CACTUS CACTUS J Tour Bus Manag Econ CACTUS – Journal of Tourism Business, Management and Economics 2247-3297 ASE Publishing House Bucharest, Romania cactus-2026-19 Research Article L83 O33 M21 Z32 Organizational maturity and artificial intelligence adoption in Romanian tourism: an empirical typology VOLFOVÁ Jitka https://orcid.org/0000-0002-4154-0359 jitka.volfova@vse.cz MATYSOVÁ Kamila https://orcid.org/0000-0002-2182-5920 DIACONESCU Vlad https://orcid.org/0009-0004-7840-5849 ISTANBOULY Andrada-Jawel https://orcid.org/0009-0002-4061-3365 Prague University of Economics and Business, Czechia Prague University of Economics and Business, Czechia Bucharest University of Economic Studies, Romania ISTANBOULY, Bucharest University of Economic Studies, Romania 31 5 2026 8 1 23 37 852026 2252026 2252026 Copyright © 2026, The Author(s) 2026 The Author(s) This is an open-access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0). https://creativecommons.org/licenses/by-nc/4.0/

<p>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.</p>

 Keywords digital maturity tourism digital transformation strategic agility

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References Arroyabe, M. F., Arranz, C. F. A., Fernandez De Arroyabe, I., &amp; Fernandez de Arroyabe, J. C. (2024). Analyzing AI adoption in European SMEs: A study of digital capabilities, innovation, and external environment. Technology in Society, 79, 102733. https://doi.org/10.1016/j.techsoc.2024.102733 10.1016/j.techsoc.2024.102733 Buhalis, D., Harwood, T., Bogicevic, V., Viglia, G., Beldona, S., &amp; Hofacker, C. (2019). Technological disruptions in services: Lessons from tourism and hospitality. Journal of Service Management, 30(4), 484–506. https://doi.org/10.1108/JOSM-12-2018-0398 10.1108/JOSM-12-2018-0398 Cameron, K. S., &amp; Quinn, R. E. (2011). Diagnosing and changing organizational culture: Based on the competing values framework (3rd ed.). Jossey-Bass. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Lawrence Erlbaum Associates. Diaconescu, V. (2024). Navigating corporate culture: Perspectives from Generation Z business students. CACTUS - Journal of Tourism Business, Management and Economics, 6(1). https://doi.org/10.24818/ CTS/6/2024/1.02 Diaconescu, V., Țigu, G., &amp; Malaia, C. M. (2025). Exploring the cultural foundations of digital innovation in tourism: A conceptual framework. CACTUS - Journal of Tourism Business, Management and Economics, 7(1). https://doi.org/10.24818/CTS/7/2025/1.05 10.24818/CTS/7/2025/1.05 Doz, Y. (2020). Fostering strategic agility: How individual executives and human resource practices contribute. Human Resource Management Review, 30(1), 100693. https://doi.org/10.1016/j.hrmr.2019.100693 10.1016/j.hrmr.2019.100693 Doz, Y. L., &amp; Kosonen, M. (2010). Embedding strategic agility: A leadership agenda for accelerating business model renewal. Long Range Planning, 43(2-3), 370-382. https://doi.org/10.1016/j.lrp.2009.07.006 10.1016/j.lrp.2009.07.006 Emilian, R., Țuclea, C. E., Țală, M. L., &amp; Brîndușoiu, C. N. (2009). Hospetitiveness - The empirical model of competitiveness in Romanian hospitality industry. Amfiteatru Economic, 11(26), 462-472. https://www.amfiteatrueconomic.ro/temp/Article_887.pdf Fiss, P. C. (2011). Building better causal theories: A fuzzy set approach to typologies in organization research. Academy of Management Journal, 54(2), 393-420. https://doi.org/10.5465/amj.2011.60263120 10.5465/amj.2011.60263120 Hartnell, C. A., Ou, A. Y., &amp; Kinicki, A. (2011). Organizational culture and organizational effectiveness: A meta-analytic investigation of the competing values framework’s theoretical suppositions. Journal of Applied Psychology, 96(4), 677-694. https://doi.org/10.1037/a0021987 10.1037/a0021987 Jasim, T. A., Khairy, H. A., Fayyad, S., &amp; Al-Romeedy, B. S. (2024). Digital leadership and creative performance in tourism and hotel enterprises: Leveraging strategic agility and organizational learning culture. GeoJournal of Tourism and Geosites, 54(2, Special Issue), 872-884. https://doi.org/10.30892/ gtg.542spl11-1262 Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39(1), 31-36. https://doi.org/10.1007/ BF02291575 Kane, G. C., Palmer, D., Phillips, A. N., Kiron, D., &amp; Buckley, N. (2017). Achieving digital maturity: Adapting your company to a changing world [Research report]. MIT Sloan Management Review and Deloitte University Press. https://sloanreview.mit.edu/projects/achieving-digital-maturity/ Li, H., Xi, J., Hsu, C. H. C., Yu, B. X. B., &amp; Zheng, X. (2025). Generative artificial intelligence in tourism management: An integrative review and roadmap for future research. Tourism Management, 110, 105179. https://doi.org/10.1016/j.tourman.2025.105179 10.1016/j.tourman.2025.105179 Liao, J., Wu, M., Du, P., Filieri, R., &amp; He, K. (2025). The past, present, and future of AI in hospitality and tourism: A bibliometric analysis. International Journal of Contemporary Hospitality Management, 37(7), 2287-2305. https://doi.org/10.1108/IJCHM-07-2024-1104 10.1108/IJCHM-07-2024-1104 National Institute of Statistics. (2023). Romanian statistical yearbook 2023 [Anuarul Statistic al României 2023]. Institutul Național de Statistică. https://insse.ro/cms/en/content/romanian-statistical-yearbook Nunnally, J. C., &amp; Bernstein, I. H. (1994). Psychometric theory (3rd ed.). McGraw-Hill. OECD. (2024). Artificial intelligence and tourism: G7/OECD policy paper (OECD Tourism Papers, No. 2024/02). OECD Publishing. https://doi.org/10.1787/3f9a4d8d-en 10.1787/3f9a4d8d-en Oliveira, T., &amp; Martins, M. F. (2011). Literature review of information technology adoption models at firm level. Electronic Journal of Information Systems Evaluation, 14(1), 110-121. https://academic-publishing.org/index.php/ejise/article/view/389/352 Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology &amp; Tourism, 22(3), 455-476. https://doi.org/10.1007/s40558-019-00160-3 10.1007/s40558-019-00160-3 Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., &amp; Podsakoff, N. P. (2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879-903. https://doi.org/10.1037/0021-9010.88.5.879 10.1037/0021-9010.88.5.879 Quinn, R. E., &amp; Rohrbaugh, J. (1983). A spatial model of effectiveness criteria: Towards a competing values approach to organizational analysis. Management Science, 29(3), 363-377. https://doi.org/10.1287/mnsc.29.3.363 10.1287/mnsc.29.3.363 Shin, H., Ryu, J., &amp; Jo, Y. (2025). Navigating artificial intelligence adoption in hospitality and tourism: Managerial insights, workforce transformation, and a future research agenda. International Journal of Hospitality Management, 128, 104187. https://doi.org/10.1016/j.ijhm.2025.104187 10.1016/j.ijhm.2025.104187 Sigala, M. (2020). Tourism and COVID-19: Impacts and implications for advancing and resetting industry and research. Journal of Business Research, 117, 312-321. https://doi.org/10.1016/j.jbusres.2020.06.015 10.1016/j.jbusres.2020.06.015 Succurro, M. (2026). Digital transformation in tourism SMEs: A strategic imperative in the evolving business landscape. Journal of Industrial and Business Economics. Advance online publication. https://doi.org/10.1007/s40812-026-00395-2 10.1007/s40812-026-00395-2 Tornatzky, L. G., &amp; Fleischer, M. (1990). The processes of technological innovation. Lexington Books. Tsaih, R.-H., &amp; Hsu, C. C. (2018). Artificial intelligence in smart tourism: A conceptual framework. In Proceedings of the 18th International Conference on Electronic Business (pp. 124-133). https://iceb.johogo.com/proceedings/2018/ICEB2018_paper_84_full.pdf Țuclea, C.-E., &amp; Poenaru, L.-F. (2026). Generation Z employees’ acceptance and AI use intensity: A moderated mediation model of psychological safety, technostress, and trust. Merits, 6(1), Article 7. https://doi.org/10.3390/merits6010007 10.3390/merits6010007 Westerman, G., Bonnet, D., &amp; McAfee, A. (2014). Leading digital: Turning technology into business transformation. Harvard Business Review Press.