Research Proposal – Blockchain for Traceability in Australian Agricultural Supply Chains

Introduction

Australian agriculture competes internationally on the strength of its provenance. The clean and verifiable image of Australian produce underpins the premium that red meat and grains command in export markets, where combined agricultural and food exports are worth more than A$70 billion a year (ABARES 2024). That reputation is only as durable as the assurance systems behind it: food fraud and misrepresented credence claims such as grass-fed or organic threaten both consumer trust and the price premium, while export market access increasingly depends on demonstrating where a product came from (Austrade 2023). Traceability converts a provenance claim into verifiable evidence.

Australia’s traceability arrangements are strong in parts but fragmented overall, and to lift assurance across commodities agriculture ministers endorsed a National Traceability Framework directed towards interoperable, digital and end-to-end traceability (DAFF 2023). Blockchain has been proposed as an enabler, yet whether Australian agribusinesses will adopt it, and under what conditions, remains poorly understood. This proposal outlines a fifteen-month sequential explanatory mixed methods study, incorporating a design-science prototype, of what shapes blockchain-traceability adoption in the Australian red meat and grains sectors.

Background

Provenance, export assurance and food fraud

Red meat and grains are among the largest contributors to Australian agricultural export earnings, at around A$16 billion and A$13 billion respectively in recent years (ABARES 2024). Much of that value rests on credence attributes a buyer cannot verify by inspection, such as country of origin, production system and freedom from residues. Because these attributes are unobservable, they are vulnerable to substitution, and the Australian Competition and Consumer Commission enforces country-of-origin labelling and pursues misleading credence claims under consumer law (ACCC 2023). A single food-fraud or substitution incident can erode a hard-won market position, and overseas buyers and regulators increasingly require verifiable evidence of provenance as a precondition of market access, placing assurance capability at the centre of export competitiveness.

Existing traceability systems and their limits

In the livestock sector, the National Livestock Identification System links property identification codes, animal identifiers and movement records to support disease traceback (MLA 2023). It is comparatively mature, but was designed for biosecurity rather than for substantiating consumer-facing provenance claims, and its data sit within a single national database. In grains, traceability is thinner: commodity vendor declarations, receival dockets and warehouse records are often paper-based, reconciled manually, and rarely follow a consignment granularly from paddock to port. Across both sectors the recurring weaknesses are the same: data held in disconnected silos, limited trust between parties who each keep their own version of events, and records that can in principle be altered after the fact. The National Traceability Framework identifies these gaps and calls for interoperable digital systems that carry assurance data along the whole chain (DAFF 2023).

Blockchain as a candidate technology

A distributed ledger maintains a shared, append-only and tamper-evident record across multiple parties without a single controlling intermediary, and a permissioned version with known participants suits commercial confidentiality while still distributing trust (Saberi et al. 2019). Its promise of a single source of provenance truth and machine-verifiable credence claims is offset by cost, complexity, uncertain interoperability with the National Livestock Identification System, and a collective-action problem in which its value to a firm depends on how many trading partners also participate (Kamble, Gunasekaran and Sharma 2020).

Problem Statement

Industry and policy enthusiasm for blockchain traceability has produced numerous pilots but very little sustained adoption at scale, on the assumption that demonstrable benefits will in time drive uptake. If adoption is in fact governed more by environmental forces such as trading-partner requirements, export-market conditions and regulatory direction than by firms’ own assessment of the technology, then technology-centred promotion will continue to disappoint. At present no Australian study has measured the relative weight of technological, organisational and environmental factors across the red meat and grains sectors, nor demonstrated through a working prototype whether a permissioned ledger can satisfy Australian export-assurance requirements. This dual gap leaves the National Traceability Framework without an evidence base for targeting effort where it will change firm behaviour (DAFF 2023).

Aim and Research Questions

The aim of this study is to identify and rank the technological, organisational and environmental factors shaping agribusinesses’ intention to adopt blockchain-based traceability in the Australian red meat and grains sectors, and to demonstrate through a prototype whether a permissioned ledger can meet export-assurance and provenance requirements. Three research questions follow.

1. RQ1: Which technology, organisation and environment factors most strongly predict Australian agribusinesses’ intention to adopt blockchain-based traceability?
2. RQ2: How do these factors and their influence differ between the red meat and grains sectors and across positions in the supply chain, namely producers, processors and exporters?
3. RQ3: To what extent can a design-science prototype demonstrate that a permissioned blockchain meets Australian export-assurance and provenance requirements, and what implementation barriers does its evaluation reveal?

Literature Review

Blockchain traceability in agrifood supply chains

A growing literature examines blockchain for agrifood traceability. Saberi et al. (2019) catalogue the benefits, including provenance verification, recall efficiency and reduced information asymmetry, alongside inter-organisational, technical and external barriers. Kamble, Gunasekaran and Sharma (2020) find that adoption depends heavily on perceived usefulness, traceability requirements and trading-partner readiness. This work is nonetheless largely conceptual or pilot-based and drawn predominantly from overseas contexts, so firm-level evidence on who adopts, and why, remains scarce.

The technology-organisation-environment framework

The dominant lens for organisational-level technology adoption is the technology-organisation-environment framework, which locates the determinants of adoption in three contexts: the technology, the adopting organisation, and the external environment (Tornatzky and Fleischer 1990). It suits blockchain traceability because the technology’s value is inherently inter-organisational and cannot be explained by individual-user models alone. Its three contexts organise the review that follows and inform the conceptual model.

Technology context

Within the technology context, the diffusion literature identifies relative advantage, complexity and compatibility as the attributes that most consistently predict adoption of an innovation (Rogers 2003). For blockchain traceability the relative advantage lies in credible, tamper-evident provenance, but complexity is high and compatibility with the National Livestock Identification System is uncertain, while perceived cost weighs heavily on smaller operators (Kamble, Gunasekaran and Sharma 2020).

Organisation context

Organisational readiness and top management support are the most frequently confirmed organisational determinants of inter-organisational systems adoption (Iacovou, Benbasat and Dexter 1995). Readiness combines financial resources, information-technology capability and data maturity, all unevenly distributed across Australian agriculture, where many producers are family-owned small and medium enterprises with limited digital infrastructure. Top management support signals strategic priority and unlocks the resources to move beyond a pilot.

Environment context

The environmental context captures forces outside the firm, principally competitive pressure, trading-partner requirements and regulatory support. Trading-partner power is decisive for inter-organisational technologies, because a dominant buyer can effectively mandate participation (Iacovou, Benbasat and Dexter 1995), while government direction and standards lower uncertainty and legitimise investment (Tornatzky and Fleischer 1990). In Australian agriculture these forces may prove stronger than firms’ internal assessments, given the influence of large processors and exporters and the direction set by the National Traceability Framework (DAFF 2023). Export-market pressure is modelled as a distinct construct because overseas market access is a defining reality for red meat and grains.

Research gap

The literature therefore offers validated constructs but no Australian, firm-level test of their relative weight across the red meat and grains sectors, and no design-science demonstration of a prototype mapped to the National Traceability Framework. This study addresses both gaps.

Conceptual Framework

Figure 1 illustrates the conceptual model. Four constructs are drawn from the technology context, two from the organisation context and four from the environment context, all modelled as predictors of blockchain-traceability adoption intention. Sector, supply-chain position and firm size are modelled as moderators, on the expectation that environmental paths will be strongest for export-oriented red meat firms while organisational-readiness paths will be weaker among small grain producers.

Hypotheses H1a to H1d propose paths from the technology constructs, with relative advantage and compatibility positive and complexity and perceived cost negative; H2a and H2b propose positive paths from the organisational constructs; and H3a to H3d propose positive paths from the environmental constructs, of which trading-partner and export-market pressure are expected to be the strongest predictors overall.

Research Design and Methodology

Overall design

The study adopts a sequential explanatory mixed methods design, in which a quantitative survey establishes the relative weight of the adoption factors and a subsequent qualitative phase explains the mechanisms behind the strongest and most counterintuitive results (Creswell and Plano Clark 2018). A third, design-science strand builds and evaluates a working prototype so the feasibility questions in RQ3 are answered by construction rather than by opinion (Hevner et al. 2004).

Phase 1: survey of agribusiness decision-makers

A cross-sectional online questionnaire will be administered to owners and senior managers of agribusinesses in the red meat and grains value chains, spanning producers, processors and exporters or logistics providers. The sampling frame will be drawn from industry body membership, saleyard and grain-receival networks and export directories, with quotas to ensure adequate representation of each sector and supply-chain position. The minimum sample size was estimated using the inverse square root method for partial least squares structural equation modelling, which sets the minimum as a function of the smallest path coefficient the study aims to detect (Kock and Hadaya 2018):

n_min = (2.486 / p_min)² = (2.486 / 0.17)² = 14.62² = 213.8, rounded to 214.

Inflating this figure by 15 per cent to allow for incomplete responses gives 214 × 1.15 = 246.1, so a target of 250 completed questionnaires has been adopted, which also supports the multi-group comparison between the red meat and grains sectors required by RQ2. Table 1 sets out the constructs, their operational definitions and the sources of the measurement items; all items use five-point Likert response formats, and complexity and perceived-cost items are reverse coded so that high scores consistently indicate a more favourable position towards adoption.

Table 1: Constructs, measurement items and sources for the Phase 1 questionnaire

Phase 2: interviews

Twelve semi-structured interviews will be conducted with participants purposively sampled from consenting survey respondents, selected for maximum variation across sector, supply-chain position and adoption intention. They will probe the reasoning behind the survey patterns, particularly any counterintuitive results, the point at which traceability breaks down along a consignment, and reactions to policy options such as mandated buyer requirements or co-funded infrastructure. Twelve interviews are expected to reach thematic saturation for a focused study of this kind.

Phase 3: design-science prototype

A permissioned distributed-ledger proof-of-concept will be built to capture the key traceability events for a red meat consignment, from property movement recorded against a property identification code and the National Livestock Identification System, through processing, to export certification. Event data will be mapped to the National Traceability Framework so the prototype demonstrates interoperability rather than a stand-alone silo (DAFF 2023). Following the build-and-evaluate logic of design science (Hevner et al. 2004), it will be demonstrated to a panel of eight practitioners and evaluated against defined criteria: data integrity and tamper-evidence, interoperability with existing identification systems, provenance-query performance, completeness against export-assurance requirements, and perceived usefulness. It will operate on synthetic and consented test data only.

Analysis plan

Survey data will be analysed using partial least squares structural equation modelling, which suits prediction-oriented models and moderate sample sizes (Hair et al. 2019). The measurement model will be assessed through composite reliability, average variance extracted and the heterotrait-monotrait ratio before the structural model is interpreted with 5,000 bootstrap subsamples. Multi-group analysis will test whether path coefficients differ significantly between the red meat and grains sectors, addressing RQ2. Interview transcripts will be analysed thematically using a coding frame derived from the technology-organisation-environment model plus open codes, and the design-science evaluation summarised against its criteria. Integration will occur through a joint display setting each quantitative path result beside the qualitative and prototype evidence that confirm, explain or qualify it.

Ethical Considerations

The study will be submitted to the university Human Research Ethics Committee and conducted in accordance with the National Statement on Ethical Conduct in Human Research (NHMRC 2023). Participation will be voluntary and based on written informed consent, with a plain-language statement explaining that responses will be reported only in aggregate. Because respondents may disclose commercially sensitive information about supplier relationships and costs, data will be de-identified at collection, firm names replaced with codes, and no individual business identifiable in any output. Data will be stored on encrypted university servers for five years and then destroyed, and the prototype will use synthetic rather than live commercial records. The study presents minimal risk, and participants may decline any question or withdraw at any time without consequence.

Project Timeline

Table 2 presents the fifteen-month schedule. Phases overlap deliberately so the prototype build begins while survey analysis is under way, allowing early findings to inform the evaluation criteria.

Table 2: Fifteen-month project timeline by phase, activity and week

Significance

The findings will inform several live Australian questions. First, they will give the National Traceability Framework an evidence base for targeting effort at the factors that move firm behaviour rather than at technology promotion alone (DAFF 2023). Second, by ranking the technological, organisational and environmental factors rather than cataloguing them, the study will help bodies such as Meat and Livestock Australia direct investment towards the binding constraint in each sector (MLA 2023). Third, a prototype shown to meet export-assurance requirements supports the market-access agenda that underpins export value (Austrade 2023; ABARES 2024), while stronger provenance verification protects the credence claims the Australian Competition and Consumer Commission is charged with policing (ACCC 2023). The resulting reference architecture is reusable beyond red meat and grains.

Limitations

Three limitations are acknowledged. The survey measures adoption intention rather than actual adoption, and the intention-behaviour gap is well documented, so results will indicate the salience of adoption factors rather than forecast uptake. Recruitment through industry channels is likely to over-represent digitally engaged firms and understate organisational-readiness barriers; offering paper responses and recruiting through saleyards and receival sites will partly offset this. Finally, the prototype is a proof-of-concept rather than a production system, and the technology is evolving quickly, so its evaluation demonstrates feasibility against current requirements rather than a fixed specification.

Conclusion

Australia’s agricultural export value rests on provenance that must be not only genuine but verifiable, and blockchain traceability offers a credible way to strengthen that verification across fragmented supply chains. Enthusiasm has nonetheless outrun adoption, because the factors that determine whether agribusinesses take up the technology have not been measured, and feasibility against Australian export-assurance requirements has not been demonstrated. This proposal combines a survey of 250 agribusiness decision-makers, twelve explanatory interviews and a design-science prototype mapped to the National Traceability Framework to rank the barriers and enablers and show what a permissioned ledger can and cannot do. The result will be an evidence base for directing policy and industry investment to the constraints that matter most in the red meat and grains sectors.

References

Austrade 2023, Australian agribusiness and food export performance, Australian Trade and Investment Commission, Canberra.

Australian Bureau of Agricultural and Resource Economics and Sciences 2024, Agricultural commodities and trade data, ABARES, Canberra.

Australian Competition and Consumer Commission 2023, Country of origin food labelling guidance, ACCC, Canberra.

Creswell, JW & Plano Clark, VL 2018, Designing and conducting mixed methods research, 3rd edn, Sage, Thousand Oaks.

Department of Agriculture, Fisheries and Forestry 2023, National traceability framework and implementation plan, Commonwealth of Australia, Canberra.

Hair, JF, Risher, JJ, Sarstedt, M & Ringle, CM 2019, ‘When to use and how to report the results of PLS-SEM’, European Business Review, vol. 31, no. 1, pp. 2-24.

Hevner, AR, March, ST, Park, J & Ram, S 2004, ‘Design science in information systems research’, MIS Quarterly, vol. 28, no. 1, pp. 75-105.

Iacovou, CL, Benbasat, I & Dexter, AS 1995, ‘Electronic data interchange and small organizations: adoption and impact of technology’, MIS Quarterly, vol. 19, no. 4, pp. 465-485.

Kamble, SS, Gunasekaran, A & Sharma, R 2020, ‘Modeling the blockchain enabled traceability in agriculture supply chain’, International Journal of Information Management, vol. 52, pp. 1-16.

Kock, N & Hadaya, P 2018, ‘Minimum sample size estimation in PLS-SEM: the inverse square root and gamma-exponential methods’, Information Systems Journal, vol. 28, no. 1, pp. 227-261.

Meat & Livestock Australia 2023, Integrity systems and the National Livestock Identification System, Meat & Livestock Australia, Sydney.

National Health and Medical Research Council 2023, National statement on ethical conduct in human research, NHMRC, Canberra.

Rogers, EM 2003, Diffusion of innovations, 5th edn, Free Press, New York.

Saberi, S, Kouhizadeh, M, Sarkis, J & Shen, L 2019, ‘Blockchain technology and its relationships to sustainable supply chain management’, International Journal of Production Research, vol. 57, no. 7, pp. 2117-2135.

Tornatzky, LG & Fleischer, M 1990, The processes of technological innovation, Lexington Books, Lexington.