Marketing Dilemmas and Countermeasures in the Context of AI Model

AI的深度应用正在重构市场营销范式,在提升效率的同时带来多重危机:一是引发信任危机;二是企业创新陷入数据依赖循环;三是伦理失范难以避免。针对困局成因,尝试提出“有限智能化原则”“反哺式训练机制”“伦理动态化治理”三元协同的对策,为企业在技术迭代中平衡商业价值与社会责任提供理论参考。The in-depth application of AI is reconstructing the marketing paradigm. While improving efficiency, it brings multiple crises: first, it triggers a trust crisis; second, enterprises’ innovation falls into a data-dependence cycle; third, ethical anomie is difficult to avoid. In view of the causes of these dilemmas, this paper attempts to propose a three-element coordinated countermeasure, namely the “principle of limited intelligence”, the “feedback training mechanism”, and the “dynamic ethical governance”, providing a theoretical reference for enterprises to balance business value and social responsibility in technological iteration.

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Publication Details

Journal
E-Commerce Letters
Published
2025-01-01
DOI
https://doi.org/10.12677/ecl.2025.1451464
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
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article

Marketing Dilemmas and Countermeasures in the Context of AI Model

万丽 陈
E-Commerce Letters
AI in Service Interactions
article

Marketing Dilemmas and Countermeasures in the Context of AI Model

万丽 陈
article en

Abstract

AI的深度应用正在重构市场营销范式,在提升效率的同时带来多重危机:一是引发信任危机;二是企业创新陷入数据依赖循环;三是伦理失范难以避免。针对困局成因,尝试提出“有限智能化原则”“反哺式训练机制”“伦理动态化治理”三元协同的对策,为企业在技术迭代中平衡商业价值与社会责任提供理论参考。The in-depth application of AI is reconstructing the marketing paradigm. While improving efficiency, it brings multiple crises: first, it triggers a trust crisis; second, enterprises’ innovation falls into a data-dependence cycle; third, ethical anomie is difficult to avoid. In view of the causes of these dilemmas, this paper attempts to propose a three-element coordinated countermeasure, namely the “principle of limited intelligence”, the “feedback training mechanism”, and the “dynamic ethical governance”, providing a theoretical reference for enterprises to balance business value and social responsibility in technological iteration.

E-Commerce LettersVol. 14(05)
Openalex Percentile: Top 95%
AI in Service Interactions
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