Study Maps Interdependent Socio-Technical Facilitators of Agricultural Blockchain Adoption

A structural modeling study of agri-food value chains has identified ten key facilitators of blockchain adoption, revealing that technological antecedents and organizational mechanisms operate as a tightly coupled socio-technical system rather than a hierarchy of independent root causes. In a mini review published in Frontiers in Blockchain, researchers Ganeshkumar D. Rede and Pallawi B. Sangode applied Interpretive Structural Modelling and complementary MICMAC analysis to evaluate how adoption enablers interact across agricultural ecosystems. Their investigation demonstrates that nine out of ten evaluated facilitators collapse into a single, mutually reinforcing cluster exhibiting high driving and high dependence power, while enhancing trust among stakeholders sits at the apex of the hierarchy as the ultimate systemic outcome. The authors emphasize that Interpretive Structural Modelling organizes expert perceptions of directional influence into hierarchical levels rather than measuring statistical effect sizes or estimating causal magnitudes. Applying transitivity consistently across expert judgments reveals that successful blockchain deployment requires coordinated support across technical, institutional, and user-facing dimensions.

The investigation frames blockchain implementation through socio-technical systems theory and the Technology-Organization-Environment framework. Socio-technical systems theory holds that technological performance and organizational arrangements co-determine adoption outcomes, requiring technical antecedents, governance structures, operational capabilities, and systemic outcomes to be analyzed within a unified framework. Drawing on these principles, the authors classified ten facilitators of blockchain adoption into operational definitions and conceptual roles. Stakeholder collaboration (A1) and shared responsibilities of partners (A3) represent governance mechanisms evaluating coordinated decision-making and distributed accountability across supply chains. Technological antecedents include enhancing information and communication technology infrastructure (A2), integrating blockchain with complementary tools like sensors and farm-management software (A4), and ease of use (A9), which reflects user interface simplicity. Operational capabilities encompass quality of service (A6), immutable audit trails (A8), and fast-payment processing (A10). Finally, value-chain improvement (A5) and trust (A7) were categorized as adoption outcomes, establishing the conceptual architecture evaluated in the modeling process.

To establish clear contextual boundaries, the authors clarified the unit of analysis, distinguishing broad agri-food supply chains from narrower farm-level precision agriculture. While smart farming relies on technologies like drones, sensors, artificial intelligence, and mobile applications to manage intra-field variability, the core facilitators examined—such as auditability, payment settlement, and multi-partner accountability—describe broader value-chain relationships. The precision agriculture sector has seen substantial expansion, driven by a projected 13.7 percent compound annual growth rate toward 10.55 billion dollars by 2025. However, the researchers addressed an internal inconsistency from an earlier manuscript draft that focused narrowly on precision farming in its title while analyzing supply-chain factors throughout its text. Resolving this discrepancy, the published study defines its unit of analysis as the coordinated network of actors spanning agricultural production, aggregation, processing, and distribution. Within this boundary, precision-farming tools serve as upstream data sources feeding field measurements into distributed ledgers to support provenance tracking, combat fraud, and execute smart contracts across commercial networks.

To evaluate content validity and gather contextual relationship data, the authors convened an expert panel comprising five decision-makers representing distinct agricultural and technological domains. The academic sector was represented by a department head with thirty-five years of research experience in agricultural technology and supply chains, while non-governmental organizations were represented by a chief executive officer possessing twenty years of experience in rural development initiatives. Commercial agro-industry perspectives were contributed by an entrepreneur with twenty-five years of operational experience, and agricultural field practices were reflected by a progressive farmer with forty-five years of experience operating precision-farming equipment. Finally, an information technology specialist with ten years of distributed-ledger development experience provided technical domain expertise. This five-member panel reviewed the ten literature-identified facilitators and provided the pairwise contextual judgments that formed the basis of the interpretive structural modeling calculations. The authors noted that in qualitative structural elicitation, panel composition directly shapes the reachability matrix and the resulting relational hierarchy.

The structural analysis relied on Interpretive Structural Modelling, a methodology introduced by Warfield to organize expert judgments of pairwise influence into a multi-tiered system. The procedure began by constructing a Structural Self-Interaction Matrix that encoded directional influence among the ten facilitators using four standard symbols: V, A, X, and O. In this convention, V indicates that facilitator i influences facilitator j; A means facilitator j influences facilitator i; X denotes mutual, bidirectional influence; and O signifies no significant relationship. Notably, the expert panel assigned a high density of bidirectional X ratings across core operational pairs. Mutual influence was recorded between stakeholder collaboration and infrastructure, stakeholder collaboration and fast payments, digital infrastructure and partner responsibilities, and system integration and audit trails. Furthermore, service quality showed mutual influence with both audit trails and ease of use. This density of bidirectional judgments revealed that the experts perceived technological tools, institutional coordination, and operational capabilities as mutually reinforcing dynamics rather than linear, unidirectional steps.

Converting the Structural Self-Interaction Matrix into an initial reachability matrix replaced directional symbols with binary digits according to standard rules. In this initial matrix capturing only direct relationships, driving power totaled six for collaboration, nine for ICT infrastructure, four for partner responsibilities, four for integration, three for value-chain improvement, three for service quality, one for trust, four for audit trails, four for ease of use, and seven for fast payments. To incorporate indirect dependencies, the authors applied transitive closure, which specifies that if facilitator i reaches factor k and factor k reaches factor j, then factor i necessarily reaches factor j. Applying transitivity altered the structural network substantially: because nine facilitators shared multiple direct and reciprocal connections, transitive closure propagated reachability throughout the entire group. In the resulting final reachability matrix, facilitators A1 through A6 and A8 through A10 each attained a driving power of ten and a dependence power of nine. Conversely, trust reached only itself while being reached by all nine other facilitators, exhibiting a driving power of one and a dependence power of ten.

Level partitioning was subsequently executed on the final reachability matrix to establish the vertical levels of the hierarchy. For each facilitator, analysts determined a reachability set of factors it reaches, an antecedent set of factors that reach it, and an intersection set representing the overlap between both groups. A facilitator is placed at the current level when its reachability set equals its intersection set. In the first iteration, stakeholder trust emerged as the sole facilitator whose reachability set containing only itself matched its intersection set, assigning it to Level 1 at the apex. Following the removal of trust, the remaining nine facilitators formed an identical, strongly connected block during the second iteration. Each factor’s reachability, antecedent, and intersection sets were identical, containing all nine elements. Consequently, all nine facilitators were assigned simultaneously to Level 2 at the base of the hierarchy. This two-iteration result established a two-level digraph, correcting an earlier, mathematically irreproducible five-level model from previous manuscript drafts.

To complement the structural hierarchy, the authors conducted a MICMAC analysis, which plots driving power against dependence power across four functional quadrants. In standard MICMAC taxonomy established by Duperrin and Godet, autonomous factors exhibit low driving power and low dependence; dependent factors possess low driving power but high dependence, indicating outcome variables; linkage factors display both high driving and high dependence power, designating unstable variables that propagate feedback throughout the network; and independent factors combine high driving power with low dependence, characterizing candidate root causes. The empirical classification placed nine of the ten facilitators—spanning infrastructure, collaboration, shared responsibility, interoperability, value-chain improvement, service quality, audit trails, ease of use, and fast payments—in the linkage cluster. Trust was classified as the sole resident of the dependent cluster. Because no facilitators fell into the independent or autonomous quadrants, the system contains no isolated root-cause levers, demonstrating that agricultural blockchain adoption behaves as a tightly coupled socio-technical feedback system.

These structural findings provide actionable guidance for agricultural extension professionals, policymakers, technology developers, and financial institutions. Because nine facilitators reside in the linkage quadrant, isolated investments in a single factor—such as expanding digital telecommunications without addressing interface usability or governance—are unlikely to achieve sustainable adoption. Instead, extension professionals and developers must implement synchronized interventions that pair hardware infrastructure and software ease of use with formalized agreements clarifying partner responsibilities. For financial institutions and farmer cooperatives, the position of fast payments inside the linkage cluster shows that digital payment mechanisms cannot function effectively without reliable audit trails and inter-organizational cooperation. Importantly, the authors explicitly moderated claims regarding downstream agricultural impacts, emphasizing that the study evaluated perceived structural relationships among adoption facilitators rather than measuring changes in crop productivity, profitability, ecological sustainability, or operational resilience, none of which were measured in the interpretive structural modeling analysis.

The researchers outlined several methodological limitations, noting that interpretive structural models represent qualitative exploratory frameworks rather than empirically tested causal systems. The findings reflect the consensus of a five-member expert panel during a single elicitation round, leaving the model sensitive to panel composition and contextual framing. A primary structural limitation arose from the high proportion of bidirectional and reciprocal codes in the initial matrix, which mathematically caused nine facilitators to collapse into a single cluster upon transitive closure. The authors noted that this collapse reflects the underlying elicitation protocol rather than proving that no hierarchical distinctions exist in commercial practice. To refine the structural hierarchy, future studies should employ more conservative elicitation protocols requiring explicit justifications for reciprocal links, or utilize fuzzy ISM and DEMATEL methods with established influence thresholds. Expanding future research across larger expert cohorts, quantitative surveys, structural equation modeling, and cross-regional field studies will help test whether differentiated root-cause hierarchies emerge under diverse socioeconomic and policy environments.

Subject of Research: Socio-technical facilitators and structural modeling of blockchain adoption in agri-food value chains

Article Title: Exploring socio-technical facilitators of blockchain adoption in agri-food value chains: an interpretive structural modelling (ISM) approach

Article References: Rede, G. D., & Sangode, P. B. (2026). Exploring socio-technical facilitators of blockchain adoption in agri-food value chains: an interpretive structural modelling (ISM) approach. Frontiers in Blockchain, 9, Article 1895432. https://doi.org/10.3389/fbloc.2026.1895432

Image Credits: AI Generated

DOI: 10.3389/fbloc.2026.1895432

Keywords: blockchain adoption, agri-food value chains, interpretive structural modelling, MICMAC analysis, socio-technical systems, agricultural technology, stakeholder trust, precision agriculture, Exploring, socio-technical, facilitators, blockchain