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Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities

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  • Sengupta, Pooja
  • Biswas, Baidyanath
  • Kumar, Ajay
  • Shankar, Ravi
  • Gupta, Shivam

Abstract

Recent studies on Airbnb have examined the predictors of room prices, successful reservations and customer satisfaction. However, a preliminary investigation of the listings from twenty-two cities across four continents revealed that a significant number of Airbnb homes remained non-booked. Thus, Poisson count-regression techniques cannot efficaciously explain the effects of predictors of successful Airbnb bookings. To address this gap, we proposed a text mining framework using Hurdle-based Poisson and Negative Binomial regressions. We found that the superhost status, host response time, and communication with guests emerged as the most significant predictors irrespective of geographies. We also found that the instant booking option strongly influences the bookings across cities with incoming business visitors. Additionally, we presented a machine learning-based variable-importance scheme, which helps determine the top predictors of successful bookings, to design customized recommendations for attracting more guests and unique advertisement content on P2P accommodation platforms.

Suggested Citation

  • Sengupta, Pooja & Biswas, Baidyanath & Kumar, Ajay & Shankar, Ravi & Gupta, Shivam, 2021. "Examining the predictors of successful Airbnb bookings with Hurdle models: Evidence from Europe, Australia, USA and Asia-Pacific cities," Journal of Business Research, Elsevier, vol. 137(C), pages 538-554.
    • Handle: RePEc:eee:jbrese:v:137:y:2021:i:c:p:538-554
    DOI: 10.1016/j.jbusres.2021.08.035
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    Cited by:

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    3. Pal, Shounak & Biswas, Baidyanath & Gupta, Rohit & Kumar, Ajay & Gupta, Shivam, 2023. "Exploring the factors that affect user experience in mobile-health applications: A text-mining and machine-learning approach," Journal of Business Research, Elsevier, vol. 156(C).
    4. Eachempati, Prajwal & Srivastava, Praveen Ranjan & Kumar, Ajay & Muñoz de Prat, Javier & Delen, Dursun, 2022. "Can customer sentiment impact firm value? An integrated text mining approach," Technological Forecasting and Social Change, Elsevier, vol. 174(C).
    5. Feng, Nan & Xu, Nan & Feng, Haiyang & Li, Minqiang, 2022. "Turn on instant booking or not? Decisions of rival hosts," Annals of Tourism Research, Elsevier, vol. 96(C).
    6. Yeo, Sook Fern & Tan, Cheng Ling & Kumar, Ajay & Tan, Kim Hua & Wong, Jee Kit, 2022. "Investigating the impact of AI-powered technologies on Instagrammers’ purchase decisions in digitalization era–A study of the fashion and apparel industry," Technological Forecasting and Social Change, Elsevier, vol. 177(C).

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