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

Sengupta, Pooja orcid logoORCID: 0000-0002-7859-2435, Biswas, Baidyanath orcid logoORCID: 0000-0002-0609-3530, Kumar, Ajay, Shankar, Ravi and 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, 137 . pp. 538-554. ISSN 0148-2963

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.
Item Type:Article (Published)
Uncontrolled Keywords:Sharing economy; Airbnb; Over-dispersion; Text analytics; Hurdle models
Subjects:Business > Electronic commerce
Business > Marketing
DCU Faculties and Centres:DCU Faculties and Schools > DCU Business School
Official URL:https://dx.doi.org/10.1016/j.jbusres.2021.08.035
Copyright Information:© 2021 Elsevier
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:26774
Deposited On:23 Mar 2022 09:58 by Baidyanath Biswas . Last Modified 23 Mar 2022 09:58

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