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Publication Detailed Description
Journal Title
Journal of China Tourism Research
Year (definitive publication)
2019
Language
English
Country
Netherlands
More Information
Web of Science®
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Abstract
This study employed a data mining approach to model the quantitative scores given to hostels located in Beijing, China, and Lisbon, Portugal, in guests’ online reviews posted on Booking.com. A neural network was built using a total of nine input features (e.g. age, most and least favorite aspects, travel and traveler types, nationality, hostel, and month and weekday of review) to model the score distributions. Each feature’s contribution to the scores was then extracted through data-based sensitivity analysis. The most favorite aspect and continent of origin were the two most significant features for hostels in both cities. Lisbon guests were also highly influenced by the hostel itself and traveler type as compared with Beijing travelers. Notably, facilities are the most favorite aspect valued by guests staying in Lisbon, while those that stay in Beijing hostels give more importance to value for money. These findings denote different guest behaviors are associated with each city’s particular offerings.
Acknowledgements
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Keywords
Service quality,Hostels,Online reviews,Data mining,Beijing,Lisbon
Fields of Science and Technology Classification
- Economics and Business - Social Sciences
- Sociology - Social Sciences
- Languages and Literature - Humanities
Funding Records
| Funding Reference | Funding Entity |
|---|---|
| UID/SOC/03127/2013 | Fundação para a Ciência e a Tecnologia |
| UID/MULTI/0446/2013 | Fundação para a Ciência e a Tecnologia |
| UID/PSI/03125/2013 | Fundação para a Ciência e a Tecnologia |
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