‘You will like it!’ using open data to predict tourists' response to a tourist attraction

2.50
Hdl Handle:
http://hdl.handle.net/2436/620367
Title:
‘You will like it!’ using open data to predict tourists' response to a tourist attraction
Authors:
Pantano, Eleonora; Priporas, Constantinos-Vasilios; Stylos, Nikolaos
Abstract:
The increasing amount of user-generated content spread via social networking services such as reviews, comments, and past experiences, has made a great deal of information available. Tourists can access this information to support their decision making process. This information is freely accessible online and generates so-called “open data”. While many studies have investigated the effect of online reviews on tourists’ decisions, none have directly investigated the extent to which open data analyses might predict tourists’ response to a certain destination. To this end, our study contributes to the process of predicting tourists’ future preferences via MathematicaTM, , software that analyzes a large set of the open data (i.e. tourists reviews) that is freely available on Tripadvisor. This is devised by generating the classification function and the best model for predicting the destination tourists would potentially select. The implications for the tourist industry are discussed in terms of research and practice.
Citation:
‘You will like it!’ using open data to predict tourists' response to a tourist attraction 2017, 60:430 Tourism Management
Publisher:
Elsevier
Journal:
Tourism Management, vol 60, Pages 430–438
Issue Date:
Jan-2017
URI:
http://hdl.handle.net/2436/620367
DOI:
10.1016/j.tourman.2016.12.020
Additional Links:
http://linkinghub.elsevier.com/retrieve/pii/S0261517716302680
Type:
Article
Language:
en
ISSN:
0261-5177
Appears in Collections:
FOSS

Full metadata record

DC FieldValue Language
dc.contributor.authorPantano, Eleonoraen
dc.contributor.authorPriporas, Constantinos-Vasiliosen
dc.contributor.authorStylos, Nikolaosen
dc.date.accessioned2017-02-02T14:55:38Z-
dc.date.available2017-02-02T14:55:38Z-
dc.date.issued2017-01-
dc.identifier.citation‘You will like it!’ using open data to predict tourists' response to a tourist attraction 2017, 60:430 Tourism Managementen
dc.identifier.issn0261-5177en
dc.identifier.doi10.1016/j.tourman.2016.12.020-
dc.identifier.urihttp://hdl.handle.net/2436/620367-
dc.description.abstractThe increasing amount of user-generated content spread via social networking services such as reviews, comments, and past experiences, has made a great deal of information available. Tourists can access this information to support their decision making process. This information is freely accessible online and generates so-called “open data”. While many studies have investigated the effect of online reviews on tourists’ decisions, none have directly investigated the extent to which open data analyses might predict tourists’ response to a certain destination. To this end, our study contributes to the process of predicting tourists’ future preferences via MathematicaTM, , software that analyzes a large set of the open data (i.e. tourists reviews) that is freely available on Tripadvisor. This is devised by generating the classification function and the best model for predicting the destination tourists would potentially select. The implications for the tourist industry are discussed in terms of research and practice.en
dc.language.isoenen
dc.publisherElsevieren
dc.relation.urlhttp://linkinghub.elsevier.com/retrieve/pii/S0261517716302680en
dc.rightsArchived with thanks to Tourism Managementen
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectopen dataen
dc.subjectonline reviewsen
dc.subjecttourismen
dc.subjecttravel propositionsen
dc.title‘You will like it!’ using open data to predict tourists' response to a tourist attractionen
dc.typeArticleen
dc.identifier.journalTourism Management, vol 60, Pages 430–438en
dc.date.accepted2016-12-
rioxxterms.funderInternalen
rioxxterms.identifier.projectUoW020217NSen
rioxxterms.versionAMen
rioxxterms.licenseref.urihttps://creativecommons.org/CC BY-NC-ND 4.0en
rioxxterms.licenseref.startdate2017-01-01en
This item is licensed under a Creative Commons License
Creative Commons
All Items in WIRE are protected by copyright, with all rights reserved, unless otherwise indicated.