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Organizational maturity and artificial intelligence adoption in Romanian tourism: an empirical typology

Type of paper: Research Article

Authors

Jitka VOLFOVÁ

Corresponding Author

Affiliation: Prague University of Economics and Business, Czechia

Email: jitka.volfova@vse.cz

https://orcid.org/0000-0002-4154-0359

Kamila MATYSOVÁ

Affiliation: Prague University of Economics and Business, Czechia

Email: kamila.matysova@vse.cz

https://orcid.org/0000-0002-2182-5920

Vlad DIACONESCU

Affiliation: Bucharest University of Economic Studies, Romania

Email: diaconescuvlad17@stud.ase.ro

https://orcid.org/0009-0004-7840-5849

Andrada-Jawel ISTANBOULY

Affiliation: ISTANBOULY, Bucharest University of Economic Studies, Romania

Email: istanboulyandrada21@stud.ase.ro

https://orcid.org/0009-0002-4061-3365

Received:May 8, 2026
Revised:May 22, 2026
Accepted:May 22, 2026
Published:May 31, 2026

How to Cite

VOLFOVÁ, J., MATYSOVÁ, K., DIACONESCU, V., & ISTANBOULY, A. J. (2026). Organizational maturity and artificial intelligence adoption in Romanian tourism: an empirical typology. CACTUS - Journal of Tourism Business, Management and Economics, 8 (1), 23-37.

Based on the official APA guide. Review the full set of examples.

© 2026 The Author(s);

Licensed under CC BY-NC 4.0

Abstract

As digital transformation accelerates and artificial intelligence (AI) becomes more accessible, tourism organizations display uneven adoption patterns. This study examines how Romanian tourism organizations cluster based on cultural-strategic profiles and whether objective demographic characteristics are associated with membership in specific maturity profiles. Using survey data from 325 Romanian tourism professionals, the article applies K-means cluster analysis to seven cultural-strategic dimensions: the four archetypes of the Competing Values Framework and the three dimensions of strategic agility. The resulting typology is validated with ANOVA tests on external variables, and associations with organizational demographics are analyzed using chi-square tests and binary logistic regression. The analysis identifies three ordered maturity profiles – Laggards (16.6%), Moderates (36.0%), and Leaders (47.4%)—with medium to large differences across AI adoption, technology-organization-environment (TOE) readiness, digital culture, strategic agility performance, and perceived digital transformation impact. None of the demographic predictors in the logistic model (organization size, geographic scope, respondent experience) significantly predicts Leader membership, and organization type is also non-significant in chi-square analysis. However, geographic scope and organization size have independent effects on actual AI adoption levels. These findings indicate that digital maturity in tourism is closely linked to cultural-strategic capability patterns, while objective resources do not determine profile membership. Given the cross-sectional and self-reported nature of the data, the results should be interpreted as associative rather than causal. The study has implications for policies and managerial interventions that integrate capability-building with measures addressing resource constraints, particularly in SME-dominated tourism contexts.

Keywords

digital maturitytourismdigital transformationstrategic agility

JEL Classification

L83O33M21Z32

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