particularly in response to price fluctuations, climate variability, and input availability. However, these decisions must be supported by structured risk management processes to generate tangible performance improvements. Third, supply chain risk management plays a central role in improving farm performance. Farmers should focus on developing practical risk management capabilities, such as identifying potential risks, evaluating their impact, and implementing mitigation strategies. This includes managing production risks, market risks, and distribution uncertainties. Strengthening SCRM can help farmers stabilize production, reduce losses, and maintain more consistent income levels.
Furthermore, the results suggest that improving farm performance requires an integrated approach that combines collaboration, decision-making, and risk management. Rather than relying on individual capabilities alone, farmers need to develop coordinated strategies that connect these elements within the supply chain. Finally, policymakers and supporting institutions should play an active role in facilitating collaboration and strengthening risk management capabilities among farmers. This can be achieved through training programs, access to market information systems, and the development of farmer groups or cooperatives. Such support can help smallholder farmers overcome structural limitations and improve their overall performance within the supply chain.
From an operational perspective, the findings suggest several priority actions for stakeholders involved in smallholder corn supply chains. Farmer groups and cooperatives should establish more structured information-sharing mechanisms to improve visibility regarding input availability, market demand, and price fluctuations. Agricultural extension agencies can support decision-making quality by providing timely market intelligence, production planning guidance, and risk awareness programs. In addition, risk management practices should be integrated into routine farm operations through activities such as production scheduling, diversification of input sources, contingency planning, and regular monitoring of market and environmental risks. These actions may help translate collaborative relationships and decision-making capabilities into tangible performance improvements.
5.3. Limitations and future research
Despite its contributions, this study has several limitations that should be acknowledged. First, this study is conducted within the context of smallholder corn farmers in Gorontalo Province, Indonesia. The findings reflect the characteristics of agricultural supply chains that are resource-constrained, fragmented, and highly exposed to environmental and market uncertainties. Therefore, the generalizability of the results may be limited to similar smallholder farming systems. Future research is encouraged to examine different agricultural sectors or regions to enhance the external validity of the proposed model.
Second, this study focuses on a limited set of constructs, namely supply chain collaboration, supply chain decision-making, supply chain risk management, and farm performance. Although these variables capture key aspects of supply chain capability, other relevant factors such as institutional support, market access, digital technology adoption, and farmer capacity may also influence performance outcomes. Future studies should consider incorporating additional variables to provide a more comprehensive understanding of agricultural supply chain performance.
Third, this study employs a cross-sectional research design with self-reported survey data collected from a single respondent group at one point in time. Although the respondents were directly involved in farming and supply chain activities, the measures reflect individual perceptions rather than objective performance indicators, which introduces the possibility of response bias. Future studies are encouraged to adopt longitudinal designs and incorporate objective performance indicators, such as yield records, income documentation, or supply chain transaction data, to better capture how supply chain capabilities evolve over time and to enhance the validity of the findings.
Finally, this study adopts Resource-Based Theory as the primary theoretical lens, which, while well-established, has been criticized for its relatively static perspective that may not fully capture the dynamic nature of agricultural supply chains. This static assumption may have influenced the interpretation of the results, particularly in terms of how capability–performance relationships are framed within a fixed resource configuration rather than as evolving processes. Future research may integrate Dynamic Capabilities Theory, contingency theory, or relational theory to provide a more comprehensive and adaptive explanation of how supply chain capabilities evolve in response to environmental changes. Future studies may also explore potential heterogeneity among farmer groups through multi-group analysis to examine whether the proposed relationships vary across different farm characteristics or environmental conditions.
6. Conclusion
This study examined the relationships among supply chain collaboration (SCC), supply chain decision-making (SCDM), supply chain risk management (SCRM), and farm performance (FP) in smallholder corn farming in Gorontalo Province, Indonesia, using Resource-Based Theory (RBT) as the theoretical foundation. The hypothesis-testing results provide a clear pattern of direct and mediated relationships. H1 was supported, confirming that SCC has a positive and significant effect on FP, whereas H2 was not supported because SCDM did not have a significant direct effect on FP. H3 was supported, demonstrating that SCRM positively and significantly influences FP. Furthermore, H4 and H5 were supported, indicating that both SCC and SCDM positively influence SCRM. The mediation analysis also supported H6 and H7. Specifically, SCRM partially mediates the relationship between SCC and FP (H6), while it fully mediates the relationship between SCDM and FP (H7).
These results provide an important refinement to the resource–capability–performance logic underlying this study. The support for H1 and H6 indicates that collaboration contributes to farm performance through both a direct pathway and an indirect pathway through SCRM. In contrast, the rejection of H2, together with the support for H5 and H7, shows that decision-making capability does not automatically translate into higher farm performance. Instead, its performance value emerges when decision-making is translated into operational risk management practices. This finding extends the application of RBT by demonstrating that relational and managerial capabilities may create different pathways to performance and that SCRM serves as a critical operational mechanism through which these capabilities are converted into performance outcomes under conditions of uncertainty.
From a practical perspective, the findings indicate that improving smallholder farm performance requires more than strengthening decision-making capability in isolation. Farmers and other supply chain actors should strengthen collaborative relationships, improve information sharing and coordination, and integrate decision-making processes with systematic risk identification, assessment, and mitigation practices. The partial mediation identified in H6 suggests that collaboration can generate performance benefits directly while also strengthening SCRM, whereas the full mediation identified in H7 suggests that decision-making contributes to performance primarily when it is embedded in effective risk management practices. Therefore, farmers, cooperatives, extension agencies, and other supporting institutions should develop integrated approaches that connect collaboration and decision-making with practical SCRM activities to improve performance under uncertain agricultural supply chain conditions.
Appendix A
| Supply Chain Collaboration (SCC) | X11 | I regularly share market and production information with my supply chain partners. |
| X12 | My supply chain partners and I engage in joint planning for production and distribution activities. | |
| X13 | I coordinate my farming activities closely with other supply chain actors. | |
| X14 | I trust my supply chain partners to fulfill their commitments. | |
| X15 | My supply chain partners and I work together to solve problems when disruptions occur. | |
| X16 | I am committed to maintaining long-term relationships with my supply chain partners. | |
| Supply Chain Decision-Making (SCDM) | X21 | I make timely decisions regarding production activities when facing uncertainty. |
| X22 | I evaluate available alternatives before making production or marketing decisions. | |
| X23 | I adjust my production plans in response to changes in market conditions. | |
| X24 | I make flexible decisions to respond to fluctuations in input availability and prices. | |
| X25 | I allocate my farming resources based on a careful assessment of risks and opportunities. | |
| Supply Chain Risk Management (SCRM) | Z11 | I regularly identify potential risks that may disrupt my farming and supply chain activities. |
| Z12 | I assess the likelihood and potential impact of risks on my farm performance. | |
| Z13 | I develop strategies to mitigate risks that could affect my production and income. | |
| Z14 | I monitor changes in market and environmental conditions that may pose risks. | |
| Z15 | I maintain contingency plans to respond to unexpected disruptions in my supply chain. | |
| Z16 | I proactively adjust my farming operations to minimize the impact of identified risks. | |
| Z17 | I share risk-related information with my supply chain partners to collectively manage disruptions. | |
| Farm Performance (FP) | Y11 | My farm productivity has improved compared to previous seasons. |
| Y12 | My farm income has been relatively stable over the past year. | |
| Y13 | My farming operations are cost-efficient compared to similar farms in my area. | |
| Y14 | I have reliable access to markets for selling my corn harvest. | |
| Y15 | The quality of my corn output consistently meets or exceeds market standards. | |
| Y16 | I am able to deliver my corn harvest to buyers on time as planned. | |
| Y17 | Overall, I am satisfied with the performance of my farming operations. |
Acknowledgements
The authors would like to express their sincere gratitude to the Faculty of Engineering, Gorontalo State University for providing research support through the Faculty Collaborative Research Scheme. This research was funded under the research contract number 1925/UN47.B5/PT.01.03/2026.
Data availability
Research data is available in the body of the article.
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References
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