Abstract
This research investigates the governance mechanisms driving sustainability in high-value, perishable agri-food systems, specifically focusing on governance path heterogeneity within the Ecuadorian strawberry sector. Moving beyond monolithic supply chain analysis, we examine how the configuration of the chain—distinguishing between two-stage (direct-to-retailer) and three-stage (intermediated) systems—dictates the effectiveness of coordination and environmental outcomes. Drawing on New Institutional Economics (NIE), we employ an empirical framework to disentangle the direct and mediated effects of formal and relational governance using data from a multi-stakeholder survey of producers and downstream agents analyzed via structural equation modeling (SEM). The findings reveal a significant “mediation shift” contingent on chain complexity: in two-stage chains, formal contracts provide the structural scaffolding that fosters relational trust; conversely, in three-stage chains, relational assets are the prerequisite for formalization. Crucially, we identify a “sustainability gap” where high transaction costs directly erode environmental performance, hindering the transition to a resilient bio-economy. These results suggest that policy interventions must be tailored to the specific organizational architecture of the supply chain to foster sustainable and resilient agri-food systems in the Andean region.
1 Introduction
Global agri-food supply chains are currently at a critical crossroads, facing intense pressure to mitigate ecological degradation, chemical runoff, and resource depletion. As the most commercialized berry worldwide, the strawberry sector exemplifies the tension between high-value market expansion and environmental strain (Anastasios and Nikouli, 2025). Given its highly perishable nature and intensive input requirements, achieving environmental sustainability in this sector necessitates sophisticated coordination among diverse stakeholders (Chen et al., 2018; Predieri et al., 2021). While scholarship has long argued that chain coordination is a prerequisite for sustainability-oriented performance (Kirezieva et al., 2016; Ludwig-Ohm et al., 2019), the mechanisms through which coordination actually translates into measurable ecological outcomes remain poorly understood.
Effective supply chain governance typically relies on a mix of formal mechanisms (e.g., written contracts, certifications) and relational assets (e.g., trust, social networks) to manage interdependencies and reduce transaction costs (Franco et al., 2024; Montecchi et al., 2021). Historically, Transaction Cost Economics (TCE) and coordination literature have focused heavily on intra-organizational efficiency, often overlooking the implications for inter-organizational environmental performance (Dornfeld et al., 2025; Prazeres et al., 2023). Furthermore, existing studies (e.g., Afif et al., 2025; Meena et al., 2023) frequently examine formal and relational coordination as isolated silos. While some evidence suggests that contracts are effective for standardization (Bachev, 2024) and relational governance improves general relationship quality (Davidson, 2025), there is a significant research gap regarding how these mechanisms interact—specifically through direct and mediated pathways—to influence the environmental footprint of the chain.
This paper addresses this gap by investigating the direct and mediated effects of coordination mechanisms on environmental performance within the strawberry supply chain. This sector provides a unique laboratory for governance research due to three factors: the extreme perishability of the fruit which mandates high-frequency exchanges, the complex “plasticulture” and irrigation needs that drive environmental impact, and the simultaneous use of informal social bonds and formal retail standards. By applying an integrated assessment framework, this study evaluates data from a large-scale survey of farmers and downstream actors (intermediaries and firms).
The research employs a robust quantitative approach, utilizing Confirmatory Factor Analysis (CFA) to validate multi-dimensional constructs of coordination and Structural Equation Modeling (SEM) to disentangle the sequential effects between variables. Unlike previous studies (e.g., Hutchison et al., 2023; Malca et al., 2023) that treat relational coordination as a monolithic construct, this paper empirically examines its multidimensionality and its role as a potential mediator that optimizes formal environmental mandates. Consequently, this study contributes to the literature in three ways: first, by modeling the specific links between coordination and ecological performance; second, by providing empirical evidence of the mediating role of relational governance in high-value fruit chains; and third, by offering a hybrid governance perspective that explains how formal and informal mechanisms complement each other to foster environmentally resilient supply chains.
The remainder of the paper is organized as follows: Section 2 establishes the theoretical foundations, research hypotheses and benchmark models of supply chains; Section 3 details the methodology; Section 4 discusses the empirical results; and Sections 5 and 6 provide the discussion and concluding remarks.
2 Theoretical foundations, research hypotheses and benchmark models of supply chains
2.1 Theoretical foundations and research hypotheses
The analysis of coordination in agri-food chains is rooted in New Institutional Economics, specifically Transaction Cost Economics (TCE) and the Relational View. While TCE emphasizes formal contracts to mitigate opportunism (Williamson, 2005), the Relational View suggests that competitive advantages, such as superior environmental performance, stem from joint idiosyncratic assets and social mechanisms (Saviotti, 2007). In the global strawberry sector—characterized by high perishability and stringent phytosanitary standards—these mechanisms do not operate in isolation but through a complex nexus of direct and mediated paths (Wu et al., 2023). Conceptually, as illustrated in Figure 1, this research proposes that relational and formal coordination mechanisms, alongside transaction cost-related aspects, are intrinsically interrelated with supply chain environmental performance. Rather than viewing these governance structures as independent silos, our model posits that they act as complements; while formal structures provide the necessary regulatory framework for ecological standards, relational mechanisms facilitate the tacit knowledge exchange required for their effective implementation. This theoretical synthesis allows for the examination of how these synergistic interactions and mediated pathways drive ecological sustainability in high-value berry trade.
Figure 1
2.1.1 Relational coordination and environmental outcomes
Relational coordination is defined as a mutually reinforcing process of high-frequency communication and shared goals (Artz and Brush, 2000). In strawberry supply chains, environmental goals (e.g., reducing pesticide runoff or water waste) require intense tacit knowledge exchange that formal contracts often fail to capture. Relational assets facilitate the “socialization” of green practices among actors (Muñoz-Herrera and Reuben, 2024). This leads to Hypothesis 1:
Hypothesis 1. Relational coordination mechanisms are positively and directly related to the environmental sustainability performance of the strawberry supply chain.
2.1.2 Formal coordination as a structural driver
Formal coordination, involving standardization, certifications, and written mandates, provides the necessary “rules of the game” for ecological compliance (Ménard, 2004, 2026). In high-value berry chains, formalization ensures that environmental standards (like GlobalGAP) are auditable. However, excessive formality can lead to rigidity (Kamutando and Tregenna, 2024), creating an ambiguous effect on performance if not managed flexibly (Oppedal Berge and Garcia Pires, 2020). Neri-Lainé (2026) showed that formal practices (rules and delegations) positively affect firm performance. This leads to Hypothesis 2:
Hypothesis 2. Formal coordination mechanisms have a significant direct effect on environmental sustainability performance, though the direction of this effect depends on the level of bureaucratic rigidity.
2.1.3 The role of transaction costs
A core premise of this research is that coordination does not only influence performance through behavioral alignment but also through the structural optimization of transaction features. In the strawberry sector, transaction costs—specifically those related to information search, monitoring, and quality enforcement—are inherently high due to the fruit’s extreme perishability and vulnerability to damage (Ateka and Mbeche, 2023). We argue that these costs exert a direct negative pressure on environmental performance; high transaction costs divert critical financial and managerial resources away from sustainability initiatives, such as waste reduction and precision agriculture. This leads to the following hypotheses:
Hypothesis 3. Transaction costs are negatively related to environmental sustainability performance.
2.1.4 Governance complementarity (the hybrid approach)
The debate on whether formal and relational mechanisms are substitutes or complements is central to research. We argue for complementarity: formal contracts provide the structural blueprint for environmental goals, while relational trust provides the “grease” that allows these contracts to function in the dynamic, climate-prone environment of berry production (Navarrete-Cruz and Birkenberg, 2024). This is consistent with (de Oliveira et al., 2019) who observe that successful buyer–supplier relationships utilize various mechanisms to reduce supply chain performance ambiguity. This leads to Hypothesis 4:
Hypothesis 4. Formal and relational coordination mechanisms complement each other, creating a synergistic effect on environmental sustainability performance.
2.2 Supply chain configurations and benchmark models
The strawberry sector operates through distinct structural configurations that influence how coordination is deployed. We analyze two primary models (
Figures 2,
3):
Three-stage configuration (): Involves a Supplier (), an Intermediary (), and a final Buyer (). This setup is common in traditional markets where intermediaries aggregate volume but can increase transaction costs and obscure environmental traceability (Awasthi et al., 2018).
Two-stage configuration (): A direct link between a Supplier () and a Buyer (), often seen in high-end export or supermarket chains. This configuration typically allows for stronger formal coordination and direct environmental monitoring (Vanzetti et al., 2017; Vieira and Pereira, 2016). The sector’s transition is theoretically significant here: the “ “(two-stage) model is increasingly dominant in the global strawberry trade due to the need for strict “cold chain” management and pesticide residue control, which requires direct and mediated coordination mechanisms.
Figure 2
Figure 3
2.2.1 The supplier’s optimality conditions
Let denote the nonnegative production output of supplier . Each supplier faces a production cost function that depends on the production output and which includes costs, that is:
A supplier makes an exchange with an intermediary for a certain amount of product denoted by. Each exchange of involves coordination and transaction costs denoted by, given by:
A supplier may exchange products with multiple intermediaries, . The quantity produced by supplier must satisfy the following conservation of flows equation:
If we let denote the product price a supplier charges for the product to intermediary , and note the conservation of flow Equation (3), we can express the criterion of profit maximization for supplier as follows:subject to for all
2.2.2 The intermediary’s optimality conditions
An intermediary faces transaction costs, denoted by the function which depends on the purchased amount from suppliers. This can be written as:
The intermediary denotes a product selling price to the buyer B at the storage place by. The optimization problem of an intermediary is then given by:where and for all and . Constraint (Equation 7) expresses that intermediaries cannot sell more than what they hold in stock.
2.2.3 The buyer’s optimality conditions
The buyer considers at the purchase decision, which is the set of coordination costs and depends on the purchased amount from the intermediary:
The buyer also considers the price charged by the intermediary for the product denoted by. The product price at the buyer outlet is denoted by. The buying condition for the buyer is:
Equation (9) states that buyers will only buy products from intermediaries if the price they can charge to their customers, , exceeds or is equal to the costs of buying and transacting with intermediaries. Nagurney et al. (2002) apply similar conditions to develop the equilibrium conditions within a supply chain network with manufacturers, retailers and consumers.
3 Data and methodology
3.1 Context: the global and local strawberry supply chain
The strawberry sector represents a high-stakes environment for environmental governance due to its intensive resource requirements and high market value (Gereffi et al., 2005). Globally, over 29,035 ha are commercially cultivated, with Mexico and Chile leading production (González-Ramírez et al., 2020); however, the Ecuadorian highlands offer a unique case for studying governance in sensitive ecosystems. In Ecuador, production is concentrated in the provinces of Tungurahua, Chimborazo, Bolívar., and Pichincha, involving approximately 8,000 peasant families across at least 3,800 ha. The typical cultivation area is small-scale, averaging 2,500 m2 per farm, yet these units are often highly modernized with government-subsidized irrigation. Economically, the sector is defined by high price volatility: producer prices fluctuate from USD 1.30–1.40/kg in low seasons to USD 0.80–0.90/kg during peaks.
The supply chain is bifurcated into two distinct channels that dictate environmental and economic outcomes. In the traditional three-stage channel, intermediaries dominate the flow from the highlands to the coastal regions, often capturing margins higher than those of the producers. These intermediaries manage farm-gate collections and distribution to public marketplaces, where consumer prices range from USD 1.10/kg in overproduction periods to USD 1.80/kg in low seasons, burdened by an average transport cost of USD 0.10/kg. Parallel to this, the processing industry, centered in major hubs like Guayaquil, Cuenca, and Quito, transforms approximately 4,520 tons of fresh fruit annually into pulp, concentrate, and jelly. The industry pays producers between USD 0.70 and USD 1.50/kg depending on seasonal supply, increasingly utilizing intermediaries as strategic quality-control agents.
Finally, the modern two-stage channel—driven by supermarket corporations with international reach in Uruguay, Panama, and Argentina—represents the frontier of chain professionalization (Kirschbaum et al., 2017). This channel trades roughly 1,230 tons annually, with consumer prices reaching USD 5.20–5.70/kg. This premium is tied to strict formal coordination, including rigid logistical demands and penalties for non-compliance regarding phytosanitary and environmental standards. This bifurcation allows us to analyze how different coordination mechanisms (formal mandates in supermarkets versus relational trust in traditional markets) directly and indirectly impact the environmental footprint of the strawberry sector.
3.2 Methodology for data collection
The identification of indicators for coordination and performance is grounded in the Integrated Sustainability Performance Assessment (ISPA) framework (Moreno-Miranda and Dries, 2022). This study specifically adapts the ISPA indicators to the physiological and commercial realities of the strawberry sector, where high perishability necessitates rapid, precise coordination.
3.2.1 Relational coordination
Relational coordination is conceptualized as a mutually reinforcing process of communication and interaction for effective task integration (
Saikouk et al., 2021). It is measured as a latent variable using three items on a 10-point Likert scale:
Trust: Defined as the willingness to rely on an exchange partner (Akhtar and Khan, 2015). Respondents rated their level of confidence in the buyer (1 = low, 10 = high).
Fair treatment: Capturing the perceived quality of treatment and equity in the relationship (Goerg et al., 2010), measured from “deeply unfair” (1) to “extremely fair” (10).
Power-sharing: The degree of influence over trade terms (e.g., pricing, delivery schedules) during negotiations (Broad, 2023), ranging from “little influence” (1) to “strong influence” (10).
3.2.2 Formal coordination
Reflecting the “rules of the game” in high-value berry chains, formal coordination is operationalized as the share of formal transactions (governed by written contracts and certifications like GlobalGAP) within the actor’s total volume. This metric allows us to quantify the level of institutionalization in the relationship (Ola and Menapace, 2020).
3.2.3 Transaction costs
To capture the efficiency of the exchange process and the “wasted effort” that often precedes environmental failure (e.g., fruit rotting while waiting for pickup), two primary indicators were used:
Negotiation time-frame: Perception of how long negotiations take before they are considered a loss (Lalive et al., 2018). Rated from 1 (very slow) to 10 (very quick).
Exchange frequency: The number of weekly trade interactions (Weseen et al., 2014). Given that strawberries require frequent harvesting (typically twice per week), this is a critical proxy for coordination intensity.
3.2.4 Environmental performance index
While the original ISPA framework covers social and economic domains, this study isolates the Environmental Index to test the specific ecological impacts of governance. To ensure transparency, we combine objective metrics with actor-based perceptions:
Resource intensity: Objective measure of water consumption (m3/kg of fruit).
Circular efficiency/waste: A 10-point Likert scale measuring the frequency and volume of food losses (post-harvest waste), which is the primary driver of the sector’s carbon and resource footprint (Pastolero and Sassi, 2022).
The environmental index was calculated through a three-step process: (i) applying Principal Components Analysis (PCA) to the indicators at each chain stage, (ii) combining these components into a standardized index, and (iii) aggregating these to the chain level by allocating equal weight to each stage (Producers, Intermediaries, and Firms). This rigorous weighting ensures that the performance score reflects the entire “seed-to-shelf” ecological impact (see Appendix A).
3.2.5 Survey design
The measurement of coordination and environmental performance variables was operationalized through a multi-actor structured questionnaire (Appendices B and C). To ensure the instrument’s cross-cultural and technical validity, the initial items were translated into Spanish and subjected to a double-blind back-translation. Subsequently, three academic experts in agri-food economics reviewed the content to ensure linguistic and conceptual clarity.
A pre-test was conducted via exploratory interviews with ten key stakeholders, including association leaders, regional intermediaries, and industrial managers. This pilot phase confirmed the operational feasibility of the indicators and informed a hybrid data collection strategy. Specifically, stakeholders suggested in-situ interviews for primary production zones to maximize response rates among smallholders, and remote digital surveys for corporate managers in distant urban hubs (e.g., Guayaquil or Quito) to accommodate their professional schedules.
3.2.6 Data collection and the “downward spiral” method
Fieldwork was carried out between mid-December 2024 and mid-February 2025, capturing the transition between production seasons. Primary data from producers was gathered through face-to-face interviews during organizational meetings of regional guilds. To mitigate social desirability bias, each interview was conducted in a dedicated private space and averaged 10 min.
To capture a true “chain perspective” and map the mediated effects of governance, a downward spiral method (tracing links from origin to destination) was implemented. Producers identified their primary buyers, who were then tracked to commercial exchange points, such as the Tungurahua municipal wholesale market. This process allowed the research team to follow the physical and contractual flow of strawberries to specific fruit processors and retail corporations. For these downstream actors, digital invitations were dispatched via the QuestionPro platform. Following the protocol of Bolíbar et al. (2019), a single, authoritative response was elicited per organization to ensure consistency in the representation of corporate coordination policies.
3.2.7 Sampling and stratification
The sampling strategy was designed to reflect the multi-channel nature of the strawberry trade. A unique feature of this study is that individual producers may appear in multiple chain configurations (e.g., selling “Grade A” fruit to supermarkets and “Grade B” to intermediaries).
Producer sample: The sampling frame comprised all farms registered with the Ministry of Agriculture by December 2024—a population subject to formal health insurance and tax (RUC/RISE) requirements. From this frame, 406 producers were randomly selected.
Intermediary sample: Through the spiral method, 68 intermediaries were identified and stratified into “Small” (58%), who lack fixed infrastructure and collect fruit at the farm-gate, and “Large” (42%), who operate fixed logistics hubs in public wholesale markets.
Processors and retailers: The final links were identified by upstream partners, resulting in a sample of 33 processors (90% SMEs, 10% large firms) and 32 retailers (dominated by large corporations with international presence).
This stratified, multi-level sample (Total N = 539) provides the statistical power necessary to perform Structural Equation Modeling (SEM) and evaluate the direct and indirect pathways between coordination mechanisms and environmental outcomes. Table 1 shows the sample distribution by supply chain configuration and actor.
Table 1
| Chain configuration | Frequency | % | |
|---|---|---|---|
| Three-stage |  | 91 | 22.41 |
 | 54 | 13.31 | |
 | 169 | 41.62 | |
| Two-stage |  | 54 | 13.31 |
 | 38 | 9.35 |
| Chain actors | N° responses | % |
|---|---|---|
| Producers | 406 | 75.32 |
| Intermediaries | 68 | 12.61 |
| Processors | 34 | 6.31 |
| Retailers | 31 | 5.76 |
Distribution of supply chain configurations and actors in the study sample.
s.d. = standard deviation. The ‘producer à large intermediary à retailer’ configuration was not observed in the sample. The same producer may be counted in different chain configurations. Source: authors’ survey.
3.3 Methodology for analysis
3.3.1 Validity and reliability testing
To ensure the robustness of the conceptual constructs, we conducted a comprehensive evaluation following the guidelines of McGrath (2005). We utilized the full sample of 539 responses to examine the internal consistency and stability of the measurement scales. Cronbach’s (Cronbach, 1951) was calculated for each construct, with values 0.70 indicating high reliability, while values between 0.50 and 0.70 were accepted as indicative of valid measures for exploratory purposes in complex supply chain environments (Ruel et al., 2021). Furthermore, Composite Reliability (CR) and Average Variance Extracted (AVE) were estimated to ensure that the latent variables captured sufficient variance relative to measurement error.
3.3.1.1 Exploratory factor analysis
EFA was employed to filter the observed variables and confirm the underlying factor structure before proceeding to structural modeling. Using the Maximum Likelihood extraction method and Promax rotation, we explored the relationships between items of relational coordination and transaction costs. The Kaiser-Meyer-Olkin (KMO) measure was utilized to assess sampling adequacy, requiring a value 0.5 (Ali, 2024), while Bartlett’s Test of Sphericity was applied to confirm that the correlation matrix was not an identity matrix ( 0.001). Formal coordination, being a single-item observed variable (share of formal transactions), was excluded from this stage as per standard psychometric protocols. Details are in (see Appendix D).
3.3.1.2 Confirmatory factor analysis
To validate the factor structure identified in the EFA, a CFA was performed using SPSS AMOS 24. The measurement model was specified with two latent constructs: Relational Coordination and Transaction Costs. We employed Maximum Likelihood (ML) estimation, which provides more accurate test statistics under non-normality by adjusting
estimates through the asymptotic covariance matrix (
Firouzabad et al., 2024). The global “goodness of fit” was evaluated using multiple criteria:
CMIN/DF: /degree of freedom ratio.
CFI and NFI: Comparative and Incremental Fit Indices (threshold 0.80).
RMSEA: Root Mean Square Error of Approximation (threshold 0.06 for good fit; Wong and Ngai, 2022).
3.3.2 Structural equation modeling and mediation analysis
The hypothetical model (Figure 1) was tested using Structural Equation Modeling (SEM) to evaluate the predictive validity of the proposed governance paths. Unlike traditional regression, SEM allows for the simultaneous estimation of the measurement model and the structural paths while accounting for measurement error (Kühnemund and Guido, 2025; Okanlawon et al., 2025).
The mediation analysis (Hypothesis 4) investigates how Relational Coordination () serves as the generative mechanism between Formal Coordination () and Environmental Performance (). Following the contemporary SEM approach, the model is defined by a system of two structural equations:
In this system, and represent the indirect pathways, while represents the direct effect of formal coordination on environmental outcomes. Crucially, while the model is estimated through these two equations, the Total Effect ()—traditionally identified in the first step of the Lin et al. (2005) framework—is mathematically derived as the sum of the direct and indirect paths (). This integrated approach allows for the calculation of the Standardized Indirect Effect, which is then tested for significance using bootstrapping (5,000 re-samples) to ensure robust results in the presence of non-normal data distributions. Figure 4 exemplify the structure of the mediation analysis.
Figure 4
4 Results
4.1 Characteristics of the supply chain actors
A total of 539 usable questionnaires were processed, with producers representing the majority of the sample (75.3%). The descriptive analysis (Table 2) reveals significant structural differences between the two-stage (direct) and three-stage (intermediated) configurations.
Table 2
| Variable | Unit | Two-stage | Three-stage | ANOVA F-values | ||
|---|---|---|---|---|---|---|
| Mean | s.d. | Mean | s.d. | |||
| Producers | ||||||
| Farm income | USD | 3,499 | 1,311 | 2,698 | 997 | 69.05*** |
| Household size | Number | 3.72 | 1.39 | 3.84 | 1.01 | 3.47 |
| Age | Years | 42.50 | 11.45 | 52.74 | 9.87 | 9.51** |
| Education | Years | 10.71 | 3.55 | 6.79 | 2.55 | 12.86** |
| Experience | Years | 15.44 | 4.85 | 18.42 | 2.43 | 8.12* |
| Yield | kg/m2 | 0.72 | 0.42 | 0.53 | 0.34 | 16.03** |
| Selling price | USD/kg | 1.71 | 0.14 | 1.49 | 0.29 | 22.08** |
| Intermediaries | ||||||
| Business income | USD | – | – | 3,399 | 1,044 | – |
| Employees | Number | – | – | 7.76 | 3.56 | – |
| Experience | Years | – | – | 10.17 | 5.17 | – |
| Buying price | USD/kg | – | – | 1.55 | 0.31 | – |
| Selling price | USD/kg | – | – | 2.11 | 0.49 | – |
| Processors/supermarkets | ||||||
| Company age | Years | 22.19 | 8.22 | 11.76 | 5.78 | 23.12** |
| Employees | Number | 76.98 | 19.12 | 13.12 | 5.29 | 35.75*** |
| Own capital | Share | 54.54 | 2.66 | 74.55 | 8.87 | 13.22** |
| Loaned capital | Share | 42.76 | 5.11 | 22.02 | 10.11 | 9.16* |
| Buying price | USD/kg | 1.87 | 0.17 | 2.09 | 0.45 | 8.55 |
| Value added | USD/kg | 0.68 | 0.22 | 0.77 | 0.22 | 12.05 |
Frequency distribution of supply chain actors’ characteristics.
s.d. = standard deviation. *, **, *** denote coefficient significant at 0.1, 0.05, and 0.001 level. Source: authors’ survey.
Producers in two-stage chains exhibit significantly higher farm income (M = 3,499 USD) and yields (0.72 kg/m2) compared to those in three-stage chains (p < 0.001). Interestingly, they are also younger (M = 42.5 years) and more educated (M = 10.71 years), suggesting that modern, direct-to-retailer strawberry chains attract more technically proficient farmers. Conversely, the three-stage chain is characterized by older, more experienced producers who receive a lower average selling price (1.49 USD/kg vs. 1.80 USD/kg).
The data in Table 3 indicates a divergence in coordination strategies between configurations. While two-stage chains rely on high-trust, high-formality relationships, three-stage chains are governed by informal power-balancing and higher transaction frequency. Specifically, two-stage actors report greater levels of trust and fair treatment but also greater administrative formality. Paradoxically, despite the lack of formal contracts, three-stage actors perceive lower transaction time frames, suggesting that traditional intermediated routes may benefit from established, albeit informal, logistical routines.
Table 3
| Variable | Code | Unit | Two-stage | Three-stage | Trust | Fair treatment | Power-sharing | Time frame | Frequency exchange | Formal transactions | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mean | s.d. | Mean | s.d. | |||||||||
| Trust perception | score [1–10] | 5.55 | 1.65 | 4.89 | 1.66 | 1 | ||||||
| Fair treatment | score [1–10] | 5.67 | 1.06 | 4.92 | 1.42 | 0.576** | 1 | |||||
| Power-sharing | score [1–10] | 5.11 | 1.22 | 5.44 | 1.74 | 0.534** | 0.519** | 1 | ||||
| Time frame | score [1–10] | 5.12 | 1.12 | 3.98 | 1.69 | −0.277** | −0.255** | −0.425** | 1 | |||
| Frequency exchange | times/week | 1.76 | 0.54 | 2.14 | 1.18 | −0.269** | −0.234** | −0.287** | 0.312** | 1 | ||
| Formal transactions | share | 71.15 | 23.09 | 14.87 | 5.54 | 0.421** | 0.324** | −0.011 | −0.177** | −0.169** | 1 | |
Descriptive statistics and correlation between variables on coordination (n = 406).
**The correlation is significant at the level 0,01 two tail. Source: authors’ own representation.
4.2 Reliability and validity
The measurement model was validated through a rigorous assessment of internal consistency and construct validity (Table 4).
Table 4
| Factor and items | Cronbach’s α | AVE | CR | Factor loadings | |
|---|---|---|---|---|---|
| Two-stage | Three-stage | ||||
| Factor 1: relational coordination | |||||
| Trust perception | 0.811 | 0.571 | 0.796 | 0.620 | 0.816 |
| Fair treatment | 0.828 | 0.569 | 0.840 | 0.829 | 0.718 |
| Power-sharing | 0.938 | 0.730 | 0.915 | 0.575 | 0.835 |
| Cumulative variance (%) | 63.3 | 74.9 | |||
| Eigenvalue | 2.13 | 2.47 | |||
| Factor 2: transaction costs | |||||
| Time frame | 0.784 | 0.509 | 0.763 | 0.585 | 0.706 |
| Frequency exchange | 0.773 | 0.480 | 0.742 | 0.518 | 0.623 |
| Cumulative variance (%) | 56.6 | 66.2 | |||
| Eigenvalue | 1.89 | 1.92 | |||
| Factor 3 and item | |||||
| Formal transactions | 0.702 | 0.536 | 0.778 | – | – |
Reliability and exploratory factor analysis of original measures.
AVE: average variance extracted; CR: composite reliability; KMO: The Kaiser-Meyer-Olkin measure of sampling adequacy. Estimation method: maximum likelihood. sampling adequacy indices: to two-stage and to three-stage configurations. Source: authors’ own representation.
Reliability: All constructs achieved Cronbach’s and Composite Reliability (CR) scores above the 0.70 threshold, indicating high internal consistency. Convergent Validity: Factor loadings in both EFA and CFA were significant (). While the Average Variance Extracted (AVE) for Exchange Frequency (0.48) was slightly below the ideal 0.50, it was retained due to its high face validity and critical importance in the highly perishable strawberry sector.
The exploratory factor analysis of relational coordination and transaction costs is presented in Table 4. Results show that trust, fair treatment, and power-sharing are captured in factor 1. Time frame and exchange frequency are represented by factor 2. Factor 1 items have relatively higher loadings than items of Factor 2. The cumulative variances of eigenvalues are above 56% for each factor and each supply chain configuration.
Table 5 presents the results of the confirmatory factor analysis, it confirms that relational coordination and transaction cost-related measures meet convergent validity conditions, so the survey instrument is deemed acceptable. Model Fit: The CFA (Table 5) confirmed a superior fit for the data. In the two-stage configuration, the indices (/df = 1.84, CFI = 0.92, RMSEA = 0.051) align with the strict “good fit” criteria.
Table 5
| Construct and items | Factor loadings | |||
|---|---|---|---|---|
| Relational coordination | Transaction costs | |||
| Two-stage | Three-stage | Two-stage | Three-stage | |
| Trust perception | 0.63*** | 0.82*** | ||
| Fair treatment | 0.82*** | 0.72*** | ||
| Power-sharing | 0.53** | 0.83*** | ||
| Time frame | 0.59** | 0.71** | ||
| Frequency exchange | 0.51** | 0.62** | ||
Confirmatory factor analysis of relational and transaction costs measures.
Loadings are standardized; **, *** denote coefficient significant at 0.05 and 0.001 level. Goodness of fit indices: , , , , to two-stage. , , , , to the three-stage configurations. Source: authors’ own representation.
4.3 Comparative environmental performance
The frequency distributions (Figure 5) reveal a pronounced divergence in ecological outcomes between the two supply chain configurations. The two-stage configuration demonstrates a superior environmental profile, with a mean score of 0.78 and a high concentration of observations (68%) within the 0.7 to 0.9 intervals. In contrast, the three-stage configuration exhibits a lower average performance (0.72), with nearly 41% of the chains scoring below the sample mean.
Figure 5
Several factors explain this environmental performance gap:
Waste reduction and perishability: The significantly higher environmental scores in two-stage chains likely stem from the “shorter” logistical path. By bypassing traditional intermediaries, strawberry producers reduce the number of handling points and the time-to-market. In a sector where strawberries lose marketability within 48–72 h, this structural efficiency directly translates into lower post-harvest losses, which is a key component of the ecological index.
The “scaffolding” effect of formality: As noted in Table 3, two-stage chains operate with a 72% share of formal transactions. These formal agreements often include technical specifications for “Good Agricultural Practices” (GAP) or cold-chain maintenance required by supermarkets. This formality acts as an environmental safeguard, mandating more precise input use and waste management compared to the informal, three-stage routes.
Intermediary inefficiency: The lower scores in the three-stage model suggest that the “large intermediaries” and traditional wholesale market structures may lack the specialized cold storage or rapid-logistics infrastructure necessary to maintain strawberry quality. The resulting “physical waste” at the wholesale level is reflected in the lower ecological performance scores for this configuration.
These results provide empirical support for the transition toward direct-to-retailer models as a strategy for green supply chain management in the Ecuadorian fruit sector. The findings suggest that “middleman” stages, while providing market access for traditional farmers, may impose an “environmental tax” in the form of higher resource waste and lower compliance with sustainable practices.
4.4 Hypotheses testing
Before evaluating the structural paths, the global fit of the Structural Equation Model (SEM) was assessed for both chain configurations. As indicated in Figure 6, all goodness-of-fit indices (including /df, CFI, and RMSEA) meet the established thresholds for a “good fit,” providing a robust foundation for hypothesis testing.
Figure 6
The structural paths reveal several key insights:
Relational coordination: The coefficients between relational coordination and environmental performance are positive and highly significant (0.35 and 0.47 for two- and three-stage chains, respectively), providing strong support for H1.
Formal coordination: The paths from formal coordination to environmental performance are also significant (0.62 for two-stage; 0.36 for three-stage). Interestingly, the effect is notably stronger in the two-stage model, likely due to the higher prevalence of supermarkets and strict certifications. This provides empirical evidence that contradicts our initial prediction of an “ambiguous” effect, suggesting that in the Ecuadorian strawberry sector, formality is a clear driver of performance (H2).
Transaction costs: As hypothesized (H3), transaction costs have a negative and significant influence on sustainability (−0.21 for two-stage; −0.24 for three-stage).
Interaction between latent variables: Following Alaloul et al. (2020), the negative correlation between relational coordination and transaction costs (−0.45 to −0.63) suggests that high-quality relationships serve as a “transactional lubricant,” lowering the costs associated with negotiation and monitoring.
4.5 The mediating effect of coordination mechanisms
The mediation analysis (Table 6) clarifies the governance “pathway” unique to each configuration.
Table 6
| Effect on sustainability dimension | Two-stage | Three-stage | ||
|---|---|---|---|---|
| Indirect | Med. Obs. | Indirect | Med. Obs. | |
| Relational➔Formal➔Environmental | 0.016 | Not significant | 0.087** | Partial |
| Formal➔Relational➔Environmental | 0.304** | Partial | 0.034 | Not significant |
| Formal➔Transactional➔Environmental | 0.401*** | Partial | – | Not found |
Path coefficient estimates and model fit indices.
Med. obs. is mediation observed. ***, **, * denote coefficients significant at the level 0.01, 0.05, 0.10. Goodness of fit indices: to two-stage configuration; to three-stage configuration. Source: authors’ own representation.
4.5.1 Two-stage configuration (direct-to-retailer)
In the two-stage chains, we observe a “formal-to-relational” mediation logic (see Figure 7). Formal coordination (contracts) acts as a baseline that fosters better relational coordination, which in turn significantly drives Environmental (0.304**) performance. This indicates a partial mediation; while contracts have a direct effect, they also work by strengthening the trust and fair treatment between producers and processors.
Figure 7
4.5.2 Three-stage configuration (intermediated)
Conversely, the three-stage chains follow a “relational-to-formal” logic (see Figure 8). Because these chains are inherently informal, trust must be established first to facilitate any degree of formal agreement. Here, formal coordination partially mediates the effect of relational capital on environmental (0.087**) performance.
Figure 8
Ultimately, these findings reveal that while the direction of the mediation path flips between configurations—moving from Formal-to-Relational in two-stage chains and Relational-to-Formal in three-stage chains—coordination mechanisms are never truly independent. In the strawberry sector, achieving ecological performance depends on an integrated governance approach where formal rules and relational trust reinforce one another to overcome the high transaction costs inherent in perishable supply chains.
5 Discussion
5.1 Main findings: the dual logic of coordination
Our results provide robust empirical support for the proposed structural model, offering a significant contribution to the burgeoning field of sustainable supply chain management (SSCM).
5.1.1 The dominance of relational capital
The positive and significant relationship between relational coordination and sustainability (0.35 and 0.47) validates the premise that trust, fair treatment, and power-sharing are not just “soft” social assets but critical performance drivers. While previous literature has explored coordination variables in internal contexts, our study extends this to the inter-organizational context of the strawberry sector. We demonstrate that in highly perishable chains, a lack of trust acts as a structural barrier, curbing the willingness of partners to engage in the collaborative efforts necessary for value-added production.
5.1.2 The effectiveness of formal coordination
Contrary to the “ambiguity” often discussed in institutional theory, our findings show that formality in exchanges (0.62 for 2-stage) consistently improves sustainability performance. This is a critical insight: in the Inter-Andean strawberry sector, the presence of written agreements provides a “scaffolding” for socio-economic stability. However, a key distinction emerged: while formality drives social and economic gains, its direct link to ecological performance is weaker, suggesting that environmental stewardship requires more than just administrative compliance—it requires targeted green protocols.
5.1.3 The transaction cost lubricant
By confirming that high transaction costs—specifically negotiation time and frequency—negatively impact sustainability, we bridge Transaction Cost Economics (TCE) with sustainability theory. The results reflect that actor perceive negotiations in fragmented chains as “laborious,” confirming that more decentralized chains are inherently less optimal (Rehman and Jajja, 2023).
5.1.4 The mediation shift: a strategic contribution
The most significant contribution of this research is the identification of a “mediation shift” based on chain configuration.
Two-stage chains (formal-to-relational): Here, formality acts as the prerequisite for trust. Contracts provide the safety net that allows relational coordination to flourish.
Three-stage chains (relational-to-formal): In these traditionally informal routes, trust must be established first to make formalization possible.
6 Conclusion and implications
6.1 General conclusions and originality
This study demonstrates that the pathways to sustainability in the strawberry sector are not uniform but are dictated by the governance path heterogeneity of the supply chain architecture. Our findings reveal a critical “mediation shift”: in two-stage (direct) chains, formal contracts serve as the structural scaffolding that builds trust; conversely, in three-stage (intermediated) chains, relational trust is the prerequisite that enables formalization. While the sector exhibits socio-economic resilience, we identify a persistent “ecological deficit” where current coordination mechanisms are optimized for commercial efficiency rather than environmental stewardship. Furthermore, the results prove that transaction costs act as a direct barrier to performance; negotiation frictions do not just slow down trade, they actively erode sustainability.
6.2 Theoretical implications
This research contributes to New Institutional Economics (NIE) by providing empirical evidence of how transaction costs—specifically negotiation time and exchange frequency—directly impact sustainability outcomes. By identifying the “mediation shift,” we provide a well-founded reference for how governance complementarity versus substitutability is contingent upon chain architecture. This challenges monolithic views of supply chain coordination and provides a new theoretical lens for analyzing high-value perishable sectors.
6.3 Implications for policy and practice
Our findings provide a strategic roadmap for development practitioners and agricultural policymakers: Differentiated Intervention: Policy should move away from “one-size-fits-all” formalization. In two-stage chains, focus on designing formal contracts as trust-building tools. In three-stage chains, prioritize “bottom-up” relational investments, such as farmer cooperatives, to create the trust baseline necessary for formal agreements. Targeting the “Ecological Gap”: To bridge the current gap, public standards must incorporate explicit environmental conditionality (e.g., Good Agricultural Practices and water-use efficiency) into both formal contracts and relational expectations. Efficiency as a Sustainability Lever: Policymakers should support digital traceability and centralized distribution hubs in the Inter-Andean region to reduce negotiation times. Lowering these “hidden costs” frees capital that can be reinvested into ecological stewardship and waste reduction.
6.4 Practical implications for managers and decision-makers
For supply chain managers, these results indicate that investment in relational assets is a functional necessity in three-stage architectures to reduce the sustainability gap. Managers in direct-to-retailer systems should prioritize the precision of formal contracts to provide the structural scaffolding required for long-term coordination and environmental performance.
6.5 Research limitations and future directions
Despite its contributions, this study is subject to several limitations. Temporal Scope: The cross-sectional nature of the data provides a static “snapshot.” Future longitudinal designs should analyze how trust and formalization co-evolve across multiple production cycles. Model Complexity: While we identified transaction costs as a direct antecedent to performance, future research could utilize nested models to determine if formal coordination reduces environmental impact specifically by lowering monitoring and enforcement costs. Sectoral Generalization: Comparative studies with non-perishable sectors are needed to determine if the identified governance path heterogeneity is a universal feature of supply chain complexity.
Statements
Data availability statement
The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding author.
Ethics statement
The studies involving humans were approved by Ethics Committee of Universidad Técnica de Ambato. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study. Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.
Author contributions
CM-M: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. AV: Formal analysis, Investigation, Validation, Writing – original draft, Writing – review & editing. DA: Formal analysis, Methodology, Writing – original draft, Writing – review & editing. LT: Writing – original draft, Writing – review & editing. IM-S: Methodology, Validation, Writing – original draft, Writing – review & editing. PC: Formal analysis, Investigation, Writing – original draft, Writing – review & editing. EA-F: Writing – original draft, Writing – review & editing.
Funding
The author(s) declared that financial support was received for this work and/or its publication. This research was supported by the Technical University of Ambato (UTA). The funder played a role in providing the institutional framework and academic resources necessary for data collection and analysis. The funders had no role in the study design, data analysis, decision to publish, or preparation of the manuscript.
Acknowledgments
The authors sincerely thank the Research and Development Directorate (DIDE) of the Technical University of Ambato for the funding and support provided during the execution of this study. This research was conducted within the framework of the project “Multidimensional Poverty in the Tungurahua Highlands, Climate Change, and Food Sovereignty: Traditional Interventions for Community Improvement,” approved under Resolution No. UTA-CONIN-2025-0399-R, dated November 24, 2025.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that Generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fsufs.2026.1820161/full#supplementary-material
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Summary
Keywords
Andean agriculture, governance heterogeneity, supply chain architecture, sustainable food systems, transaction cost economics
Citation
Moreno-Miranda C, Vilela-Carrillo A, Armas-Real D, Tapia-Vasco L, Molina-Sánchez I, Carrasco-Velástegui P and Altamirano-Freire E (2026) Bridging the sustainability gap: how supply chain architecture dictates governance effectiveness in the Ecuadorian strawberry sector. Front. Sustain. Food Syst. 10:1820161. doi: 10.3389/fsufs.2026.1820161
Received
28 February 2026
Revised
24 April 2026
Accepted
22 May 2026
Published
01 July 2026
Volume
10 - 2026
Edited by
Sanzidur Rahman, University of Reading, United Kingdom
Reviewed by
Mónica Molina Barzola, Bolivarian University of Ecuador, Ecuador
Luis Rodriguez, Central University of Ecuador, Ecuador
Vaibhav Sharma, Malaviya National Institute of Technology, Jaipur, India
Updates
Copyright
© 2026 Moreno-Miranda, Vilela-Carrillo, Armas-Real, Tapia-Vasco, Molina-Sánchez, Carrasco-Velástegui and Altamirano-Freire.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Carlos Moreno-Miranda, cs.moreno@uta.edu.ec
Disclaimer
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.