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

Bin Yao, Richard T.R. Qiu, Daisy X.F. Fan, Anyu Liu, Dimitrios Buhalis

Research output: Journal article publicationJournal articleAcademic researchpeer-review

35 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)4520-4542
Number of pages23
JournalInternational Journal of Contemporary Hospitality Management
Issue number12
Publication statusPublished - 22 Nov 2019
Externally publishedYes


  • Airbnb
  • Big data
  • Binomial logistic model
  • Booking probability
  • Sequential Bayesian updating
  • Sharing economy
  • Signaling theory

ASJC Scopus subject areas

  • Tourism, Leisure and Hospitality Management


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