Generative Search: Evidence from a Large-Scale Field Experiment

Generative search is an emerging search paradigm that integrates Generative AI (GenAI) into traditional search engines by presenting users with AI-generated responses before conventional search results. Whereas keyword-based search requires consumers to translate their underlying needs into effective keyword queries, generative search lets users express those intentions directly in natural language, a shift made possible by GenAI’s new mode of information presentation. This shift moves the consumer-search engine interaction upstream to the stage of problem formulation, rendering empirically observable a previously hidden phase of search behavior: the mapping from problem formulation to keyword articulation. Yet, it remains empirically unknown whether generative search increases consumer purchases and how search behavior changes when it can begin from expressed intent rather than keywords. Using a large-scale field experiment conducted on Meituan, a leading Chinese technology platform, this paper provides empirical evidence on the effectiveness of generative search. We find that generative search significantly increases consumer purchases. Further analysis reveals that generative search facilitates more effective and diverse keyword queries and reduces exploratory browsing and clicking, while concentrating evaluation within relevant categories and merchants. The empirical evidence is most consistent with a mechanism in which AI-generated answers provide information about consumers’ underlying needs, while also expanding awareness of relevant attributes and shifting attention toward more relevant categories. These findings provide empirical insights for future consumer search models that allow search to originate at the intention-expressing stage.

This paper was accepted by Raphael Thomadsen, marketing.

Funding: Financial support from the Ministry of Education – Singapore [Grant A-8001730-00-00] is gratefully acknowledged.

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2025.02458.