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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. (2018). Factors influencing hotels’ online prices. Journal of Hospitality Marketing and Management. 27 (4), 443-464
Exportar Referência (IEEE) 
S. M. Moro et al.,  "Factors influencing hotels’ online prices", in Journal of Hospitality Marketing and Management, vol. 27, no. 4, pp. 443-464, 2018
Exportar BibTeX 
@article{moro2018_1714881810002,
	author = "Moro, S. and Rita, P. and Oliveira, C.",
	title = "Factors influencing hotels’ online prices",
	journal = "Journal of Hospitality Marketing and Management",
	year = "2018",
	volume = "27",
	number = "4",
	doi = "10.1080/19368623.2018.1395379",
	pages = "443-464",
	url = "http://www.tandfonline.com/doi/full/10.1080/19368623.2018.1395379"
}
Exportar RIS 
TY  - JOUR
TI  - Factors influencing hotels’ online prices
T2  - Journal of Hospitality Marketing and Management
VL  - 27
IS  - 4
AU  - Moro, S.
AU  - Rita, P.
AU  - Oliveira, C.
PY  - 2018
SP  - 443-464
SN  - 1936-8623
DO  - 10.1080/19368623.2018.1395379
UR  - http://www.tandfonline.com/doi/full/10.1080/19368623.2018.1395379
AB  - Digital corporations are creating new paths of business driven by consumers empowered by social media. Understanding the role that each feature drawn from online platforms has on price fluctuation is vital for leveraging decision making.
In this study, 5603 simulations of online reservations from 23 Portuguese cities were gathered, including characterizing features from social media, web visibility and hotel amenities, from four renowned online sources: Booking.com, TripAdvisor, Google, and Facebook. After data preparation, including removal of irrelevant features in terms of modeling and outlier cleaning, a tuned dataset of 3137 simulations and 30 features (including the price charged per day) was used first for evaluating the modeling performance of an ensemble of multilayer perceptrons, and then for extracting valuable knowledge through the data-based sensitivity analysis.
Findings show that all features from the encompassed factors (social media, online reservation, hotel characteristics, web visibility and city) play a significant role in price.
ER  -