The price of advice: Experimental evidence on the effects of AI recommenders

Amit Zac and Michal S. Gal

No 375, Working Papers from The University of Chicago Booth School of Business, George J. Stigler Center for the Study of the Economy and the State

Abstract: The integration of large language models (LLMs) into recommender systems (RS) has given rise to a new generation of Conversational RS (CRS). This study asks how CRS systems shape consumer behavior, and, in particular, their spending. Despite the rapid proliferation of such systems, including widely used tools like OpenAI's ChatGPT and Google's Gemini, we still lack evidence on their behavioral effects. This study provides the first controlled empirical test of CRS influence on real purchasing decisions. In a laboratory experiment, complemented by large-scale API studies, participants were randomly assigned to one of four conditions: a traditional search baseline, GPT, Gemini, or a customized GPT designed to steer users toward more expensive products. CRS consistently increase consumer expenditures, with Customized GPT producing the highest average spending. Importantly, these effects are not driven by differences in perceived product quality, prior shopping experience, or generalized trust. Rather, they stem from subtle linguistic framing and increased exposure to premium brands. Taken together, the findings position LLM-based CRS as novel and potent choice architects with downstream implications for consumer protection, market design, and regulatory oversight.

Keywords: recommendation systems; conversational recommender systems (CRS); largelanguage models (LLMS); LLM-empowered recommendation systems; consumerbehavior; algorithmic choice architecture; algorithmic bias; human-AI shoppinginteraction; trust in CRS; algorithmic regulation (search for similar items in EconPapers)
Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:zbw:cbscwp:336742

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