Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 434

The DOI matches its Crossref record; 114 of the 114 references it cites were found in a registry or an index.

Business intelligence and big data in hospitality and tourism: a systematic literature review

Mariani, Marcello, Baggio, Rodolfo, Fuchs, Matthias, Höepken, Wolfram·2018·International Journal of Contemporary Hospitality Management

Abstract

Purpose: This paper aims to examine the extent to which Business Intelligence and Big Data feature within academic research in hospitality and tourism published until 2016, by identifying research gaps and future developments and designing an agenda for future research. Design/methodology/approach: The study consists of a systematic quantitative literature review of academic articles indexed on the Scopus and Web of Science databases. The articles were reviewed based on the following features: research topic; conceptual and theoretical characterization; sources of data; type of data and size; data collection methods; data analysis techniques; and data reporting and visualization. Findings: Findings indicate an increase in hospitality and tourism management literature applying analytical techniques to large quantities of data. However, this research field is fairly fragmented in scope and limited in methodologies and displays several gaps. A conceptual framework that helps to identify critical business problems and links the domains of business intelligence and big data to tourism and hospitality management and development is missing. Moreover, epistemological dilemmas and consequences for theory development of big data-driven knowledge are still a terra incognita. Last, despite calls for more integration of management and data science, cross-disciplinary collaborations with computer and data scientists are rather episodic and related to specific types of work and research. Research limitations/implications: This work is based on academic articles published before 2017; hence, scientific outputs published after the moment of writing have not been included. A rich research agenda is designed. Originality/value: This study contributes to explore in depth and systematically to what extent hospitality and tourism scholars are aware of and working intendedly on business intelligence and big data. To the best of the authors’ knowledge, it is the first systematic literature review within hospitality and tourism research dealing with business intelligence and big data.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1108/ijchm-07-2017-0461

Cites

114 of 114 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 114 references

Cited by

See all 434

Metadata

Authors
Mariani, M., Baggio, R., Fuchs, M., & Höepken, W.
Venue
International Journal of Contemporary Hospitality Management
Publisher
Emerald
Published
2018-10-31
Volume
30
Issue
12
Pages
3514-3554
ISSN
0959-6119 (ISSN-L)
OpenAlex
W2799570094
Cited by
434
References
114
FWCI
14.80
Type
journal article
Language
en
Open access
closed

About these checks

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.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Business intelligence and big data in hospitality and tourism: a systematic literature review. https://app.cochat.ai/papers/10.1108/ijchm-07-2017-0461
The paper
Mariani, M., Baggio, R., Fuchs, M., & Höepken, W. (2018). Business intelligence and big data in hospitality and tourism: a systematic literature review. International Journal of Contemporary Hospitality Management. https://doi.org/10.1108/ijchm-07-2017-0461

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.