The Impact of AI-Generated Personalized Advertising on Consumer Trust, Privacy Violation Perceptions, and Purchase Intensity
Session
Management Business and Economics
Description
The rapid adoption of artificial intelligence (AI) in digital advertising enables highly granular personalization but also raises consumer concerns about privacy and trust. This paper investigates how AI-generated personalization affects consumer trust, perceived privacy violation, and purchase intensity, and examines the moderating role of transparency about data use. Grounded in privacy calculus and trust formation theories, we propose and test a conceptual model in which perceived privacy violation mediates the relationship between AI personalization and purchase intensity, while transparency attenuates negative privacy perceptions and strengthens trust. We employ a mixed-methods design. First, a preregistered randomized online experiment (2 x 2 factorial: AI-personalized vs. non-AI-personalized × high vs. low transparency) will measure attitudinal outcomes (trust, perceived privacy violation, purchase intention) and observed behavioral proxies (click-through, simulated conversion) with an anticipated sample of N ≈ 400 participants. Structural equation modeling (SEM) will test mediation and moderation hypotheses. Second, we will triangulate experimental findings with behavioral log analysis from an e-commerce/A-B testing dataset (subject to partner collaboration), using difference-indifferences and hierarchical models to assess real-world effects on conversion rates and repeat purchase behavior. This study contributes theoretically by integrating AI personalization into privacy–trust frameworks and empirically by combining controlled experimentation with behavioral validation. Practically, it offers evidence-based guidance for marketers on balancing personalization gains and privacy-preserving transparency strategies. Findings aim to inform ethical, consumer-centered implementation of AI in advertising and suggest regulatory and managerial implications for sustaining long-term consumer trust.
Keywords:
AI personalization, consumer privacy, purchase intensit
Proceedings Editor
Edmond Hajrizi
Location
UBT Lipjan, Kosovo
Start Date
25-10-2025 9:00 AM
End Date
26-10-2025 6:00 PM
DOI
10.33107/ubt-ic.2025.411
Recommended Citation
Arapi, Rajan and Xheladini, Ajsel, "The Impact of AI-Generated Personalized Advertising on Consumer Trust, Privacy Violation Perceptions, and Purchase Intensity" (2025). UBT International Conference. 26.
https://knowledgecenter.ubt-uni.net/conference/2025UBTIC/MBE/26
The Impact of AI-Generated Personalized Advertising on Consumer Trust, Privacy Violation Perceptions, and Purchase Intensity
UBT Lipjan, Kosovo
The rapid adoption of artificial intelligence (AI) in digital advertising enables highly granular personalization but also raises consumer concerns about privacy and trust. This paper investigates how AI-generated personalization affects consumer trust, perceived privacy violation, and purchase intensity, and examines the moderating role of transparency about data use. Grounded in privacy calculus and trust formation theories, we propose and test a conceptual model in which perceived privacy violation mediates the relationship between AI personalization and purchase intensity, while transparency attenuates negative privacy perceptions and strengthens trust. We employ a mixed-methods design. First, a preregistered randomized online experiment (2 x 2 factorial: AI-personalized vs. non-AI-personalized × high vs. low transparency) will measure attitudinal outcomes (trust, perceived privacy violation, purchase intention) and observed behavioral proxies (click-through, simulated conversion) with an anticipated sample of N ≈ 400 participants. Structural equation modeling (SEM) will test mediation and moderation hypotheses. Second, we will triangulate experimental findings with behavioral log analysis from an e-commerce/A-B testing dataset (subject to partner collaboration), using difference-indifferences and hierarchical models to assess real-world effects on conversion rates and repeat purchase behavior. This study contributes theoretically by integrating AI personalization into privacy–trust frameworks and empirically by combining controlled experimentation with behavioral validation. Practically, it offers evidence-based guidance for marketers on balancing personalization gains and privacy-preserving transparency strategies. Findings aim to inform ethical, consumer-centered implementation of AI in advertising and suggest regulatory and managerial implications for sustaining long-term consumer trust.