拼购模式下农产品供应链数字化赋能的机理解构与优化路径——以拼多多电商助农实践为例

Mechanism Deconstruction and Optimization Path of Digital Empowerment for Agricultural Supply Chains under Group-Buying Model—A Case Study of Pinduoduo’s E-Commerce Agricultural Assistance

摘要: 数字经济正在从底层逻辑上重塑农业生产与流通的既有范式，以拼多多为代表的社交电商模式如何突破传统农产品供应链产销脱节的结构性矛盾，已成为驱动产业可持续发展的关键命题。本研究立足于利益相关者与三重底线理论视域，构建了包含政府引导、平台运营与社会反馈三维度的结构方程模型。研究采用线上滚雪球抽样法，面向全国范围内深度参与“多多农园”及“百亿补贴”项目的农户与商家获取204份有效数据，利用SPSS与AMOS软件进行信效度检验与路径分析。实证结果显示，政府支持、平台运营与社会绩效之间已形成显著的正向协同效应，政府政策引导与社会公众诉求是启动供应链转型的核心驱动力，且拼多多“拼购 + 产地直发”模式带来的运营绩效提升能够显著促进社会与环境绩效的改善(β = 0.787)，验证了数字化技术在实现“经济效益与社会责任双赢”方面的有效性。因子分析进一步揭示，当前制约供应链韧性的核心瓶颈在于冷链履约成本高，企业、复合型数智人才匮乏以及绿色信任机制的缺位。未来的助农模式需从单纯的技术叠加转向系统性的生态重构，通过打造“云–网–端”一体化数智基建、建立产教融合的利益联结机制以及完善绿色数据治理体系，实现经济价值与社会生态价值的动态平衡。

Abstract: The digital economy is profoundly reshaping the underlying logic of agricultural production and circulation. A critical proposition for driving sustainable industrial development is determining how the social e-commerce model, represented by Pinduoduo, breaks through the structural contradiction of the disconnect between production and marketing in traditional agricultural supply chains. Methodology: Grounded in Stakeholder Theory and the Triple Bottom Line framework, this study constructs a three-dimensional structural equation model (SEM) encompassing government guidance, platform operation, and social feedback. Utilizing an online snowball sampling method, the study obtained 204 valid responses from farmers and merchants deeply involved in Pinduoduo’s “Duo Duo Farm” and “10 Billion Subsidy” projects nationwide. Reliability and validity testing, along with path analysis, were conducted using SPSS and AMOS software. Results: Empirical results demonstrate a significant positive synergistic effect among government support, platform operation, and social performance. Government policy guidance and social public demand serve as the core driving forces initiating supply chain transformation. Notably, the operational performance enhancement driven by Pinduoduo’s “Group-Buying + Origin-Direct” model significantly promotes social and environmental performance (β = 0.787), validating the efficacy of digital technology in achieving a “win-win” for economic benefits and social responsibility. Conclusion: Factor analysis further reveals that high cold-chain fulfillment costs, a shortage of interdisciplinary digital talents, and the absence of a green trust mechanism are the primary bottlenecks restricting supply chain resilience. Future agricultural assistance models must shift from mere technological addition to systemic ecological reconstruction. This involves building an integrated “Cloud-Net-End” digital infrastructure, establishing an industry-education integration mechanism for interest linkage, and perfecting the green data governance system to achieve a dynamic balance between economic value and socio-ecological value.