Paper: arXiv 2510.07801

Authors: Jinho Cha, Youngchul Kim, Junyeol Ryu, Sangjun Park, Jeongho Kang, Hyeyoung Hwang

Abstract

This study develops a strategic procurement framework integrating blockchain-based smart contracts with bounded demand variability modeled through a truncated normal distribution. While existing research emphasizes the technical feasibility of smart contracts, the operational and economic implications of adoption under moderate uncertainty remain underexplored. We propose a multi-supplier model in which a centralized retailer jointly determines the optimal smart contract adoption intensity and supplier allocation decisions. The formulation endogenizes adoption costs, supplier digital readiness, and inventory penalties to capture realistic trade-offs among efficiency, sustainability, and profitability. Analytical results establish concavity and provide closed-form comparative statics for adoption thresholds and procurement quantities. Extensive numerical experiments demonstrate that moderate demand variability supports partial adoption strategies, whereas excessive investment in digital infrastructure can reduce overall profitability. Dynamic simulations further reveal how adaptive learning and declining implementation costs progressively enhance adoption intensity and supply chain performance. The findings provide theoretical and managerial insights for balancing digital transformation, resilience, and sustainability objectives in smart contract-enabled procurement.

Complexity vs Empirical Score

  • Math Complexity: 6.5/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a solid analytical formulation using a truncated normal distribution for demand to derive concavity and closed-form comparative statics for smart contract adoption. However, it relies primarily on numerical experiments and dynamic simulations rather than empirical validation with real-world market datasets. The clarity and literature review are well-structured, but reproducibility is limited by the absence of publicly shared code.

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Optimize smart contract adoption & procurement under bounded demand variability"] --> B["Inputs & Data:<br>Truncated Normal Demand, Readiness, Adoption Costs, Inventory Penalties"]
    B --> C["Multi-Supplier Mathematical Model:<br>Endogenizes adoption intensity & supplier allocation"]
    C --> D["Computational & Analytical Processes:<br>Closed-form comparative statics & dynamic simulations"]
    D --> E["Key Findings:<br>Moderate variability supports partial adoption; over-investment reduces profit; adaptive learning boosts ROI"]

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0 of 68 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.