Game Theory Reveals How Fairness Concerns Disrupt Blockchain Agricultural Supply Chains

A mathematical investigation into agricultural supply chains demonstrates that sharing the substantial investment costs of blockchain traceability between manufacturers and retailers can stimulate technology adoption, yet behavioral fairness concerns significantly alter pricing decisions, investment levels, and channel profitability. Published in Frontiers in Sustainable Food Systems, the research analyzes a two-tier agricultural supply chain through a two-stage Stackelberg game-theoretic framework. In the model, an agricultural manufacturer acts as the Stackelberg channel leader by determining wholesale pricing and the technical level of blockchain traceability investment, while a downstream retailer acts as the follower by establishing retail prices and marketing efforts. By comparing scenarios with and without contractual cost sharing alongside fairness-neutral and fairness-concerned behavioral configurations, the study exposes how profit-allocation sensitivities create complex strategic trade-offs. The findings indicate that while retailer cost sharing generally encourages greater traceability deployment, disproportionate cost burdens or heightened sensitivity to inequity can distort operational decisions, compress participant margins, and degrade overall system efficiency.

Deploying distributed ledger technology across agri-food networks has gained momentum globally as supply chain partners seek to ensure authenticity, quality, and cold-chain integrity for fresh and organic produce. Traditional tracking mechanisms, such as conventional radio-frequency identification and standard barcoding systems, frequently encounter vulnerabilities related to centralized data management, interoperability limitations, and potential record manipulation. Blockchain frameworks overcome these vulnerabilities by generating tamper-resistant, verifiable provenance records spanning farm origin, chemical applications, logistics, and retail distribution, thereby mitigating information asymmetry and strengthening consumer trust. Nevertheless, implementing comprehensive blockchain infrastructure requires heavy upfront capital commitments in Internet of Things sensors, systems integration, smart-contract architectures, and external verification audits. Because the agricultural manufacturer typically executes the core technological deployment while downstream platforms reap the benefits of expanded consumer demand, disputes over cost responsibility and revenue distribution frequently emerge. These operational and financial frictions motivated the formal mathematical inquiry into how cost-sharing contracts interact with psychological sensitivities regarding profit fairness.

To rigorously examine these dynamics, the study specifies a linear, additively separable consumer demand function influenced negatively by retail price and positively by both verifiable blockchain traceability and retailer marketing intensity. Within this formulation, the market base is defined alongside a customer price sensitivity coefficient, an impact coefficient reflecting consumer sensitivity to blockchain traceability, and an advertising effectiveness coefficient. Traceability does not merely represent generic quality enhancement but quantifies verifiable information whose credibility elevates consumer willingness to pay. Traceability infrastructure costs and retailer promotional outlays are modeled using strictly convex, quadratic cost functions governed by explicit cost coefficients, capturing increasing marginal expenses and diminishing returns. Unit production costs remain constant to isolate technology decisions, while parameter viability constraints ensure market feasibility and strict objective concavity. The sequence of decisions follows backward induction under complete information, reflecting realistic commercial operations where fundamental infrastructure investments and wholesale contracts are established before final consumer pricing and promotional campaigns are enacted.

The analytical architecture centers on four distinct contractual and behavioral scenarios formulated to permit matched comparative evaluations. The baseline configuration, designated NN, represents a benchmark where neither cost sharing nor fairness preferences exist, leaving the manufacturer to bear full traceability expenses while both parties independently maximize monetary profits. In the NM configuration, the manufacturer bears all technological costs but exhibits fairness concerns regarding channel profit distribution, evaluating outcomes using a fairness-adjusted utility function. The SN scenario introduces a contractual agreement where the retailer covers a fractional share of traceability investment costs under mutual behavioral neutrality. Finally, the SR scenario pairs retailer cost sharing with a fairness-concerned retailer. In both behavioral settings, fairness reference benchmarks are derived via generalized Nash bargaining with normalized zero disagreement payoffs. Under NM, bargaining exponents reflect manufacturer fairness sensitivity alongside a neutral retailer, whereas under SR, reference shares incorporate retailer bargaining power, retailer fairness sensitivity, and the manufacturer’s complementary bargaining weight.

Analyzing the transition from the baseline NN setting to the fairness-neutral cost-sharing scenario SN demonstrates that retailer financial participation generates significant operational shifts. When the retailer assumes a fractional share of traceability investment expenses, the manufacturer’s effective marginal cost drops from its full initial value to a discounted level. This structural cost relief creates strong economic incentives for the manufacturer to elevate the equilibrium traceability standard. Higher traceability subsequently expands market demand, granting the manufacturer latitude to charge a higher wholesale price without causing demand destruction, which in turn motivates the retailer to intensify its marketing effort and raise the final consumer price. While this dual mechanism consistently increases the manufacturer’s monetary profit, the effect on retailer profitability is non-monotonic. At moderate sharing ratios, incremental consumer demand compensates the retailer for its financial support. However, when the cost-sharing fraction exceeds a mathematically derived threshold, the retailer’s direct cost burden outpaces its revenue gains, causing total channel profit to trace an inverted U-shaped trajectory.

When the manufacturer operates with fairness concerns in the absence of cost sharing, the NM scenario reveals an inherently self-defeating behavioral mechanism. Because a fairness-concerned manufacturer assesses its performance relative to the generalized Nash bargaining profit split rather than absolute monetary return, it strategically raises the wholesale price to claim a larger per-unit margin and defend its relative distribution. Concurrently, the manufacturer treats traceability investment as an inefficient mechanism for equity protection, because technological enhancements expand retail volume and leak economic surplus to the downstream partner. Consequently, the manufacturer curtails traceability investment relative to fairness-neutral levels. The elevated wholesale price compels the retailer to increase retail prices, which suppresses consumer demand and compounds the contraction caused by reduced traceability. As a result, both parties suffer lower equilibrium sales volumes and diminished monetary profits. The manufacturer’s fairness-adjusted utility may briefly rise at low sensitivity values as perceived distributional equity improves, but it sharply declines once profit destruction dominates beyond a critical behavioral threshold.

Introducing retailer fairness concerns under a cost-sharing contract produces a contrasting set of behavioral and operational adjustments within the SR scenario. As the retailer’s fairness sensitivity increases, the retailer actively lowers both its retail price and promotional expenditure to realign its operational payoff relative to its Nash bargaining reference point. Foreseeing depressed promotional support from the retailer, the manufacturer optimally reduces both its wholesale price and its investment in blockchain traceability. Although reduced retail prices offer partial support to sales volume, the simultaneous retreat in marketing intensity and traceability credibility exerts a dominant negative influence, causing aggregate equilibrium demand to contract. The manufacturer experiences unambiguous losses in both sales volume and wholesale margin, leading to strictly diminished manufacturer profits. Conversely, the retailer can experience an improvement in its own monetary returns under defined analytical conditions, because substantial reductions in marketing outlays and cost-sharing obligations can surpass the losses resulting from softer market demand, effectively redistributing channel gains at the expense of system-wide efficiency.

Numerical simulations executed in MATLAB R2023b clarify the sensitivity of these closed-form equilibrium solutions across extensive parameter spaces. Utilizing baseline inputs—including a market demand base of 100, unit production cost of 20, price sensitivity of 2, traceability sensitivity of 5, marketing effectiveness of 3, blockchain cost parameter of 30, marketing cost parameter of 15, retailer bargaining power of 0.3, and varying fairness coefficients—the simulations illuminate how the cost-sharing ratio dictates behavioral outcomes. When the cost-sharing proportion remains below one-third, rising retailer fairness concern steadily diminishes total supply chain profit because efficiency losses dominate immediately. In contrast, when the sharing ratio exceeds one-third, such as at a value of 0.6, total channel profit initially increases with moderate retailer fairness concern up to a calculated critical threshold of approximately 2.22 before declining. In this higher cost-sharing regime, moderate retailer fairness concern effectively counterbalances the manufacturer’s overinvestment tendencies induced by aggressive cost shifting, temporarily restoring channel balance until excessive fairness sensitivity precipitates severe demand destruction.

To isolate the distinct economic mechanism introduced specifically by distributed ledger architectures, the researchers evaluated a controlled conventional traceability benchmark within the fairness-neutral baseline framework. Unlike blockchain systems where tamper resistance and decentralized validation allow consumers to treat traceability data as fully credible, conventional centralized recordkeeping provides weaker cross-party verification and remains vulnerable to post hoc alterations. The model formalizes this disparity by introducing a credibility retention parameter between zero and one, discounting the demand-stimulating effectiveness of conventional tracking while holding the underlying quadratic investment cost function identical. The analytical comparison confirms that full information credibility yields strictly higher equilibrium traceability investment, greater marketing expenditure, higher wholesale and retail prices, larger sales volumes, and superior profits for both channel participants. This benchmark proves that the economic superiority of blockchain in the modeled setting originates directly from the elimination of credibility discounting, which unlocks greater marginal returns on technology investments when baseline implementation expenses are equivalent.

Comprehensive one-at-a-time robustness evaluations varying five foundational structural parameters by plus and minus twenty percent across 105 distinct parameter evaluations confirmed that the principal cross-scenario rankings remain mathematically stable. Across all tested parameter intervals, manufacturer fairness concerns consistently depressed traceability levels and monetary earnings, retailer cost sharing elevated investment decisions while exerting non-monotonic profit effects, and retailer fairness concerns reallocated returns away from the channel leader. From a managerial standpoint, these findings suggest that agricultural enterprises adopting blockchain must avoid static, open-ended cost-sharing agreements that burden retail partners excessively. Instead, practitioners should establish phase-gated cost contributions tied directly to verifiable data integrity milestones and institute transparent profit-allocation mechanisms to prevent defensive pricing responses. The authors acknowledge that the model reflects a selective four-scenario structure within a manufacturer-led Stackelberg hierarchy, leaving alternative leadership configurations, retailer-dominated e-commerce platforms, and simultaneous bilateral fairness concerns as crucial subjects for subsequent empirical and theoretical investigation.

Subject of Research: Economic modeling of blockchain traceability investment, cost sharing, and fairness concerns in agricultural supply chains

Article Title: Blockchain traceability investment in agricultural supply chains: cost sharing, fairness concerns, and channel performance

Article References: Lu, Y. (2026). Blockchain traceability investment in agricultural supply chains: cost sharing, fairness concerns, and channel performance. Frontiers in Sustainable Food Systems, 10, Article 1946225. https://doi.org/10.3389/fsufs.2026.1946225

Image Credits: AI Generated

DOI: 10.3389/fsufs.2026.1946225

Keywords: agricultural supply chains, blockchain traceability, Stackelberg game, cost sharing, fairness concerns, operations research, channel performance, Nash bargaining, Blockchain, traceability, investment, agricultural