Strategic Reconfiguration of Decision Authority in AI-Driven Global Supply Chains under Geopolitical Fragmentation: Developing Geo-Algorithmic Decision Authority Theory (GADAT)

Engineering notes

Key topics: autonomous driving, planning. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Artificial intelligence (AI) is now deeply embedded in global supply-chain decision-making, automating tasks ranging from demand forecasting to supplier qualification and logistics planning. Alongside this technological shift, geopolitical fragmentation, expressed through sanctions, data localization mandates, new regional trade blocs, and friend-shoring has reshaped the institutional environment in which firms operate. Existing research examines AI delegation as an efficiency-enhancing mechanism and geopolitical fragmentation as a structural risk, but the two forces have rarely been theorized together. This conceptual paper develops an integrative theoretical framework, that is, Algorithmic Decision Authority Theory (GADAT), to explain how geopolitical fragmentation fundamentally shifts AI delegation from an efficiency-centric architecture toward a legitimacy-centric architecture. Drawing on Resource Dependence Theory, Institutional Theory, Information Processing Theory, and Dynamic Capabilities, the paper introduces several new constructs: Political–Algorithmic Misalignment (PAM), Geo-Institutional Opacity (GIO), Legitimacy-Centric Delegation (LCD), Algorithmic Legitimacy Buffering (ALB), and Delegation Adjustment Capability (DAC). The theory identifies three core mechanisms—coercive legitimacy pressures, political-triggered information constraints, and institutional heterogeneity—through which geopolitical fragmentation reshapes the locus, granularity, and accountability of decision authority in AI-enabled supply chains. The paper then articulates a model predicting four structural outcomes: recentralized authority, federated hybrid authority, trust-partitioned authority, and insulated algorithmic authority. Eight propositions link these configurations to supply-chain resilience, performance, and multinational adaptability. A dedicated section examines alternative theoretical explanations, transaction cost economics, agency theory, classic information processing models, and traditional supply-chain governance perspectives—to demonstrate the added value of GADAT. The paper concludes with a research agenda for empirical testing and proposes managerial implications for configuring AI decision rights in politically heterogeneous environments.

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