Cited by 50Diverse Aspects of Tourism ResearchMigration, Aging, and Tourism StudiesSemantic Web and Ontologies

Using ontology-based clustering to understand the push and pull factors for British tourists visiting a Mediterranean coastal destination

Open access
Valls, Aida, Gibert, Karina, Orellana, Alícia, Antón-Clavé, Salvador·2018·Information & Management

Integrity signals

Registry

Crossref · 10.1016/j.im.2017.05.002

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140 of 140 references verified0 citation errors · 0 not checkable

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  • Data mining can uncover patterns that are difficult for human marketers to see.

    “Existing studies have shown how data mining can be used to uncover patterns that are difficult for human marketers to see.”

    From A strategic framework for artificial intelligence in marketing (2021) · cites this paper as Valls et al. 2018

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Authors
Valls, A., Gibert, K., Orellana, A., & Antón-Clavé, S.
Affiliations
Universitat Politècnica de Catalunya (ES)
Venue
Information & Management
Publisher
Elsevier BV
Published
2018-03
Volume
55
Issue
2
Pages
145-159
ISSN
0378-7206 (ISSN-L)
OpenAlex
W2612757929
Cited by
50
References
140
Citations by year
2026: 3 · 2025: 2 · 2024: 7 · 2023: 15 · 2022: 4 · 2021: 10 · 2020: 4 · 2019: 3 · 2018: 2
FWCI
6.30
Type
journal article
Language
en
Open access
Locations
  • Information & Management (publishedVersion)
  • UPCommons institutional repository (Universitat Politècnica de Catalunya) (acceptedVersion)
  • RECERCAT (Consorci de Serveis Universitaris de Catalunya) (submittedVersion, public-domain)
Funders
English language
Topics
  • Social Sciences › Sociology and Political Science › Diverse Aspects of Tourism Research
  • Social Sciences › Demography › Migration, Aging, and Tourism Studies
  • Physical Sciences › Computer Science › Artificial Intelligence › Semantic Web and Ontologies
Keywords
tourist motivation, push and pull factors, destination choice, ontology-based clustering, tourist segmentation, place meaning, British tourists, Catalonia, qualitative data analysis

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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

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