Adaptive Fulfillment Systems Under Incomplete Control: A Risk-Oriented Modeling Framework

Abstract

Platform-based fulfillment systems increasingly rely on third-party sellers whose actions are only partially governed by platform rules and incentives. This study proposes a risk-oriented modeling framework to evaluate the boundaries of seller control under such incomplete governance. We develop a dual-layer causal structure: the first layer examines how delivery delays are shaped by platform-controlled (e.g., shipping tier) and seller-sensitive variables (e.g., order priority, customer tier, and region); the second layer analyzes whether sellers actively select higher-tier shipping options in response to strategic order attributes. Empirical analysis using 180,000 real transaction records reveals two key findings. First, upgrading the shipping tier is the only intervention that significantly reduces delivery delays, underscoring the primacy of platform-side control in risk mitigation. Second, sellers show no systematic behavioral adjustment to order urgency, customer segmentation, or geographic differences, revealing a breakdown in incentive alignment and adaptive behavior. These results highlight the limitations of decentralized fulfillment systems when not complemented by robust coordination mechanisms. The proposed framework offers new insights for designing adaptive and incentive-compatible logistics governance in platform ecosystems.