Personalized to Persuade: The Effects of Contextualization and Warmth on Trust and Reliance in Conversational AI
Artificial Intelligence (AI) agents personalize their responses by tailoring explanations to users' backgrounds, interests, and prior interactions, referred to as contextualization. Personalization has been identified as a persuasive strategy in politics or in marketing. However, the persuasive effect of contextualization in everyday tasks, where users often lack prior knowledge, remains unclear. We conducted a $2\times2$ between-subjects experiment ($N = 380$) examining how contextualization, combined with conversational warmth, shapes reliance and persuasiveness of an AI assistant arguing against expert recommendations. Our findings reveal that contextualization reduces the persuasive p...
| Authors | Mert Yazan, Suzan Verberne, Frederik Bungaran Ishak Situmeang |
|---|---|
| Primary category | cs.HC |
| Published | 29 May 2026 |
| Last updated at source | 29 May 2026 |
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