Residential College | false |
Status | 已發表Published |
Standing out from the crowd – An investigation of the signal attributes of Airbnb listings | |
Bin Yao1; Richard T.R. Qiu2; Daisy X.F. Fan3; Anyu Liu4; Dimitrios Buhalis3 | |
2019 | |
Source Publication | International Journal of Contemporary Hospitality Management |
ABS Journal Level | 3 |
ISSN | 0959-6119 |
Volume | 32Issue:12Pages:4520-4542 |
Abstract | PurposeDue 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/approachA 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. FindingsResults 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/valueThis 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. |
Keyword | Signaling Theory Big Data Airbnb Binomial Logistic Model Booking Probability Sequential Bayesian Updating Sharing Economy |
DOI | 10.1108/IJCHM-02-2019-0106 |
URL | View the original |
Indexed By | SSCI |
Language | 英語English |
WOS Research Area | Social Sciences - Other Topics ; Business & Economics |
WOS Subject | Hospitality, Leisure, Sport & Tourism ; Management |
WOS ID | WOS:000497872100007 |
Scopus ID | 2-s2.0-85073932489 |
Fulltext Access | |
Citation statistics | |
Document Type | Journal article |
Collection | Faculty of Business Administration DEPARTMENT OF INTEGRATED RESORT AND TOURISM MANAGEMENT |
Corresponding Author | Richard T.R. Qiu |
Affiliation | 1.School of Economics, Liaoning University, Shenyang, China 2.Department of Integrated Resort and Tourism Management, Faculty of Business Administration, University of Macau, Taipa, Macao 3.eTourism Lab, International Centre for Tourism and Hospitality Research, Bournemouth University, Poole, UK 4.School of Hospitality and Tourism Management, University of Surrey,Guildford, UK |
Corresponding Author Affilication | Faculty of Business Administration |
Recommended Citation GB/T 7714 | Bin Yao,Richard T.R. Qiu,Daisy X.F. Fan,et al. Standing out from the crowd – An investigation of the signal attributes of Airbnb listings[J]. International Journal of Contemporary Hospitality Management, 2019, 32(12), 4520-4542. |
APA | Bin Yao., Richard T.R. Qiu., Daisy X.F. Fan., Anyu Liu., & Dimitrios Buhalis (2019). Standing out from the crowd – An investigation of the signal attributes of Airbnb listings. International Journal of Contemporary Hospitality Management, 32(12), 4520-4542. |
MLA | Bin Yao,et al."Standing out from the crowd – An investigation of the signal attributes of Airbnb listings".International Journal of Contemporary Hospitality Management 32.12(2019):4520-4542. |
Files in This Item: | Download All | |||||
File Name/Size | Publications | Version | Access | License | ||
Yao&Qiu&Fan&Liu&Buha(493KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | View Download |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment