Factors influencing hotels’ online prices
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10071/15412 |
Resumo: | 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. |
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Factors influencing hotels’ online pricesOnline bookingPricingHotel reservationSocial mediaData miningDigital 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.Taylor and Francis2018-03-21T10:06:30Z2019-09-21T00:00:00Z2018-01-01T00:00:00Z20182019-03-20T11:30:44Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10071/15412eng1936-862310.1080/19368623.2018.1395379Moro, S.Rita, P.Oliveira, C.info:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-11-09T17:59:38Zoai:repositorio.iscte-iul.pt:10071/15412Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T22:31:21.153415Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Factors influencing hotels’ online prices |
title |
Factors influencing hotels’ online prices |
spellingShingle |
Factors influencing hotels’ online prices Moro, S. Online booking Pricing Hotel reservation Social media Data mining |
title_short |
Factors influencing hotels’ online prices |
title_full |
Factors influencing hotels’ online prices |
title_fullStr |
Factors influencing hotels’ online prices |
title_full_unstemmed |
Factors influencing hotels’ online prices |
title_sort |
Factors influencing hotels’ online prices |
author |
Moro, S. |
author_facet |
Moro, S. Rita, P. Oliveira, C. |
author_role |
author |
author2 |
Rita, P. Oliveira, C. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Moro, S. Rita, P. Oliveira, C. |
dc.subject.por.fl_str_mv |
Online booking Pricing Hotel reservation Social media Data mining |
topic |
Online booking Pricing Hotel reservation Social media Data mining |
description |
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. |
publishDate |
2018 |
dc.date.none.fl_str_mv |
2018-03-21T10:06:30Z 2018-01-01T00:00:00Z 2018 2019-09-21T00:00:00Z 2019-03-20T11:30:44Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10071/15412 |
url |
http://hdl.handle.net/10071/15412 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
1936-8623 10.1080/19368623.2018.1395379 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.publisher.none.fl_str_mv |
Taylor and Francis |
publisher.none.fl_str_mv |
Taylor and Francis |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
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1799134875464237056 |