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What makes a good “guest”: Evidence from Airbnb hosts' reviews

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  • Xue, Lan
  • Leung, Xi Y.
  • Ma, Shihan (David)

Abstract

The purpose of this study is to systematically explore the characteristics of being a good “guest” from the perspective of Airbnb hosts. The study adopts a mixed method combining big data analysis with qualitative content analysis to analyze 10,068 reviews posted by Airbnb hosts. A framework is developed to organize the identified themes and illustrate the characteristics of being a good guest in the context of Airbnb. The framework included three layers in a hierarchical manner: customer-like (low level), guest-like (medium level), and friend-like (high level). The higher the level, the more interaction and engagement from both sides. The findings suggest that Airbnb is primarily considered a consumption space that stressed effective interaction between hosts and guests. The study renders practical implications for platforms to improve guest behaviors and enhance host-guest relationship in the sharing economy.

Suggested Citation

  • Xue, Lan & Leung, Xi Y. & Ma, Shihan (David), 2022. "What makes a good “guest”: Evidence from Airbnb hosts' reviews," Annals of Tourism Research, Elsevier, vol. 95(C).
  • Handle: RePEc:eee:anture:v:95:y:2022:i:c:s0160738322000779
    DOI: 10.1016/j.annals.2022.103426
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    References listed on IDEAS

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    1. Zhu, Yunxia & Cheng, Mingming & Wang, Jie & Ma, Laikun & Jiang, Ruochen, 2019. "The construction of home feeling by Airbnb guests in the sharing economy: A semantics perspective," Annals of Tourism Research, Elsevier, vol. 75(C), pages 308-321.
    2. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    3. Li, Jianping & Feng, Yuyao & Li, Guowen & Sun, Xiaolei, 2020. "Tourism companies' risk exposures on text disclosure," Annals of Tourism Research, Elsevier, vol. 84(C).
    4. Liang Tang & Silong Peng & Yiming Bi & Peng Shan & Xiyuan Hu, 2014. "A New Method Combining LDA and PLS for Dimension Reduction," PLOS ONE, Public Library of Science, vol. 9(5), pages 1-10, May.
    5. Al-Natour, Sameh & Turetken, Ozgur, 2020. "A comparative assessment of sentiment analysis and star ratings for consumer reviews," International Journal of Information Management, Elsevier, vol. 54(C).
    6. Cheng, Mingming & Zhang, Guojie, 2019. "When Western hosts meet Eastern guests: Airbnb hosts' experience with Chinese outbound tourists," Annals of Tourism Research, Elsevier, vol. 75(C), pages 288-303.
    7. Georgios Zervas & Davide Proserpio & John W. Byers, 2021. "A first look at online reputation on Airbnb, where every stay is above average," Marketing Letters, Springer, vol. 32(1), pages 1-16, March.
    8. Amélie Fiorello & Damien Bo, 2012. "Community-based ecotourism to meet the new tourist's expectations: an exploratory study," Post-Print hal-00951031, HAL.
    9. Grimwood, Bryan S.R. & Yudina, Olga & Muldoon, Meghan & Qiu, Ji, 2015. "Responsibility in tourism: A discursive analysis," Annals of Tourism Research, Elsevier, vol. 50(C), pages 22-38.
    10. Chien, P. Monica & Ritchie, Brent W., 2018. "Understanding intergroup conflicts in tourism," Annals of Tourism Research, Elsevier, vol. 72(C), pages 177-179.
    11. Sharpley, Richard, 2014. "Host perceptions of tourism: A review of the research," Tourism Management, Elsevier, vol. 42(C), pages 37-49.
    12. Pera, Rebecca & Viglia, Giampaolo & Grazzini, Laura & Dalli, Daniele, 2019. "When empathy prevents negative reviewing behavior," Annals of Tourism Research, Elsevier, vol. 75(C), pages 265-278.
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