信任迁移理论视域下AI智能体重构电商消费入口机制研究

Research on the Reconfiguration of E-Commerce Consumption Entry Points Mechanisms by AI Agents from the Perspective of Trust Transfer Theory

摘要: 2026年被业界视为AI购物普及元年，人工智能正从辅助工具升级为电商购物场景中的基础设施。本文聚焦AI智能体如何重构电商消费入口这一核心问题，基于技术接受模型与信任迁移理论，采用案例比较法，选取阿里千问(生态闭环模式)与Kimi (中立导购模式)作为典型平台，从决策链路压缩、流量分配逻辑重塑及用户控制感再平衡三个维度展开分析。研究发现，AI智能体通过将传统搜索浏览后比价的电商消费链路压缩为对话推荐下单的新模式，显著提升了购物效率，同时使平台竞争焦点从流量规模转向需求理解深度；生态闭环模式与中立导购模式在转化效率、商品范围、用户信任基础等方面各有优劣，分别适用于不同消费场景，未来可能走向融合。本文为理解AI驱动下的消费行为变迁提供了理论视角，也为电商平台AI战略制定与商家营销策略调整提供了实践参考。

Abstract: The year 2026 is regarded by the industry as the first year of AI shopping popularization, as artificial intelligence is evolving from a supporting tool into the infrastructure of e-commerce shopping scenarios. This paper focuses on how AI agents are restructuring e-commerce consumption entry points. Based on the Technology Acceptance Model and Trust Transfer Theory, and employing a case comparison method, this study selects Alibaba Qianwen (an ecosystem-closed-loop model) and Kimi (a neutral shopping guide model) as representative platforms. The analysis is conducted from three dimensions: decision path compression, reshaping of traffic allocation logic, and rebalancing of user perceived control. The findings reveal that AI agents significantly improve shopping efficiency by compressing the traditional search-browse-price comparison path into a new dialogue-recommend-order model, while shifting the focus of platform competition from traffic volume to the depth of demand understanding. The ecosystem-closed-loop model and the neutral shopping guide model each have their own strengths and weaknesses in terms of conversion efficiency, product range, and user trust foundation, and are suitable for different consumption scenarios, with a potential trend toward integration in the future. This paper provides a theoretical perspective for understanding AI-driven changes in consumer behavior, as well as practical implications for e-commerce platforms in formulating AI strategies and for merchants in adjusting their marketing strategies.