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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA)
Moro, S., Rita, P., Oliveira, C., Batista, F. & Ribeiro, R. (2018). Leveraging national tourist offices through data analytics. International Journal of Culture, Tourism, and Hospitality Research. 12 (4), 420-426
Exportar Referência (IEEE)
S. M. Moro et al.,  "Leveraging national tourist offices through data analytics", in Int. Journal of Culture, Tourism, and Hospitality Research, vol. 12, no. 4, pp. 420-426, 2018
Exportar BibTeX
@article{moro2018_1711703616279,
	author = "Moro, S. and Rita, P. and Oliveira, C. and Batista, F. and Ribeiro, R.",
	title = "Leveraging national tourist offices through data analytics",
	journal = "International Journal of Culture, Tourism, and Hospitality Research",
	year = "2018",
	volume = "12",
	number = "4",
	doi = "10.1108/IJCTHR-04-2018-0051",
	pages = "420-426",
	url = "https://www.emeraldinsight.com/loi/ijcthr"
}
Exportar RIS
TY  - JOUR
TI  - Leveraging national tourist offices through data analytics
T2  - International Journal of Culture, Tourism, and Hospitality Research
VL  - 12
IS  - 4
AU  - Moro, S.
AU  - Rita, P.
AU  - Oliveira, C.
AU  - Batista, F.
AU  - Ribeiro, R.
PY  - 2018
SP  - 420-426
SN  - 1750-6182
DO  - 10.1108/IJCTHR-04-2018-0051
UR  - https://www.emeraldinsight.com/loi/ijcthr
AB  - Purpose
This study aims to propose a data-driven approach, based on open-source tools, that makes it possible to understand customer satisfaction of the accommodation offer of a whole country.
Design/methodology/approach
The method starts by extracting information from all hotels of Portugal available at TripAdvisor through Web scraping. Then, a support vector machine is adopted for modeling the TripAdvisor score, which is considered a proxy of customer satisfaction. Finally, knowledge extraction from the model is achieved using sensitivity analysis to unveil the influence of features on the score.
Findings
The model of the TripAdvisor score achieved a mean absolute percentage error of around 5 per cent, proving the value of modeling the extracted data. The number of rooms of the unit and the minimum price are the two most relevant features, showing that customers appreciate smaller and more expensive units, whereas the location of the hotel does not hold significant relevance.
Originality/value
National tourist offices can use the proposed approach to understand what drives tourists’ satisfaction, helping to shape a country’s strategy. For example, licensing new hotels may take into account the unit size and other characteristics that make it more attractive to tourists. Furthermore, the procedure can be replicated at any time and in any country, making it a valuable tool for data-driven decision support on a national scale.
ER  -