Data science for hospitality and tourism

Paulo Rita, Nicole Rita, Cristina Oliveira

Research output: Contribution to journalReview article

Abstract

Purpose: This paper aims to embrace the challenge of performing a state-of-the-art scientific literature analysis in data science for hospitality and tourism. This is important because relatively little contemporary analysis has been published. Design/methodology/approach: Data on over 800 publications were collected from the Scopus database and analyzed by the differing types of publications, evolution of publications across time, top publishers and outlets, publications per area and per topic, top keywords used, most cited papers and most productive authors. Findings: Conclusions are drawn and some suggestions are offered regarding topics that are likely to provide opportunities for future research. Originality/value: This paper identifies the need for analysis on state-of-the-art academic research published to-date on the application of methods and techniques relating to data science in hospitality and tourism.

Original languageEnglish
Pages (from-to)717-725
Number of pages9
JournalWorldwide Hospitality and Tourism Themes
Volume10
Issue number6
DOIs
Publication statusPublished - 3 Dec 2018

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tourism
Tourism
science
academic research
technical literature
methodology
analysis
Tourism and hospitality
Values
state of the art
need
method
Design methodology
Academic research
Key words
Data base

Keywords

  • Data science
  • Hospitality and tourism
  • Literature analysis

Cite this

Rita, Paulo ; Rita, Nicole ; Oliveira, Cristina. / Data science for hospitality and tourism. In: Worldwide Hospitality and Tourism Themes. 2018 ; Vol. 10, No. 6. pp. 717-725.
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Data science for hospitality and tourism. / Rita, Paulo; Rita, Nicole; Oliveira, Cristina.

In: Worldwide Hospitality and Tourism Themes, Vol. 10, No. 6, 03.12.2018, p. 717-725.

Research output: Contribution to journalReview article

TY - JOUR

T1 - Data science for hospitality and tourism

AU - Rita, Paulo

AU - Rita, Nicole

AU - Oliveira, Cristina

N1 - Rita, P., Rita, N., & Oliveira, C. (2018). Data science for hospitality and tourism. Worldwide Hospitality and Tourism Themes, 10(6), 717-725. DOI: 10.1108/WHATT-07-2018-0050

PY - 2018/12/3

Y1 - 2018/12/3

N2 - Purpose: This paper aims to embrace the challenge of performing a state-of-the-art scientific literature analysis in data science for hospitality and tourism. This is important because relatively little contemporary analysis has been published. Design/methodology/approach: Data on over 800 publications were collected from the Scopus database and analyzed by the differing types of publications, evolution of publications across time, top publishers and outlets, publications per area and per topic, top keywords used, most cited papers and most productive authors. Findings: Conclusions are drawn and some suggestions are offered regarding topics that are likely to provide opportunities for future research. Originality/value: This paper identifies the need for analysis on state-of-the-art academic research published to-date on the application of methods and techniques relating to data science in hospitality and tourism.

AB - Purpose: This paper aims to embrace the challenge of performing a state-of-the-art scientific literature analysis in data science for hospitality and tourism. This is important because relatively little contemporary analysis has been published. Design/methodology/approach: Data on over 800 publications were collected from the Scopus database and analyzed by the differing types of publications, evolution of publications across time, top publishers and outlets, publications per area and per topic, top keywords used, most cited papers and most productive authors. Findings: Conclusions are drawn and some suggestions are offered regarding topics that are likely to provide opportunities for future research. Originality/value: This paper identifies the need for analysis on state-of-the-art academic research published to-date on the application of methods and techniques relating to data science in hospitality and tourism.

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