AI-Driven Personalized Marketing: A Study on the Balance Between Consumer Trust Building and Privacy Protection
- DOI
- 10.2991/978-94-6463-835-6_92How to use a DOI?
- Keywords
- artificial intelligence; personalized marketing; consumer trust; privacy computing; algorithm transparency
- Abstract
With the widespread application of generative artificial intelligence in the marketing field, personalized marketing has become the core strategy of enterprise digital transformation. However, the tension between personalized services and privacy protection constitutes the “personalization-privacy paradox”, which seriously affects consumer acceptance. Based on the privacy computing theory and technology acceptance model, this study systematically combed 522 relevant literature and constructed a multi-level theoretical model of consumer trust formation in the context of artificial intelligence marketing. The study found that the impact of algorithm transparency on trust is an inverted U-shaped relationship, the personalization-privacy paradox contains dual sub-mechanisms, and cultural factors significantly regulate the trust formation process.
- Copyright
- © 2025 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Jingya Yu PY - 2025 DA - 2025/09/17 TI - AI-Driven Personalized Marketing: A Study on the Balance Between Consumer Trust Building and Privacy Protection BT - Proceedings of the 2025 3rd International Academic Conference on Management Innovation and Economic Development (MIED 2025) PB - Atlantis Press SP - 871 EP - 876 SN - 2352-5428 UR - https://doi.org/10.2991/978-94-6463-835-6_92 DO - 10.2991/978-94-6463-835-6_92 ID - Yu2025 ER -