earticle

영어

In peer-to-peer accommodation platforms like Airbnb, property descriptions serve not only as informative content but also as persuasive tools that shape user expectations and decisions. While previous studies have focused on visual cues, pricing, and reviews, the linguistic style and framing of these descriptions remain underexplored. This study investigates how the informational versus emotional orientation of property descriptions—and the presence of negative keywords—affect two key user outcomes: booking behavior and customer satisfaction. Grounded in expectation disconfirmation theory, the study conceptualizes textual descriptions as signals that manage user expectations and convey authenticity or risk. Using a large-scale dataset of over 60,000 Airbnb listings in the United States, we apply natural language processing (NLP) and sentiment analysis to classify descriptions and identify negative expressions. These linguistic features are then used as explanatory variables in Poisson and negative binomial regression models to analyze count-based booking behavior, and in OLS models for continuous satisfaction ratings. We also introduce interaction terms to examine the moderating effects of negative keywords. Our research addresses the following questions: (1) How do information-based and emotion-based descriptions affect booking and satisfaction outcomes? (2) Does the presence of negative keywords enhance or undermine the persuasive effectiveness of these descriptions? (3) How do these textual strategies interact to shape user judgment? This study contributes to the literature on platform-mediated markets by demonstrating how language functions not only as a means of communication but also as a strategic framing and signaling mechanism. It shows that textual cues can shape trust, credibility, and expectation managementin online peer-to-peer environments. From a practical standpoint, platforms can leverage these insights to design tools and nudges that assist hosts in crafting effective, trustworthy, and expectation-aligned descriptions.