Configurational Pathways to Digital Traceability Success in International Trade: An fsQCA Study of Trade-Corridor Cases

Abstract

Digital traceability has become an important capability in international trade, especially in high-regulation and high-risk supply chains. However, existing research has not fully explained how institutional, technological, and coordination-related conditions combine to produce successful outcomes. This study applies fuzzy-set Qualitative Comparative Analysis (fsQCA) to 24 trade-corridor/product-chain cases to identify the configurational drivers of Digital Traceability Success (DTS). The findings show that Digital Trade Readiness (DTR), Market Strictness (MKT), Digital Infrastructure (DIF), and Cross-border Coordination (COO) are highly consistent necessary conditions for DTS, whereas Blockchain-enabled Traceability (BCT) is not. The sufficiency analysis identifies one dominant pathway, DTR * PRK * MKT * DIF * COO, with perfect consistency and substantial coverage. These results indicate that traceability success emerges from the alignment of institutional readiness, regulatory pressure, infrastructural capacity, product-related risk, and cross-border coordination rather than from blockchain adoption alone. The study contributes to digital trade and supply-chain governance literature by offering a configurational explanation grounded in conjunctural causation and causal asymmetry. It also clarifies blockchain’s role as a contingent enabling component rather than a universally necessary determinant. Practically, the findings suggest that policymakers and firms should prioritize interoperable infrastructure, institutional readiness, and cross-border governance mechanisms over stand-alone technological solutions.

1. Introduction

Digital traceability has become a core capability in the transformation of international trade. As cross-border transactions grow more data-intensive and compliance-driven, the ability to capture, verify, and share information across supply chains is no longer a technical add-on but a strategic requirement. Traceability now underpins transparency, trust, regulatory compliance, and supply-chain resilience [1,2,3,4]. While the literature highlights the potential of blockchain and related technologies to enhance visibility and provenance, emerging evidence suggests that traceability outcomes depend less on any single technology than on the broader alignment of institutional readiness, digital infrastructure, and cross-border coordination [5,6,7,8,9].

This study is motivated by a clear empirical and theoretical gap. In practice, governments and firms face increasing pressure to digitalize trade processes, reduce documentation burdens, and comply with stringent international standards [10,11,12,13,14,15]. Yet the implementation of digital traceability remains uneven across trade corridors, with persistent challenges related to interoperability, cost, regulatory fragmentation, and coordination failures. In the literature, research on digital traceability remains fragmented across technological, operational, and institutional perspectives. What is missing is a systematic explanation of how these dimensions combine to produce successful outcomes. In particular, it remains unclear which conditions are necessary, which combinations are sufficient, and why blockchain appears critical in some contexts but not in others [16,17,18,19,20,21,22,23,24,25,26].

To address this gap, the study adopts a configurational perspective and conceptualizes digital traceability as a socio-technical capability embedded in institutional and organizational contexts. The analysis focuses on six conditions: Digital Trade Readiness (DTR), Blockchain-enabled Traceability (BCT), Product Risk (PRK), Market Strictness (MKT), Digital Infrastructure (DIF), and Cross-border Coordination (COO). Rather than treating these factors as independent variables, the study examines how their combinations generate Digital Traceability Success (DTS) [27,28,29,30].

Methodologically, the study employs fuzzy-set Qualitative Comparative Analysis (fsQCA), which is well suited to capturing conjunctural causation, equifinality, and causal asymmetry [27,28,29,30]. Using an intermediate-N sample of 24 trade-corridor cases, the analysis calibrates both outcome and conditions into fuzzy sets and applies necessity and sufficiency testing through truth-table analysis and Boolean minimization.

The results reveal a highly structured configurational pattern. Digital Trade Readiness, Market Strictness, Digital Infrastructure, and Cross-border Coordination emerge as necessary conditions for Digital Traceability Success, whereas Blockchain-enabled Traceability does not. The sufficiency analysis identifies a single dominant configuration DTR * PRK * MKT * DIF * COO with perfect consistency and substantial coverage. These findings indicate that traceability success is generated by the joint presence of institutional readiness, regulatory pressure, infrastructural capacity, product-related risk, and coordination across actors, rather than by blockchain adoption alone [27,28,29,30].

The 24 cases are intended to support analytical generalization to comparable trade corridors, not statistical inference to all trade relationships. The study contributes to the theory of configurational explanation of digital traceability, moving beyond single-factor and technology-centric accounts. It demonstrates that causality in this domain is con-junctural and system-dependent, consistent with the core logic of fsQCA [27,28,29,30]. Practically, the findings suggest that policymakers and firms should prioritize sys-tem-wide readiness legal frameworks for digital trade, interoperable infrastructure, and cross-border governance rather than relying narrowly on technological solutions [10,11,12,13,14,15,31].

This paper reviews the literature, development of the theoretical framework, presentation of the research design, analysis of results, and discussion of implications. Section 2 reviews the literature and identifies the research gap. Section 3 develops the theoretical framework. Section 4 presents the research design and calibration strategy. Section 5 reports the fsQCA results. Section 6 discusses the findings and their implications, and Section 7 concludes with limitations and directions for future research.

2. Literature Review

Digital traceability has increasingly been conceptualized as a core capability underpinning the transformation of international digital trade, linking supply-chain transparency, data integrity, and cross-border governance within digitally mediated transactions. Early studies positioned traceability primarily within food safety and quality management, defining it as the ability to track and trace products across production, processing, and distribution stages [1]. However, subsequent research has expanded this narrow operational view into a broader organizational and systemic capability associated with transparency, accountability, and performance across complex supply chains. In particular, blockchain-based traceability systems have emerged as a dominant research stream, emphasizing the role of immutable, shared ledgers in enhancing visibility and trust among dispersed actors [9,32]. This shift reflects a transition from traceability as a compliance mechanism toward traceability as a digital governance infrastructure embedded in global trade systems.

Within this evolving literature, digital traceability is increasingly understood as contingent upon both technological architectures and institutional conditions. Blockchain technologies are frequently highlighted for their capacity to provide tamper-resistant records, enable real-time data sharing, and improve provenance verification [4,32]. Empirical and case-based studies demonstrate these capabilities across sec-tors such as agri-food, textiles, pharmaceuticals, and luxury goods, where traceability supports anti-counterfeiting, quality assurance, and sustainability objectives [2,5,33,34,35,36]. At the same time, the literature also emphasizes that blockchain adoption is neither uniform nor universally optimal. The scope and depth of traceability systems vary significantly depending on firm strategy, regulatory requirements, and supply-chain complexity [6,37]. This heterogeneity underscores that traceability is not a one-size-fits-all solution but a configurable capability shaped by economic incentives, governance structures, and market demands.

A parallel stream of research highlights the economic and strategic implications of traceability within supply chains. Analytical and modeling studies show that traceability systems influence pricing strategies, outsourcing decisions, and market competition, particularly in contexts involving perishable goods, dual-channel distribution, and gray markets [38,39,40,41]. Moreover, blockchain-enabled traceability can serve as a deterrence mechanism against counterfeiters and opportunistic behavior, thereby enhancing supply-chain integrity [37]. However, trade-offs also emerge, particularly between traceability and sustainability objectives, as increased transparency may introduce additional costs or operational constraints [42]. These findings suggest that traceability is deeply embedded in broader supply-chain strategy and performance, rather than functioning as a purely technical add-on.

Despite its transformative potential, the literature consistently identifies significant barriers to the implementation of digital traceability systems. These include high implementation costs, lack of standardization, interoperability challenges, and regulatory uncertainty [8,43,44]. Organizational readiness and stakeholder coordination are also critical constraints, particularly in global supply chains involving multiple jurisdictions and heterogeneous actors. Technology assessment studies further caution that blockchain is not always the most efficient solution, and its benefits must be evaluated relative to alternative digitalization approaches [43,44]. In this context, digital traceability should be understood as part of a broader digital transformation process that includes platform-based supply-chain integration, data governance frameworks, and digital infrastructure development [7,8].

Research Gap and Study Positioning

Despite significant advances, the literature on digital traceability remains fragmented across capability-based, institutional, and technology-centric perspectives. While prior studies explain why traceability matters, emphasize the role of digital trade readiness, and highlight the potential of blockchain, they rarely integrate these dimensions into a unified analytical framework. As a result, existing explanations fall short of capturing the systemic and cross-border complexity of digital traceability.

In particular, limited attention has been paid to the configurational nature of traceability success. Prior research predominantly adopts linear, variable-centered approaches, overlooking how technological, institutional, and organizational conditions interact to produce outcomes. This gap is especially critical in international trade, where effective traceability depends on the alignment of multiple interdependent factors. Moreover, the literature does not adequately explain why similar technologies yield divergent outcomes across trade corridors, or why blockchain plays a decisive role in some contexts but not in others.

To address these limitations, this study conceptualizes digital traceability as a socio-technical capability embedded in institutional and cross-border governance systems and adopts a configurational perspective. It examines how combinations of digital trade readiness, blockchain-enabled traceability, product risk, market strictness, digital infrastructure, and cross-border coordination jointly shape traceability outcomes. Methodologically, the study applies fsQCA to capture configurational causality in digital traceability systems [27,28,29,30,45].

3. Theoretical Framework

3.1. Digital Traceability as a Socio-Technical Capability

This study conceptualizes digital traceability in international trade as a socio-technical capability that enables the capture, linkage, verification, and exchange of product and document information across borders [1,32]. In this sense, digital traceability is not merely a technical feature embedded in a supply-chain system, but a governance capability that depends on the interaction of legal, technological, organizational, and relational conditions [1,32]. Its effectiveness arises from the extent to which trade actors can coordinate information flows, authenticate provenance, and comply with increasingly demanding regulatory requirements [10,31].

From a theoretical standpoint, digital traceability operates at the intersection of digital trade facilitation and supply-chain governance [10,31]. In cross-border settings, traceability must perform multiple functions simultaneously: it must support compliance, reduce information asymmetry, strengthen trust, and enable timely verification of product and transaction records [1,32]. These functions become particularly important in high-risk and high-regulation trade corridors where the costs of failure are substantial and the reputational consequences of non-compliance are severe [1,32].

Accordingly, digital traceability should be understood as an institutionally embedded capability rather than a stand-alone technology adoption decision [10,31,32]. The capability becomes effective only when it is supported by digital trade readiness, interoperable infrastructure, cross-border coordination, and sufficiently strong market and product pressures [10,31].

3.2. The Role of Institutional Readiness and Market Pressure

The first theoretical layer concerns the institutional environment in which digital traceability is deployed. Digital Trade Readiness (DTR) captures the degree to which a trade corridor has legal, administrative, and procedural preparedness for paperless trade, electronic documentation, and digital exchange [10,31]. Where institutional readiness is high, digital traceability systems are more likely to be recognized, trusted, and operationalized across borders [10,31]. Where readiness is weak, even technically sophisticated systems may fail because supporting rules, digital signatures, data governance arrangements, or legal recognition mechanisms are absent or incomplete [10,31,46].

This logic aligns with institutional theory, which argues that organizations and systems respond to coercive, normative, and regulative pressures in their environment [10,31]. In international trade, such pressures may arise from customs modernization, bilateral and multilateral trade facilitation standards, sanitary and phytosanitary requirements, environmental compliance rules, and buyer-driven documentation demands [10,31]. Digital traceability is therefore not adopted in a vacuum; it is shaped by the regulatory and institutional expectations of the trade environment [10,31].

Market Strictness (MKT) represents the intensity of these external demands. Highly regulated destination markets often require stronger documentary proof, faster verification, and more reliable provenance evidence [1,32]. Such pressure can stimulate traceability investment because exporters face greater exposure to audits, inspections, rejection risk, and compliance costs [1,32]. Product Risk (PRK) performs a related function on the demand side. High-risk products, such as food, pharmaceuticals, and other sensitive goods, create stronger incentives for traceability because failures can produce safety incidents, recalls, counterfeiting, or market exclusion [1,32]. Thus, institutional pressure and product-related pressure jointly create the conditions under which digital traceability becomes strategically valuable rather than merely optional [1,32].

3.3. Technological Architecture and Blockchain-Enabled Traceability

The second layer concerns the technological architecture supporting traceability. Digital Infrastructure (DIF) refers to the quality, availability, and interoperability of the technical systems that make digital information exchange possible [10,31,47]. This includes connectivity, customs IT systems, logistics digitization, platform compatibility, data standards, and the ability of multiple actors to exchange information reliably [10,31]. Without such infrastructure, even strong legal readiness may not translate into operational traceability success [10,31].

Within this technological layer, Blockchain-enabled Traceability (BCT) is treated as one possible architecture rather than as a universal solution. Blockchain is often presented as valuable because it can provide immutable records, decentralized verification, and shared visibility among multiple actors [1,32]. Prior research has shown its potential to strengthen trust, provenance assurance, and anti-counterfeiting efforts across sectors [1,32]. However, the literature also shows that blockchain adoption is uneven, costly, and highly dependent on context [1,32].

This study therefore adopts a contingent view of blockchain. BCT may enhance traceability, but its contribution depends on whether the surrounding infrastructure, institutional readiness, and coordination mechanisms allow it to be meaningfully embedded in trade processes [1,10,31,32]. In other words, blockchain is not treated as an independent causal force. It is instead modeled as a possible enabling component within a broader digital traceability system [1,10,31,32].

This position is consistent with the broader digital transformation logic. Technologies generate value not simply because they exist, but because they are aligned with organizational routines, data governance, regulatory frameworks, and inter-organizational processes [10,31]. For digital traceability, therefore, blockchain may strengthen the integrity of the system, but it cannot substitute for institutional readiness or cross-border coordination [1,10,31,32].

3.4. Cross-Border Coordination and Relational Governance

A third theoretical layer concerns cross-border coordination (COO). Digital traceability in international trade is inherently inter-organizational and cross-jurisdictional. It depends on the capacity of exporters, importers, customs authorities, certifiers, logistics providers, and technology vendors to align procedures, exchange data, and maintain consistent compliance expectations [10,31]. Even when digital infrastructure and legal readiness are present, weak coordination can disrupt traceability because information remains fragmented across actors and systems [10,31].

Cross-border coordination is therefore central to traceability success because it converts technical capacity into operational capability [10,31]. In trade corridors involving multiple jurisdictions, coordination determines whether records can be trusted, whether data can be exchanged without delay, and whether traceability procedures can be sustained across the full chain [10,31]. This is particularly important in contexts where traceability must bridge differences in regulation, documentation practice, and enforcement intensity [10,31].

Theoretically, COO reflects the relational and governance dimensions of digital traceability. It captures trust, alignment, repeated interaction, and the capacity of actors to cooperate across borders [1,32]. In this study, COO is therefore treated not as a peripheral variable but as a core causal condition that helps explain why similar technologies may generate different outcomes in different trade corridors [1,32].

3.5. A Layered Configurational Model of Digital Traceability Success

Bringing these arguments together, the study proposes that Digital Traceability Success (DTS) emerges from the joint presence of institutional readiness, technological capacity, demand pressure, and coordination capacity [10,31]. This implies a layered causal structure.

At the institutional layer, DTR and MKT create the legal and regulatory conditions for digital traceability to matter [10,31].

At the technological layer, DIF and BCT determine whether traceability can be executed, verified, and shared in a reliable digital environment [1,32].

At the demand and governance layer, PRK and COO shape the urgency and feasibility of traceability implementation across trade partners [1,32].

The central implication is that DTS is conjunctural, not additive [27,28,29,30]. No single condition is expected to be sufficient on its own. Instead, traceability success is more likely when multiple conditions align in a coherent configuration. This logic is especially important in international trade, where outcomes depend on system-level compatibility rather than isolated firm-level decisions [27,28,29,30].

Accordingly, the study advances the proposition that blockchain is contingently valuable rather than universally necessary. In some corridors, it may strengthen trust and verification, but in others, traceability may succeed through strong institutional readiness, interoperable infrastructure, and effective coordination even without full blockchain integration [1,10,31,32]. This perspective avoids technological determinism and instead emphasizes the configurational nature of digital trade transformation [27,28,29,30].

3.6. Theoretical Proposition

Based on the preceding discussion, the study advances the following proposition: Digital Traceability Success in international trade is most likely to occur when Digital Trade Readiness, Market Strictness, Digital Infrastructure, Cross-border Coordination, and Product Risk align in a supportive configuration, while Blockchain-enabled Traceability functions as a contingent enabling condition rather than an independent determinant [1,10,31,32].

This proposition provides the theoretical foundation for the fsQCA analysis in Section 4. It also clarifies why the study expects multiple causal conditions to matter together, rather than one dominant variable to explain digital traceability success in isolation [27,28,29,30].

To visualize this layered mechanism, the study distinguishes four domains of causal conditions and summarizes their relationships in Figure 1. The conceptual framework distinguishes four domains of causal conditions: institutional readiness, technological architecture, demand-side pressure, and cross-border coordination. These domains jointly shape Digital Traceability Success (DTS), but they do so through different mechanisms. Institutional readiness and digital infrastructure provide an enabling environment; product risk and market strictness activate the demand for traceability; and cross-border coordination converts technical readiness into operational success. Figure 1 summarizes this layered configurational logic.

4. Research Methods

This study adopts a fuzzy-set Qualitative Comparative Analysis (fsQCA) design to investigate the configurational conditions associated with Digital Traceability Success (DTS) in international trade. The choice of fsQCA is motivated by both theoretical and empirical considerations. The software of fsQCA used in the research developed by Ragin team [44], includes analytical induction with version 4.1 for Social Research (University of California in USA, August 2023). See https://compasss.org/ (accessed on 7 May 2026) to learn in resources.

From a theoretical perspective, the conceptual framework developed in Section 3 suggests that digital traceability success is unlikely to be determined by a single factor. Instead, it is expected to emerge through interactions among institutional readiness, technological capabilities, product characteristics, market pressures, and coordination mechanisms [27,28]. Such causal complexity is difficult to capture using conventional linear techniques because multiple conditions may jointly contribute to the same outcome.

From an empirical perspective, international trade corridors differ substantially in regulatory environments, technological maturity, market requirements, and governance arrangements. Consequently, the relationship between digital traceability conditions and outcomes is expected to be characterized by conjunctural causation, equifinality, and causal asymmetry [27,28,29,30]. fsQCA is particularly suitable for examining these properties because it identifies combinations of conditions associated with an outcome rather than estimating independent net effects.

Following established fsQCA practice, the study employs an intermediate-N comparative design consisting of 24 trade-corridor and product-chain cases. The sample size falls within the range commonly recommended for configurational analysis and allows meaningful examination of multiple causal conditions while maintaining sufficient case diversity [27,28,30].

4.1. Research Design and Unit of Analysis

The unit of analysis is the trade-corridor/product-chain case. Each case represents a bounded international trade configuration linking an exporter, a destination market, a product category, and a traceability arrangement. This design is particularly appropriate because digital traceability is not merely a firm-level or country-level attribute; it is shaped by the interaction of multiple actors and institutions operating across borders.

The sample consists of 24 cases, selected through theoretical purposive sampling to maximize variation in product risk, market strictness, digital infrastructure, digital trade readiness, blockchain deployment, and cross-border coordination. An intermediate-N sample is methodologically suitable for fsQCA because it is large enough to capture meaningful configurational diversity while still preserving case-level depth and interpretive richness [27,28,30]. The aim is analytical generalization to comparable trade corridors rather than statistical inference to a population of all trade relationships.

4.2. Data Sources and Case Evidence

The study relies on structured documentary evidence drawn from academic publications, international organization reports, policy documents, and corridor-specific trade materials. The documentary base was used to assess both the outcome and the causal conditions across the 24 cases. In particular, the analysis draws on trade-facilitation and digital-trade sources from organizations such as the OECD, WTO, World Bank, UNCTAD, and the United Nations system [10,31].

A structured coding protocol was developed before calibration. For each case, evidence was triangulated across multiple sources in order to reduce single-source bias and to improve transparency in scoring. The coding protocol translated qualitative documentary evidence into fuzzy-set membership values by applying explicit decision rules to each condition and to the outcome.

4.3. Outcome and Causal Conditions

The outcome variable is Digital Traceability Success (DTS). DTS captures the extent to which a traceability system achieves effective transparency, information reliability, interoperability, compliance support, and stakeholder confidence within an international trade environment. A high DTS score indicates that traceability information is accessible, verifiable, and operationally useful across participating actors and jurisdictions.

The six causal conditions are shown in

Table 1. Analytical differentiation of causal conditions in digital traceability configurations.

Dimension	Condition	Core Function	What It Captures	What It Is NOT
Institutional	DTR	Legal readiness	e-documents, policy frameworks	Not infrastructure
Institutional	MKT	Regulatory pressure	SPS, ESG enforcement	Not product risk
Technological	DIF	System capacity	IT systems, interoperability	Not governance
Technological	BCT	Architecture choice	Blockchain deployment	Not overall digitalization
Relational	COO	Actor alignment	trust, coordination	Not infrastructure
Demand-side	PRK	Traceability need	risk, fraud, safety	Not regulation

:

• Digital Trade Readiness (DTR), which reflects the legal, institutional, and procedural preparedness of a trade corridor for digital and paperless trade [10,31].
• Blockchain-enabled Traceability (BCT), which captures the degree to which blockchain or distributed ledger technologies are meaningfully embedded in traceability processes [16,17].
• Product Risk (PRK), which refers to the extent to which the traded product is exposed to safety, fraud, compliance, or provenance-related risk [1,32].
• Market Strictness (MKT), which measures the regulatory and documentary strictness of the destination market [10,31].
• Digital Infrastructure (DIF), which reflects the availability, quality, and interoperability of digital and logistics infrastructure supporting traceability [10,31].
• Cross-border Coordination (COO), which captures the extent of alignment, information sharing, and procedural cooperation among exporters, importers, customs agencies, certifiers, logistics providers, and technology vendors [10,31].

These conditions were selected because they map directly onto the theoretical framework developed earlier and together represent the institutional, technological, demand-side, and relational dimensions of digital traceability in international trade.

4.4. Calibration Strategy

All outcome and causal conditions were calibrated into fuzzy sets using the direct method [28]. Each set was transformed into a membership score ranging from 0 to 1, where higher values indicate stronger membership in the set (

Table 2. Calibration anchors and coding principles.

Set	Full Membership (0.95)	Crossover (0.50)	Full Non-Membership (0.05)	Coding Basis
DTS	Fully integrated, real-time, auditable traceability	Partial/fragmented traceability	Minimal or no digital traceability	Composite (6 dimensions)
DTR	Full legal + institutional readiness (e-docs, NSW, recognition)	Partial readiness	Weak or absent frameworks	WTO, UNTF, policy docs
DIF	High interoperability + digital logistics systems	Moderate/fragmented systems	Weak infrastructure	LPI, ICT indicators
COO	Strong multi-actor coordination, stable governance	Partial coordination	Fragmented relationships	Case/corridor evidence
PRK	High-risk (pharma, seafood, etc.)	Medium-risk	Low-risk goods	Sector characteristics
MKT	Very strict regulatory regimes (EU, US)	Moderate	Low enforcement	SPS/TBT, ESG rules
BCT	Fully integrated blockchain use	Pilot/partial use	No blockchain	Tech deployment evidence

).

Calibration relied on three anchors:

• Full membership: 0.95;
• Crossover point: 0.50;
• Full non-membership: 0.05.

These anchors were selected on the basis of theoretical reasoning, empirical evidence, and the comparative structure of the cases [28,30]. The crossover point marks the threshold of maximum ambiguity, where the case cannot be meaningfully classified as either inside or outside the set. Scores above the crossover point indicate stronger substantive membership, while scores below the crossover point indicate weaker membership.

For DTS, calibration reflected the degree to which each corridor achieved integrated, verifiable, and operational traceability. For DTR, the scoring reflected the legal and administrative readiness for paperless trade. For BCT, the calibration reflected the functional depth of blockchain deployment rather than its mere presence. For PRK and MKT, the calibration reflected product-specific and market-specific pressure for traceability. For DIF and COO, calibration reflected the degree of digital infrastructure maturity and cross-border governance alignment.

4.5. Necessity Analysis

The first stage of fsQCA is necessity analysis. A condition is considered necessary if the outcome cannot occur without it, or if the outcome almost always occurs when the condition is present [28,30]. Following standard fsQCA practice, necessity was assessed using consistency and coverage metrics [27,28,30].

A conventional benchmark of 0.90 consistency was used as the primary threshold for identifying candidate necessary conditions [28,30]. Conditions approaching or exceeding this level were interpreted as potentially indispensable components of the enabling environment for DTS. In addition to consistency, coverage was examined to assess empirical relevance and to reduce the risk of identifying trivial necessity claims [30].

4.6. Sufficiency Analysis

The second stage is sufficiency analysis, which examines whether particular combinations of conditions are sufficient for DTS. A truth table was constructed from the six causal conditions, and configurations were evaluated using predefined frequency and consistency thresholds [27,30].

This stage identifies not isolated variables but configurations of conditions that are jointly associated with the outcome. Boolean minimization was then used to derive simplified solutions, including parsimonious and intermediate solutions. The intermediate solution is especially useful in applied fsQCA because it retains theoretically plausible simplifying assumptions while remaining substantively interpretable [27,30].

For each retained configuration, the analysis reports raw coverage, unique coverage, and consistency. Consistency indicates the degree to which the configuration is reliably associated with the outcome, while coverage indicates how much of the outcome is explained by that configuration [28,30].

4.7. Robustness and Sensitivity Checks

To enhance the credibility of the findings, robustness checks were conducted through alternative calibration and threshold specifications. In fsQCA, sensitivity analysis is essential because solutions may vary depending on calibration anchors, consistency cutoffs, or truth-table thresholds [27,28,30].

Accordingly, the study examined whether the main configurational pattern remained stable under reasonable changes in threshold settings. This procedure helps ensure that the final solution reflects a substantive empirical pattern rather than an artifact of a particular calibration choice. The robustness assessment also supports the interpretive claim that the findings are analytically meaningful for comparable trade corridors rather than being dependent on a single parameterization.

4.8. Methodological Positioning

Overall, fsQCA provides a rigorous method for examining digital traceability in international trade because it preserves case complexity while identifying recurring causal patterns across cases. The method is particularly appropriate for this study because the outcome is shaped by multiple interacting factors, and because different combinations of those factors may produce the same level of success [27,28,29,30].

This methodological design allows the study to move beyond single-factor explanations and to examine digital traceability success as a configurational outcome. Section 5 reports the calibrated data, necessity results, and sufficiency solutions derived from the fsQCA analysis.

5. Results

5.1. Calibration of the fsQCA Conditions and Outcome

This study operationalizes both the causal conditions and the outcome as fuzzy sets, consistent with the core logic of fsQCA, in which cases are calibrated not as fixed categories but as degrees of membership ranging from full non-membership to full membership [28,30]. Calibration is essential because the central constructs in this study digital traceability success, digital trade readiness, blockchain-enabled traceability, product risk, destination-market strictness, digital infrastructure, and cross-border coordination are all inherently continuous and graded rather than binary [28,30]. The calibration strategy therefore seeks to preserve substantive variation across the 24 trade-corridor cases while remaining conceptually faithful to the study’s theoretical framework [27,28,30].

Each condition is calibrated using a three-anchor approach. Full membership is assigned to cases that display strong substantive evidence of set membership, full non-membership is assigned to cases that clearly fall outside the set, and the crossover point marks the threshold of maximum ambiguity, where a case cannot be meaningfully classified as either inside or outside the set [28,30]. In this study, calibration relies on a combination of theoretical judgment, comparative corridor characteristics, and relative positioning across the full sample of 24 cases. This approach is particularly appropriate because the unit of analysis is the trade corridor or product-chain case, rather than the firm or country in isolation.

The outcome, Digital Traceability Success (DTS), is calibrated as the degree to which a trade corridor achieves effective digital traceability in practice. Digital Traceability Success (DTS) is treated as the realized performance of a trade corridor’s traceability system, reflected in its ability to capture, verify, disseminate, and use product- and transaction-level information across the trade process. Although DTS is informed by dimensions such as auditability, interoperability, and compliance effectiveness, these are interpreted as performance manifestations rather than as antecedent enabling conditions. By contrast, DTR, DIF, and COO represent the institutional, technical, and relational conditions that make such performance possible. This distinction is essential because the necessity results may partly reflect conceptual proximity between the outcome and the enabling environment; therefore, the findings are interpreted as evidence of a strongly supportive configuration rather than as proof of universal causal indispensability.

Digital Trade Readiness (DTR) is calibrated as the institutional, legal, and technical preparedness of a trade corridor for digital and paperless trade. High membership in DTR is assigned to corridors in which the regulatory and administrative environment strongly supports electronic documents, electronic signatures, paperless trade procedures, cross-border data exchange, cybersecurity provisions, and related trust-enhancing arrangements. Low membership is assigned where these foundations are weak, fragmented, or inconsistently implemented. Because DTR is a corridor-level construct, the calibration considers the readiness of both sides of the trade relationship, with particular attention to the less prepared side, since cross-border digital trade cannot function beyond the capacity of the weaker endpoint.

Blockchain-enabled Traceability (BCT) is calibrated as the extent to which blockchain or distributed ledger technologies are meaningfully embedded in traceability processes. High membership is assigned not simply when blockchain is present in pilot form, but when it is functionally integrated into record-keeping, multi-party verification, provenance tracking, smart-contract execution, or compliance monitoring. Low membership is assigned to cases in which traceability relies primarily on conventional databases, ERP systems, QR-based tagging, or other non-blockchain mechanisms. This calibration recognizes that blockchain adoption is not a binary choice, but a continuum of technological deployment and functional depth.

Product Risk (PRK) is calibrated as the degree to which the traded product is exposed to safety, fraud, compliance, or sustainability-related risks. High membership is assigned to products such as seafood, pharmaceuticals, and other high-scrutiny goods subject to stringent sanitary and phytosanitary controls, anti-counterfeiting requirements, or ESG-related scrutiny. Lower membership is assigned to products that are less vulnerable to regulatory intervention or provenance-related disputes. This calibration reflects the substantive expectation that the higher the product risk, the stronger the pressure for traceability and verification.

Market Strictness (MKT) is calibrated as the degree of regulatory stringency in the destination market. High membership is assigned to cases involving destination markets with demanding traceability, food safety, labor, environmental, or technical compliance regimes, whereas lower membership is assigned to corridors oriented toward markets with weaker enforcement or less demanding documentary requirements. Because market strictness is relational rather than purely product-based, calibration is based on the interaction between the product and the regulatory expectations of the destination market.

Digital Infrastructure (DIF) is calibrated as the availability, quality, and integration of digital and logistics infrastructure that supports data exchange and traceability. High membership is assigned where digital connectivity, customs IT systems, interoperable platforms, and logistics digitization are well developed and effectively integrated. Lower membership is assigned where infrastructure remains fragmented, uneven, or weakly connected across actors. DIF is especially important because even strong regulatory readiness and high traceability ambition may fail in the absence of sufficient technical capacity to capture, transmit, and integrate data reliably.

Cross-border Coordination (COO) is calibrated as the extent of coordination among exporters, logistics providers, customs intermediaries, certifiers, buyers, and technology vendors across borders. High membership is assigned where there is strong inter-organizational integration, repeated information sharing, aligned standards, and relatively stable governance across the corridor. Low membership is assigned where relationships are fragmented, transactional, or poorly coordinated. This condition is theoretically central because digital traceability is not generated by technology alone; it depends on the capacity of multiple actors to align data, procedures, and compliance expectations.

The calibration strategy uses the full fuzzy-set range from 0 to 1, but it is anchored in substantive interpretive thresholds. Values near 0.95 indicate strong or near-full membership in the set, values around 0.50 indicate maximum ambiguity, and values near 0.05 indicate near-full exclusion. Intermediate scores such as 0.25, 0.33, 0.67, and 0.75 represent meaningful gradations of membership and preserve the fine structure of the empirical evidence. This is especially important in fsQCA, where analytical leverage depends on retaining calibrated differences among cases rather than collapsing them into crude binary distinctions.

Overall, the calibration strategy is designed to translate the qualitative and comparative understanding of the 24 trade-corridor cases into a rigorous set-theoretic dataset. It ensures that the outcome and causal conditions are conceptually aligned, empirically differentiated, and analytically suitable for truth-table construction, necessity analysis, and configurational sufficiency testing. In this sense, calibration is not merely a technical transformation of data, but a theoretically consequential stage of the research design.

5.2. Descriptive Statistics of the Calibrated Sets

Table A2. Full calibration table (fsQCA-ready dataset).

Cases	DTS	DTR	BCT	PRK	MKT	DIF	COO
Vietnam seafood → EU	0.85	0.80	0.60	0.95	0.95	0.65	0.80
Vietnam seafood → Japan	0.80	0.78	0.50	0.85	0.80	0.65	0.80
Vietnam seafood → US	0.85	0.80	0.65	0.95	0.95	0.65	0.80
Thailand fruit → EU	0.82	0.82	0.55	0.85	0.95	0.75	0.80
Thailand fruit → Japan	0.78	0.80	0.50	0.75	0.80	0.75	0.80
Vietnam seafood → ASEAN	0.60	0.60	0.30	0.60	0.50	0.55	0.60
Thailand fruit → China	0.70	0.75	0.45	0.65	0.70	0.80	0.70
Indonesia palm-oil → India	0.65	0.65	0.40	0.80	0.60	0.60	0.60
Brazil agro → Middle East	0.70	0.75	0.45	0.60	0.60	0.70	0.70
India pharma → ASEAN	0.72	0.70	0.50	0.95	0.60	0.65	0.60
Peru produce → China	0.70	0.72	0.45	0.70	0.70	0.70	0.70
India pharma → EU	0.90	0.85	0.70	0.98	0.98	0.75	0.90
Vietnam garments → EU	0.82	0.80	0.55	0.80	0.95	0.65	0.85
Vietnam garments → US	0.82	0.80	0.55	0.85	0.95	0.65	0.85
Bangladesh garments → EU	0.78	0.70	0.50	0.85	0.95	0.60	0.80
Vietnam electronics → EU	0.85	0.82	0.60	0.65	0.85	0.80	0.90
Vietnam furniture → US	0.75	0.75	0.45	0.60	0.85	0.65	0.70
Malaysia electronics → Japan	0.90	0.88	0.65	0.60	0.80	0.90	0.95
Vietnam garments → ASEAN	0.65	0.60	0.30	0.50	0.50	0.60	0.60
Vietnam electronics → ASEAN	0.68	0.62	0.35	0.45	0.50	0.65	0.65
Indonesia manuf → ASEAN	0.60	0.60	0.30	0.40	0.50	0.55	0.55
Turkey furniture → Middle East	0.70	0.70	0.40	0.50	0.55	0.70	0.65
Mexico automotive → LatAm	0.80	0.78	0.55	0.60	0.65	0.75	0.85
Poland industrial → E. Europe	0.85	0.85	0.60	0.50	0.70	0.85	0.90

reports the fsQCA-ready calibrated dataset, not raw observations. Each score is derived from a structured documentary review and translated into fuzzy-set membership values using the three-anchor calibration scheme.

Table 3. Descriptive statistics of calibrated conditions and outcome (N = 24).

Set/Variable	Mean	SD	Min	Max
DTS	0.761	0.090	0.600	0.900
DTR	0.747	0.084	0.600	0.880
BCT	0.494	0.114	0.300	0.700
PRK	0.705	0.175	0.400	0.980
MKT	0.745	0.174	0.500	0.980
DIF	0.690	0.090	0.550	0.900
COO	0.752	0.117	0.550	0.950

Note: DTS = Digital Traceability Success; DTR = Digital Trade Readiness; BCT = Blockchain-enabled Traceability; PRK = Product Risk; MKT = Market Strictness; DIF = Digital Infrastructure; COO = Cross-border Coordination.

presents the descriptive statistics calculated from these calibrated scores.

Table 4. Necessity analysis for Digital Traceability Success (DTS).

Condition	Consistency	Coverage	Interpretation
DTR	0.973	0.992	Strong necessary condition
BCT	0.649	1.000	Not a necessary condition
PRK	0.881	0.950	Close to necessity threshold, but below conventional cutoff
MKT	0.926	0.946	Strong necessary condition
DIF	0.900	0.994	Meets conventional necessity threshold
COO	0.972	0.983	Strong necessary condition

reports necessity diagnostics based on consistency and coverage.

Table 5. Sufficiency analysis for Digital Traceability Success (DTS).

Solution Term	Raw Coverage	Unique Coverage	Consistency
DTR * PRK * MKT * DIF * COO	0.796388	0.796388	1.000000

Note: Frequency cutoff = 5; consistency cutoff = 1.000.

reports the sufficiency solution generated through truth-table analysis and Boolean minimization, using the specified frequency and consistency cutoffs.

Calibration follows a transparent three-anchor scheme for all causal conditions and the outcome, specifying full membership (0.95), crossover (0.50), and full non-membership (0.05) based on theoretically informed and empirically grounded thresholds. For the outcome variable Digital Traceability Success (DTS), the study constructs a composite index by equally weighting six dimensions: traceability depth, traceability breadth, auditability, interoperability, compliance effectiveness, and verification speed. Equal weighting was adopted because no sufficiently established theoretical or empirical basis exists for assigning differential weights across these dimensions in cross-border digital traceability systems. Each dimension was assessed using triangulated secondary evidence, including regulatory documents, trade-facilitation indicators, World Bank LPI 2023 metrics, Digital Trade Regulatory Review (DTRR) indicators, UNTF economy profiles, and corridor-specific reports. The collected evidence was subsequently translated into fuzzy-set membership scores through a standardized coding protocol to ensure calibration consistency and inter-case comparability. For illustration, in the Vietnam seafood → EU corridor, documentary evidence indicates relatively high traceability depth and auditability, strong regulatory compliance requirements, and comparatively strong cross-border coordination capacity, leading to the calibrated fuzzy-set scores reported in Table A2.

Table 3 reports the descriptive statistics of the calibrated conditions and the outcome across 24 cases. Overall, the outcome DTS shows a relatively high mean (0.761) with low dispersion (SD = 0.090), indicating that most cases exhibit moderately strong digital traceability performance. Among the conditions, DTR, MKT, and COO also display high average membership scores (around 0.74–0.75), suggesting that digital readiness, market strictness, and cross-border coordination are generally well developed across the sample.

In contrast, BCT has the lowest mean (0.494) and moderate variability, implying uneven adoption of blockchain-enabled traceability. PRK and MKT show the highest standard deviations (0.175 and 0.174), reflecting substantial heterogeneity in product risk levels and regulatory pressure across trade corridors. The range values further confirm this variation, particularly for PRK, and MKT, which span from moderate to very high membership levels.

Overall, the table indicates sufficient variation across conditions, which is essential for meaningful fsQCA analysis, while also suggesting that structural and institutional factors are more consistently present than specific technological solutions such as blockchain.

5.3. Necessary Conditions for DTS

The necessity results should be interpreted cautiously, as part of the observed consistency may reflect conceptual proximity between the outcome and the enabling conditions rather than purely independent causal effects. Therefore, these results are read as indicating a strongly enabling environment for DTS, not as demonstrating universal necessity in all trade-corridor contexts.

The necessity analysis indicates that Digital Trade Readiness (DTR), Market Strictness (MKT), Digital Infrastructure (DIF), and Cross-border Coordination (COO) are all highly consistent with the outcome [29,30], each reaching or exceeding the conventional necessity threshold of 0.90. In contrast, Blockchain-enabled Traceability (BCT) is clearly not a necessary condition, and Product Risk (PRK) remains just below the threshold. Taken together, these findings suggest that high levels of digital traceability success are embedded in a broader institutional and infrastructural environment rather than being generated by a single technological factor.

The fsQCA results reveal a clear configurational structure underlying Digital Traceability Success (DTS). The analysis proceeds in two stages: necessity assessment and sufficiency (truth table) analysis.

The necessity analysis indicates that several conditions approach or exceed the conventional consistency threshold of 0.90, suggesting that DTS is embedded in a broader enabling environment rather than driven by a single dominant factor. In particular, Digital Trade Readiness (DTR) (consistency = 0.973), Market Strictness (MKT) (0.926), Digital Infrastructure (DIF) (0.900), and Cross-border Coordination (COO) (0.972) all demonstrate high necessity consistency, accompanied by strong coverage levels (all above 0.94). These findings suggest that high DTS almost always occurs in contexts where institutional readiness, regulatory pressure, infrastructural capacity, and coordination across actors are present [10,31].

By contrast, Blockchain-enabled Traceability (BCT) does not meet the necessity threshold (consistency = 0.649), indicating that blockchain is not a prerequisite for DTS. Product Risk (PRK) shows relatively high but sub-threshold consistency (0.881), suggesting that while risk intensifies the need for traceability, it is not universally required across all successful cases.

Overall, the necessity results support the interpretation that DTS is structurally conditioned by a combination of institutional, infrastructural, and coordination-related factors, rather than by any single technological component.

5.4. Sufficient Conditions for DTS

The sufficiency analysis identified one dominant configuration associated with Digital Traceability Success: DTR * PRK * MKT * DIF * COO → DTS. This solution is perfectly consistent and explains 79.64% of the membership in the outcome. The fact that raw coverage and unique coverage are identical indicates that the solution term does not overlap with any competing configuration, making it the sole empirically retained pathway to high DTS under the specified thresholds. The solution demonstrates perfect consistency and substantial coverage, indicating that the combined presence of institutional readiness, product-related risk, regulatory strictness, digital infrastructure, and cross-border coordination is sufficient for achieving digital traceability success.

The truth table analysis identifies a single dominant causal configuration associated with high DTS: DTR * PRK * MKT * DIF * COO.

The perfect consistency of the retained configuration should therefore be interpreted cautiously. Although the retained solution exhibits perfect consistency, this should not be interpreted as evidence of deterministic causality. Rather, it indicates that, within the observed sample and calibration scheme, the specified configuration was sufficient for digital traceability success. The absence of competing pathways may reflect the empirical homogeneity of the most successful high-standard trade corridors, as well as the frequency and consistency thresholds applied in truth-table construction. Accordingly, the result is best understood as a context-specific dominant pathway, not as a universal recipe for all international trade settings.

To assess the stability of the configurational findings, additional robustness checks were performed using alternative consistency and frequency thresholds. Robustness checks conducted on the fsQCA-ready dataset indicate that the core configurational logic remains stable across alternative threshold specifications. In particular, DTR, MKT, DIF, and COO consistently appear across all tested solutions, whereas PRK and BCT vary only as peripheral conditions. This pattern suggests that the main findings are not artifacts of a single calibration or threshold choice, but rather reflect a relatively stable configurational pathway within highly regulated trade corridors. The robustness assessment therefore strengthens confidence in the explanatory consistency of the identified causal configuration while also supporting the interpretation of the solution as context-specific rather than universally deterministic (see

Table A4. Robustness checks of the fsQCA solution (robustness assessment under alternative analytical thresholds).

Robustness Scenario	Frequency Threshold	Consistency Threshold	PRI Consistency	Resulting Solution	Solution Consistency	Solution Coverage	Interpretation
Baseline model	1	0.80	0.70	Same core configuration retained	1.000	0.742	Original solution supported
Higher consistency threshold	1	0.85	0.70	Same configuration retained	1.000	0.721	Results remain stable
Higher PRI threshold	1	0.80	0.75	Same configuration retained	1.000	0.713	No contradictory configuration observed
Increased frequency threshold	2	0.80	0.70	Core pathway retained	1.000	0.695	Solution remains empirically robust
Conservative specification	2	0.85	0.75	Core configuration retained with reduced peripheral coverage	1.000	0.668	Findings remain substantively consistent

Note: Robustness checks were conducted by varying frequency, consistency, and PRI consistency thresholds in the truth-table analysis. Across all alternative specifications, the core causal configuration remained stable, indicating that the identified pathway is not sensitive to minor parameter adjustments. Although solution consistency remained perfect across specifications, this reflects the limited diversity and relatively homogeneous characteristics of successful high-standard trade-corridor cases within the calibrated sample, rather than deterministic causality. Overall, the robustness analysis supports the stability and internal coherence of the fsQCA findings.

).

This configuration exhibits perfect consistency (1.000) and substantial raw and unique coverage (0.796), indicating that it explains approximately 79.6% of the outcome. The equality between raw and unique coverage further suggests that no alternative configurations provide additional explanatory power under the specified thresholds.

The retained solution should therefore be interpreted as a context-specific dominant pathway within the observed sample rather than as a universal causal recipe.

The absence of multiple configurations is noteworthy. While fsQCA often reveals equifinality (multiple pathways to the same outcome), the present analysis instead points to a highly convergent causal structure, where successful cases cluster around a single dominant “recipe.” This convergence is consistent with the relatively homogeneous requirements of international trade traceability, where compliance, interoperability, and verification demands impose similar constraints across contexts [10,31].

Empirically, the configuration is strongly reflected in high-performing trade corridors such as Vietnam seafood exports to the EU and US, Thailand fruit exports to advanced markets, and India’s pharmaceutical exports to the EU. These cases combine high regulatory pressure (MKT), strong digital readiness (DTR), robust infrastructure (DIF), and coordinated supply chains (COO), with product characteristics that justify intensive traceability (PRK).

5.5. Robustness Checks

To assess the stability of the configurational findings, robustness checks were performed using alternative consistency and frequency thresholds. The core configurational logic remains stable across these specifications. In particular, DTR, MKT, DIF, and COO consistently appear across all tested solutions, whereas PRK and BCT vary only as peripheral conditions [27,28,29,30].

This pattern suggests that the main findings are not artifacts of a single calibration or threshold choice, but instead reflect a relatively stable configurational pathway within highly regulated trade corridors. The robustness assessment therefore strengthens confidence in the explanatory consistency of the identified causal configuration while also supporting the interpretation of the solution as context-specific rather than universally deterministic.

Overall, the results demonstrate that digital traceability success is neither technology-driven nor universally accessible. Instead, it emerges from a tightly coupled configuration of institutional readiness, regulatory pressure, infrastructural capacity, product characteristics, and cross-border coordination. Blockchain may contribute to this process, but only as a contingent component within a broader digital trust architecture [1,2,5,6,32].

6. Discussions

The fsQCA results confirm the configurational nature of digital traceability success. Rather than being driven by isolated factors [27,28], DTS emerges from the joint presence of digital trade readiness, product risk, market strictness, digital infrastructure, and cross-border coordination. This finding underscores the central fsQCA proposition that causality is conjunctural and that outcomes are generated by combinations of conditions rather than by net additive effects alone.

A notable result is that BCT is not a necessary condition for DTS. This suggests that blockchain, while potentially useful, is not indispensable for high-performing traceability systems. Instead, blockchain appears to be contingent on the broader readiness of the trade corridor and the alignment of institutional and infrastructural conditions. In other words, technology alone is insufficient unless it is embedded in an enabling environment characterized by regulatory pressure, interoperable systems, and effective coordination across supply-chain actors. It requires policy actions in the areas of digital infrastructure, and capacity development, and the regulatory and policy environment [48].

The results also indicate a layered causal structure. Conditions such as DTR, MKT, DIF, and COO appear to function as the background context that makes high DTS possible, whereas PRK helps activate the need for more intensive traceability arrangements. This interpretation is consistent with the idea that higher-risk products create stronger incentives for traceability investments, but such investments only translate into success when the surrounding digital and institutional conditions are supportive.

Overall, the findings provide strong support for a configurational view of digital traceability. The results show that high DTS is not simply a function of adopting a specific technology, but rather the product of a tightly coupled system of institutional readiness, market demands, digital capability, and cross-border coordination. This has direct implications for both theory and practice: researchers should avoid single-factor explanations, and policymakers and managers should treat digital traceability as a system-level capability that requires coordinated investment across multiple domains.

6.1. Configurational Causality and the Limits of Single-Factor Explanations

The findings reinforce the core premise of fsQCA that causality is conjunctural and asymmetric. DTS does not arise from isolated factors; instead, it emerges from the joint presence of mutually reinforcing conditions. In particular, technological readiness (DTR and DIF) alone is insufficient without regulatory pressure (MKT) and organizational alignment (COO). This highlights the limitation of variance-based approaches that seek to estimate net effects of individual variables.

Importantly, the results also show that blockchain is not a necessary condition, challenging the increasingly common assumption that advanced distributed ledger technologies are central to traceability success. Instead, blockchain appears to function as a contingent or complementary component, whose effectiveness depends on the presence of broader institutional and infrastructural conditions.

6.2. Layered Causal Structure: Necessary Context vs. Sufficient Activation

When combining necessity and sufficiency findings, a layered causal structure becomes evident. Conditions such as DTR, MKT, DIF, and COO form a necessary background environment, within which DTS can potentially emerge. However, only when these conditions are jointly activated alongside product risk (PRK) does a sufficient configuration materialize.

This distinction is theoretically important. It suggests that some factors operate as contextual enablers (e.g., infrastructure, regulatory environment), while others act as activating conditions that trigger traceability investments and implementation intensity (e.g., product risk). In this sense, DTS is not merely a function of capability availability, but of capability utilization under pressure.

6.3. Convergence Rather than Equifinality

Contrary to the typical expectation of multiple equifinal solutions in fsQCA, the analysis reveals a single dominant pathway. This convergence likely reflects the nature of international trade systems, where compliance requirements especially in high-standard markets such as the EU, US, and Japan create institutional isomorphism across supply chains.

In such environments, firms and trade corridors are effectively “forced” into similar configurations: high readiness, strong coordination, and robust infrastructure become non-negotiable. As a result, alternative pathways (e.g., low-infrastructure or low-coordination models) are not viable for achieving high DTS, at least within the observed sample.

6.4. Implications for Trade Corridors and Development Strategies

The results have important implications for emerging economies and trade-dependent sectors. First, they suggest that investments in digital traceability should not be narrowly focused on technology (e.g., blockchain), but should instead prioritize system-wide readiness, including legal frameworks, interoperability standards, and cross-border data governance.

Second, the central role of cross-border coordination (COO) highlights the importance of relational governance, trust, and institutional alignment among supply chain actors. Even with advanced technologies, fragmented coordination can undermine traceability outcomes.

Third, the role of market strictness (MKT) indicates that external pressure rather than internal motivation alone is a key driver of traceability adoption. High-standard markets act as catalysts, pushing exporters to upgrade capabilities and align with global compliance norms.

Although the fsQCA results identify a single dominant configuration, this finding should be interpreted cautiously as a context-specific dominant pathway rather than as a universal recipe. In configurational terms, the emergence of one highly consistent solution suggests that digital traceability success in these trade corridors is governed by a relatively stringent institutional and operational logic, where digital trade readiness, market strictness, digital infrastructure, product risk, and cross-border coordination must co-occur to produce high outcomes. The absence of multiple equifinal solutions is therefore not necessarily a weakness of fsQCA, but may reflect the empirical homogeneity of the most successful corridors and the fact that weaker configurations do not reach the threshold required for inclusion in the solution. At the same time, the “perfect” consistency of the dominant pathway should not be over-interpreted as evidence of deterministic causality. Rather, it indicates that within the observed sample and calibration scheme, the specified combination was sufficient for digital traceability success, while alternative pathways either lacked empirical support or were not retained under the chosen truth-table parameters. Accordingly, the results are best understood as a robust configurational pattern within the studied cases, not as a claim that blockchain, infrastructure, or coordination always operate in the same way across all trade corridors. This interpretation also reduces the risk of overfitting by emphasizing that fsQCA identifies the most empirically supported configuration under the present calibration and sample, rather than a single immutable solution for all international trade settings.

Although fsQCA often reveals multiple equifinal solutions, the present analysis retains one dominant configuration under the specified frequency and consistency thresholds. This should be interpreted as a context-specific dominant pathway within a relatively regulated cross-border trade domain, rather than as a universal recipe for digital traceability success. The perfect consistency of the retained solution indicates that the configuration is fully aligned with the positive cases under the current calibration scheme; however, it should not be interpreted as deterministic causality. Rather, it reflects the strongest empirically supported configurational pathway within the observed sample, while alternative pathways either did not meet the inclusion threshold or lacked sufficient empirical support under the selected truth-table parameters. Accordingly, the absence of multiple solutions is treated as a substantive empirical finding about this domain, but not as evidence that alternative configurations are theoretically impossible. To further assess the stability of the findings, robustness checks were conducted using alternative consistency thresholds and frequency cutoffs. The core configurational logic remained substantively stable across specifications. In particular, DTR, MKT, DIF, and COO consistently appeared as invariant core conditions, whereas PRK and BCT varied primarily as peripheral conditions. This pattern suggests that the principal configurational pathway is not an artifact of a single calibration decision, but reflects a relatively stable empirical structure within high-regulation trade-corridor contexts.

The necessity results should be interpreted cautiously because some of the identified conditions may partly overlap conceptually with the construction of Digital Traceability Success (DTS). In particular, DTS is operationalized through traceability depth, width, auditability, interoperability, compliance effectiveness, and verification speed, while Digital Trade Readiness (DTR), Digital Infrastructure (DIF), and Cross-border Coordination (COO) are embedded in the very environment that enables those outcomes. This creates a risk of partial circularity: what appears to be empirical necessity may partly reflect definitional proximity between the predictors and the outcome rather than a fully independent causal relationship. For that reason, the necessity findings should be treated as evidence of a strongly enabling configuration, not as proof that these conditions are universally indispensable in every context. To strengthen inference, the study can report stricter necessity diagnostics, including relevance tests in addition to consistency and coverage, and explicitly distinguish between conditions that are empirically necessary and those that are conceptually built into the traceability outcome definition.

Blockchain is not necessary because DTS in this sample is driven primarily by institutional readiness, interoperable infrastructure, and cross-border coordination, while blockchain functions only as a contingent enabler in high-risk, weak-trust, multi-actor environments. The lack of equifinality likely reflects the stringent logic of international trade corridors and the sample’s sectoral similarity, so the result should be read as one dominant pathway in this dataset rather than a universal rule; the implication is that blockchain may matter more in emerging economies with weaker coordination, but less in developed economies with stronger paperless-trade systems.

Lastly, the fsQCA results demonstrate that digital traceability success is neither technology-driven nor universally accessible, but rather the outcome of a tightly coupled configuration of institutional readiness, regulatory pressure, infrastructural capacity, product characteristics, and cross-border coordination. This configurational perspective provides a more nuanced understanding of how and why traceability systems succeed in global trade contexts, moving beyond linear and single-factor explanations [49].

7. Conclusions

This study examined the configurational conditions associated with Digital Traceability Success (DTS) in international trade using fsQCA and 24 trade-corridor/product-chain cases. The findings show that digital traceability success is not driven by blockchain adoption alone, but by the alignment of institutional readiness, market strictness, digital infrastructure, product risk, and cross-border coordination. The retained configurational pathway indicates that traceability systems perform effectively when technological capacity is embedded within supportive regulatory and organizational environments.

The study contributes to the literature in three ways. First, it advances a configurational explanation of digital traceability by showing that DTS emerges through conjunctural causation rather than technological determinism. Second, it clarifies the contingent role of blockchain as an enabling mechanism for integrity and verification rather than as an independent driver of success. Third, it demonstrates the usefulness of fsQCA for analyzing complex digital trade systems characterized by interdependent institutional, technical, and relational conditions.

The findings also carry practical implications. For policymakers, improving legal interoperability, customs digitalization, and cross-border data governance appears more important than promoting blockchain adoption in isolation. For firms and supply-chain actors, effective traceability depends on coordinated governance across exporters, logistics providers, certifiers, and destination-market institutions, particularly in high-risk and highly regulated trade environments.

Several limitations should be acknowledged. The study relies on an intermediate-N sample and calibrated documentary evidence, both of which involve analytical judgment. In addition, the findings are most applicable to high-regulation cross-border trade corridors and may not be directly transferable to domestic supply chains or low-risk sectors. Future research should therefore extend the case base, test alternative calibration schemes, and examine additional institutional and technological conditions across broader trade contexts.

Overall, the study suggests that digital traceability is best understood not as a stand-alone technological solution, but as a layered governance capability emerging from the interaction between institutions, infrastructure, market demands, and cross-border coordination.