Standing out from the crowd – an exploration of signal attributes of Airbnb listings

Abstract

Purpose -Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers' perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.Design/methodology/approach -A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.Findings -Results show that, in addition to host-specific information such as "Superhost" and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.

From brand identity to brand equity: a multilevel analysis of the organization–employee bidirectional effects in upscale hotels

“…The results showed that the overall external BE relies heavily on the physical facility quality, which is tangible, visible, and easy to be recognized by customers. As signaling theory argues, in an immature market, which lacks sufficient information and understanding, customers must seek observable and creditable signals to ensure service quality and overall experience (Yao et al, 2019). Qualities of hotels are traditionally suggested by star rating, facilities, and brand affiliation, which are standardized and identifiable (Oskam and Boswijk, 2016).…”

Study of Price Determinants of Sharing Economy-Based Accommodation Services: Evidence from Airbnb.com

“…For the category of price determinants related to host attributes, most previous studies showed that whether the host is a "superhost" can increase accommodation prices [12,15,18,20,32], with a few exceptions [19]. Further, counterintuitively, posting a profile picture of a host has a negative effect on the price [12].…”

Booking Prediction Models for Peer-to-peer Accommodation Listings using Logistics Regression, Decision Tree, K-Nearest Neighbor, and Random Forest Classifiers

“…A study using sequential Bayesian [28] aims to understand the booking probability of listings and to know the posterior distribution. Demand forecast is an essential part of revenue management [29] because it maximizes revenue gain [30].…”

“…The predictor variables were based on the findings of previous studies [28] [7] but five predictor variables, namely the number of neighboring listings, available neighboring listings, house rules, property description, and the number of listing pictures were excluded from this study because of data and computing limitations. Instead, we added other variables such as total host listings, host verifications, accommodates, the guests included, minimum nights, and maximum nights.…”