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Standing out from the crowd - An exploration of signal attributes of Airbnb listings.

Yao, B., Qiu, R.T.R., Fan, X., Liu, A. and Buhalis, D., 2019. Standing out from the crowd - An exploration of signal attributes of Airbnb listings. International Journal of Contemporary Hospitality Management, 31 (12), 4520-4542.

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DOI: 10.1108/IJCHM-02-2019-0106


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 applies the signaling theory to the booking of Airbnb listings and explores the influence of quality signals on the odds of an Airbnb listing being booked. Methodology 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. 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.

Item Type:Article
Uncontrolled Keywords:Airbnb; booking probability; signaling theory; binomial logistic model; sequential Bayesian updating; big data; sharing economy
Group:Bournemouth University Business School
ID Code:32505
Deposited By: Symplectic RT2
Deposited On:10 Jul 2019 08:11
Last Modified:14 Mar 2022 14:16


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