Mapping the Attitudinal Continuum Toward AI in Tourism: An Explainable Segmentation of a Croatian Consumer Sample

This study examines Croatian consumers’ perceptions and attitudes towards artificial intelligence (AI) applications in tourism and hospitality services through advanced machine learning clustering analysis. Using K-means clustering with shapley additive explanations feature importance analysis on a non-probability online sample of 592 respondents, the analysis found that AI attitudes in this sample form a continuum rather than densely separated groups and partitions that continuum into four interpretable profiles. Emotional response to anticipated AI interaction and age were among the features most important for distinguishing cluster membership in a Random Forest classification model trained to distinguish the four segments, alongside income and education; because these were clustering inputs, and segments formed on attitudes alone do not differ in them, their prominence reflects how the partition was constructed rather than a link between socioeconomic position and AI attitudes. A supplementary validation analysis indicated that segment membership reflects attitudinal variation rather than independently predicting prior AI-related behaviour. The partition yielded four profiles: AI Enthusiasts (21.1%), AI Sceptics (19.9%), Pragmatic Users (33.3%), and Cautious Adopters (25.7%). Convergent evidence from K-means, DBSCAN, Gaussian mixture, and Gower/PAM clustering indicates that the enthusiastic-to-resistant gradient is robust across methods, whereas the placement of individual respondents along it is metric-dependent. The profiles are therefore presented as an instrumental description of attitudinal variation rather than as discrete consumer types and are informative for communication strategy at the aggregate level rather than for individual-level targeting. As the study draws on a non-probability sample, the segments should be understood as characterizing the surveyed respondents rather than the Croatian population as a whole.

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Publication Details

Journal
AI
Published
2026-11-04
DOI
https://doi.org/10.3390/ai7110458
Primary Topic
AI in Service Interactions
Type
article
Field-Weighted Citation Impact
0.00
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article

Mapping the Attitudinal Continuum Toward AI in Tourism: An Explainable Segmentation of a Croatian Consumer Sample

Barbara Pisker, Kristian Đokić, Ariana Ergović
AI
AI in Service Interactions
article

Mapping the Attitudinal Continuum Toward AI in Tourism: An Explainable Segmentation of a Croatian Consumer Sample

Barbara Pisker, Kristian Đokić, Ariana Ergović
article en

Abstract

This study examines Croatian consumers’ perceptions and attitudes towards artificial intelligence (AI) applications in tourism and hospitality services through advanced machine learning clustering analysis. Using K-means clustering with shapley additive explanations feature importance analysis on a non-probability online sample of 592 respondents, the analysis found that AI attitudes in this sample form a continuum rather than densely separated groups and partitions that continuum into four interpretable profiles. Emotional response to anticipated AI interaction and age were among the features most important for distinguishing cluster membership in a Random Forest classification model trained to distinguish the four segments, alongside income and education; because these were clustering inputs, and segments formed on attitudes alone do not differ in them, their prominence reflects how the partition was constructed rather than a link between socioeconomic position and AI attitudes. A supplementary validation analysis indicated that segment membership reflects attitudinal variation rather than independently predicting prior AI-related behaviour. The partition yielded four profiles: AI Enthusiasts (21.1%), AI Sceptics (19.9%), Pragmatic Users (33.3%), and Cautious Adopters (25.7%). Convergent evidence from K-means, DBSCAN, Gaussian mixture, and Gower/PAM clustering indicates that the enthusiastic-to-resistant gradient is robust across methods, whereas the placement of individual respondents along it is metric-dependent. The profiles are therefore presented as an instrumental description of attitudinal variation rather than as discrete consumer types and are informative for communication strategy at the aggregate level rather than for individual-level targeting. As the study draws on a non-probability sample, the segments should be understood as characterizing the surveyed respondents rather than the Croatian population as a whole.

AIVol. 7(11)
University of Osijek (HR)
Openalex Percentile: Top 17%
AI in Service Interactions
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