Standing out from the crowd – an exploration of signal attributes of Airbnb listings - Bournemouth University Staff Profile Pages

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

Authors: Yao, B., Qiu, R.T.R., Fan, D.X.F., Liu, A. and Buhalis, D.

Journal: International Journal of Contemporary Hospitality Management

Volume: 31

Issue: 12

Pages: 4520-4542

ISSN: 0959-6119

DOI: 10.1108/IJCHM-02-2019-0106

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. Originality/value: This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.

http://eprints.bournemouth.ac.uk/32505/

Source: Scopus