What motivates chatbot use among tourists? A mixed-methods comparison of expert and user opinion

The aim of this study is to identify the most suitable type of chatbot for use in the hotel industry: task-oriented vs. hedonic. To fulfil this aim, the motivations that are likely to generate more widespread use of chatbots in tourism services are analysed from the dual viewpoint of potential users and chatbot experts, to yield a complete picture of the preferences of the different agents in the sector. The former public helps identify those motivations that relate to current chatbot use, while the latter also enables motivations relating to possible future use to be anticipated. First, in-depth interviews are conducted with (i) potential users of hotel chatbots and (ii) chatbot experts (both academic and professional) to validate a series of motivations for chatbot use. An fs/QCA analysis is then conducted to identify the necessary conditions that a chatbot must fulfil, from the perspective of 29 potential users and 21 experts. The results suggest that there is a clear preference for task-oriented chatbots among users (short-term perspective), while the experts adopt a longer-term outlook focusing more on the entertainment and novelty value that chatbots can deliver. From the results, it can be concluded that, while experts regard many of the recurring problems of chatbots in tourism as having been overcome, tourists still demand that chatbots meet requirements such as productivity and convenience, pointing to areas that require further attention from the industry.

Authors

Institutions

Publication Details

Journal
European Journal of Tourism Research
Published
2025-06-07
DOI
https://doi.org/10.54055/ejtr.v40i.3904
Citations
1
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
2.48

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

What motivates chatbot use among tourists? A mixed-methods comparison of expert and user opinion

Juan Miguel Rey Pino, Carmen M. Sabiote-Ortíz, José Alberto Castañeda García, Julio Vena-Oya
1 citations
European Journal of Tourism Research
AI in Service Interactions
2.48
article

What motivates chatbot use among tourists? A mixed-methods comparison of expert and user opinion

Juan Miguel Rey Pino, Carmen M. Sabiote-Ortíz, José Alberto Castañeda García, Julio Vena-Oya
article en
1 citations

Abstract

The aim of this study is to identify the most suitable type of chatbot for use in the hotel industry: task-oriented vs. hedonic. To fulfil this aim, the motivations that are likely to generate more widespread use of chatbots in tourism services are analysed from the dual viewpoint of potential users and chatbot experts, to yield a complete picture of the preferences of the different agents in the sector. The former public helps identify those motivations that relate to current chatbot use, while the latter also enables motivations relating to possible future use to be anticipated. First, in-depth interviews are conducted with (i) potential users of hotel chatbots and (ii) chatbot experts (both academic and professional) to validate a series of motivations for chatbot use. An fs/QCA analysis is then conducted to identify the necessary conditions that a chatbot must fulfil, from the perspective of 29 potential users and 21 experts. The results suggest that there is a clear preference for task-oriented chatbots among users (short-term perspective), while the experts adopt a longer-term outlook focusing more on the entertainment and novelty value that chatbots can deliver. From the results, it can be concluded that, while experts regard many of the recurring problems of chatbots in tourism as having been overcome, tourists still demand that chatbots meet requirements such as productivity and convenience, pointing to areas that require further attention from the industry.

European Journal of Tourism ResearchVol. 40
Universidad de Granada (ES), Universidad de Jaén (ES)
Ministerio de Ciencia, Innovación y Universidades, European Commission, Agencia Estatal de Investigación
Openalex Percentile: Top 10%
AI in Service Interactions
2.48
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.