基于数据要素配置的平台算法治理与电商交易效率研究

Research on Platform Algorithm Governance and E-Commerce Transaction Efficiency from the Perspective of Data Factor Allocation

摘要: 在数字经济加速演进的背景下，平台算法已逐步成为电子商务生态的基础性支撑，通过其对数据要素的系统性采集、处理与配置，深度参与到交易撮合、信息分发和价格形成等关键环节之中。这一机制在提升交易效率的同时，也带来了算法决策透明度不足、平台权力集中以及交易公平性弱化等治理问题。为深入分析算法治理强度如何通过影响数据要素配置，进而作用于电子商务交易效率，本文围绕数据要素市场化配置机制，构建了一个涵盖平台、商家和消费者的三方博弈模型。研究结果表明，算法治理强度与交易效率之间存在倒U型关系：适度的治理能够缓解信息不对称、优化数据配置结构，从而提升交易效率；而过度治理则会增加商家合规负担、抑制市场创新活力，导致效率下降。同时，平台的数据控制能力与消费者的信息敏感度在这一机制中具有显著的调节作用。本研究从理论上明确了算法治理的效率边界，为电商平台实施差异化、动态化的算法治理政策提供了学理依据。

Abstract: As the digital economy continues to deepen, platform algorithms have increasingly become a foundational pillar of the e-commerce ecosystem. Through the systematic collection, processing, and allocation of data factors, algorithms are deeply embedded in key transactional stages, including matching, information dissemination, and price formation. While this mechanism enhances transaction efficiency, it also gives rise to governance challenges such as insufficient transparency in algorithmic decision-making, the concentration of platform power, and the erosion of transactional fairness. To rigorously examine how the intensity of algorithmic governance affects e-commerce transaction efficiency through its influence on the allocation of data factors, this study adopts the perspective of market-oriented data factor allocation and constructs a tripartite game-theoretic model involving platforms, merchants, and consumers. The results indicate an inverted U-shaped relationship between algorithmic governance intensity and transaction efficiency. Moderate governance mitigates information asymmetry and improves the structure of data allocation, thereby enhancing transaction efficiency. In contrast, excessive governance increases merchants’ compliance burdens and suppresses market innovation, ultimately leading to efficiency losses. Moreover, platforms’ data control capacity and consumers’ information sensitivity play significant moderating roles in this mechanism. By theoretically delineating the efficiency boundary of algorithmic governance, this study provides a rigorous analytical foundation for the implementation of differentiated and dynamic algorithm governance policies in e-commerce platforms.