arXiv:2609.18729v1 Announce Type: cross
Abstract: Consumers increasingly use AI chatbots for advice on what to buy. With companies like OpenAI and Google monetising their AI through advertising, this...
By Lucas G. Uberti-Bona Marin, Thales Bertaglia, Giovanni Astante, Bram Rijsbosch, Gijs van Dijck, Anik\'o Hann\'ak, Gerasimos Spanakis, Konrad Kollnig
The study investigates how people evaluate AI-generated financial advice by conducting a randomized vignette experiment with 285 U.S. adults. Participants were presented with consistent financial recommendations delivered in three styles—AI, expert, and online community—alongside source labels. The results show that advice style most strongly influenced message and safety appraisals, expert labels increased perceived source knowledge, and decision context shaped risk and safety judgments, with these appraisals explaining a large portion of overall quality, trust, and intended reliance.
By Aryan Ramchandra Kapadia, Eshwar Chandrasekharan, Koustuv Saha
arXiv:2609.24644v1 Announce Type: cross
Abstract: As people turn to generative AI for financial advice, these systems can personalize how they communicate and what they say. Whether these forms of pe...
By Hasibur Rahman, Benjamin R. Cowan, Smit Desai
arXiv:2603. 23433v3 Announce Type: replace Abstract: AI agents are becoming active decision-makers on the Internet.
By Giulio Frey, Kawin Ethayarajh
arXiv:2608. 09019v1 Announce Type: cross Abstract: As generative AI increasingly becomes a common source of daily decision-making, including financial choices, it is critical to understand how people evaluate AI-generated financial advice.
By Aryan Ramchandra Kapadia, Eshwar Chandrasekharan, Koustuv Saha
The study investigates how Large Language Models (LLMs) acting as surrogate consumers are influenced by marketing pricing cues such as just‑below pricing and promotional framing. Using a tool called "Tool‑Lab" to trace information acquisition, the researchers found that when no cost is imposed, pricing cues rarely mislead LLMs, but when acquisition costs are introduced under a vague goal prompt, LLMs tend to omit important diagnostic attributes and make suboptimal choices similar to human heuristics. The findings suggest that marketing heuristics in AI‑driven shopping are shaped more by storefront information architecture than by inherent LLM limitations.
By Davood Wadi, Yu Ma