Topic modeling in hospitality and tourism research: Application areas, business insights, and managerial implications

Purpose - Topic modeling (TM) explores customer experience and behaviors from large volumes of textual data, such as online reviews uncovering (dis)satisfaction cues often overlooked by hospitality managers. Despite its potential, TM application in hospitality research is limited compared to other social science methods. This paper aims to investigate the scope of TM research in the hospitality domain and contribute to the understanding of the areas where it can be effectively applied, the purposes it can serve, and the types of problems it can address. Methodology - The research methodology is rooted in the systematic literature review - 40 relevant papers were collected and analysed to identify the areas of hospitality where TM is mostly applied, business insights derived from TM application, and commonly utilised TM approaches. Findings -TM research in hospitality is conducted in five research areas: accommodation and lodging, food and beverages, attractions and events, nature-based tourism, and travel services. Researchers apply TM to gain nine different business insights, such as dissatisfaction drivers, segment-based preferences, sentiments, preference changes over time, service quality perception, or underexplored areas. Implications - TM-based research provides actionable recommendations for the enhancement of managerial practices within the hospitality industry, such as promotion and destination management, service improvements, and reduction of overtourism.

Authors

Publication Details

Journal
Menadzment u hotelijerstvu i turizmu
Published
2025-01-01
DOI
https://doi.org/10.5937/menhottur2500010g
Primary Topic
Computational and Text Analysis Methods
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Topic modeling in hospitality and tourism research: Application areas, business insights, and managerial implications

Olivera Grljević
Menadzment u hotelijerstvu i turizmu
Computational and Text Analysis Methods
article

Topic modeling in hospitality and tourism research: Application areas, business insights, and managerial implications

Olivera Grljević
article en

Abstract

Purpose - Topic modeling (TM) explores customer experience and behaviors from large volumes of textual data, such as online reviews uncovering (dis)satisfaction cues often overlooked by hospitality managers. Despite its potential, TM application in hospitality research is limited compared to other social science methods. This paper aims to investigate the scope of TM research in the hospitality domain and contribute to the understanding of the areas where it can be effectively applied, the purposes it can serve, and the types of problems it can address. Methodology - The research methodology is rooted in the systematic literature review - 40 relevant papers were collected and analysed to identify the areas of hospitality where TM is mostly applied, business insights derived from TM application, and commonly utilised TM approaches. Findings -TM research in hospitality is conducted in five research areas: accommodation and lodging, food and beverages, attractions and events, nature-based tourism, and travel services. Researchers apply TM to gain nine different business insights, such as dissatisfaction drivers, segment-based preferences, sentiments, preference changes over time, service quality perception, or underexplored areas. Implications - TM-based research provides actionable recommendations for the enhancement of managerial practices within the hospitality industry, such as promotion and destination management, service improvements, and reduction of overtourism.

Menadzment u hotelijerstvu i turizmuVol. 13(2)
Openalex Percentile: Top 70%
Computational and Text Analysis Methods
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.