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Cited by 726

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Why Tourists Choose Airbnb: A Motivation-Based Segmentation Study

Guttentag, Daniel, Smith, Stephen, Potwarka, Luke, Havitz, Mark·2018·Journal of Travel Research
DOI 10.1177/0047287517696980
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Abstract

Airbnb has grown very rapidly over the past several years, with millions of tourists having used the service. The purpose of this study was to investigate tourists’ motivations for using Airbnb and to segment them accordingly. The study involved an online survey completed in 2015 by more than 800 tourists who had stayed in Airbnb accommodation during the previous 12 months. Aggregate results indicated that respondents were most strongly attracted to Airbnb by its practical attributes, and somewhat less so by its experiential attributes. An exploratory factor analysis identified five motivating factors—Interaction, Home Benefits, Novelty, Sharing Economy Ethos, and Local Authenticity. A subsequent cluster analysis divided the respondents into five segments—Money Savers, Home Seekers, Collaborative Consumers, Pragmatic Novelty Seekers, and Interactive Novelty Seekers. Profiling of the segments revealed numerous distinctive characteristics. Various practical and conceptual implications of the findings are discussed.

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Metadata

Authors
Guttentag, D., Smith, S., Potwarka, L., & Havitz, M.
Venue
Journal of Travel Research
Publisher
SAGE Publications
Published
2017-04-27
Volume
57
Issue
3
Pages
342-359
ISSN
  • 0047-2875 (ISSN-L)
  • 1552-6763
DOI
10.1177/0047287517696980
OpenAlex
W2608271941
Cited by
726
References
4
FWCI
118.33
Type
journal article
Language
en
Open access
closed
Licence
journals.sagepub.com/page/policies/text-and-data-mining-license (tdm)
Funders
Social Sciences and Humanities Research Council of Canada (Joseph-Armand Bombardier Graduate Scholarship)

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.