Guiding without Generating: Artificial Intelligence (AI)-Enabled Topic Nudges in Online Reviews
Fangyan Wang, Sai Liang, Zaiyan Wei

TL;DR
This study examines how AI-enabled topic nudges in online reviews influence review content, length, complexity, and helpfulness, revealing both benefits in coverage and drawbacks in readability and perceived usefulness.
Contribution
It provides empirical evidence on AI-guided prompts shaping user-generated content, highlighting effects on review diversity, length, complexity, and helpfulness, with insights into user heterogeneity.
Findings
Nudges increase topical coverage and review length.
Reviews become more complex and less readable.
Helpfulness votes decrease, especially when content lacks focus.
Abstract
Digital platforms increasingly face a common challenge in the age of artificial intelligence (AI): how to elicit richer and more useful user-generated content (UGC) without fully automating content production. We study this question in the context of online reviews by examining Yelp's introduction of an AI-enabled topic nudging tool in 2023, which provides real-time prompts to guide reviewers in addressing key dimensions of the dining experience as they write. Using more than 1.5 million Yelp reviews and a differences-in-differences design, we find that AI-enabled topic nudges significantly reshape review generation. The nudges expand topical coverage, especially for underrepresented aspects such as service and ambiance, and lead to longer reviews, but they also reduce overall review volume. In addition, reviews become more textually complex and less readable, and receive fewer…
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