A text analytics framework for understanding the relationships among host self-description, trust perception and purchase behavior on Airbnb

Abstract Trust plays an important role in sharing transactions on short-term rental platforms. However, the impact of host self-description on trust perception and whether trust perception can influence purchase behavior remain under-studied. Therefore, a text analytics framework was proposed to research the relationships among host self-description, trust perception and purchase behavior on Airbnb. Specifically, a deep-learning-based method was designed to automatically code trust perception of host self-descriptions. And the linguistic and semantic features of description texts were extracted with text mining methods. The estimated order quantity was used to quantify purchase behavior. Then, the influence of linguistic and semantic features on trust perception was identified, and the relationship between trust perception and purchase behavior was also verified. The empirical analysis derives the following findings: i. The readability of self-description is positively associated with trust perception; ii. Perspective taking expressed in self-description is also helpful; iii. Excessive positive sentiment expression can raise barriers to trust building; iv. Paying more attention to family relationship, openness, service and travel experience in self-description would be helpful; v. Trust perception can promote purchases. These findings can help hosts write better self-description, which contributes to trust building and purchases on short-term rental platforms.