Decoding the Trust Matrix
- Xinyi Zuo
- Nov 11, 2025
- 1 min read
Feng, Y., & Kim, H. J. (2025). Decoding the trust matrix: Unraveling key predictors of consumer trust in AI-generated personalized advertising. Journal of Interactive Advertising, 1-16. https://doi.org/10.1080/15252019.2025.2468286
Artificial intelligence (ai)-generated personalized advertising (aiGpa) refers to the practice of marketers employing advanced machine learning algorithms to leverage consumer data for creating relevant advertising content at a reduced cost. In this research, we explored the determinants of consumer trust in aiGpa by focusing on aspects related to the source, the receiver (consumer), and the context. We measured consumer trust by considering indicators associated with both the source (reliability, ability, usefulness, and trustworthiness) and the receiver (willingness to rely on). Results from a survey of 1,144 participants revealed that consumer trust in aiGpa was positively predicted by two source-related factors (ad transparency and ad similitude), two receiver-related factors (consumer personalization need and consumer ai familiarity), and was also influenced by one context-related factor (ai policy uncertainty). The theoretical and practical implications are discussed.
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