Xia, HaiyangMuskat, BirgitKarl, MarionLi, QianLi, Gang2025-06-302025-06-300047-2875WOS:001444679600001ORCID:/0000-0003-2905-6836/work/183184551https://www.webofscience.com/api/gateway?GWVersion=2&SrcApp=anu_research_portal_plus2&SrcAuth=WosAPI&KeyUT=WOS:001444679600001&DestLinkType=FullRecord&DestApp=WOS_CPLhttps://hdl.handle.net/1885/733766020Previous methods for destination competitiveness improvement have mainly focused on identifying and prioritizing competitive disadvantages of destinations. Although effective, this approach may not be optimal as it may require more change than improving combinations of other competitive disadvantages. Furthermore, these methods neglect the differing foci of travel experiences between tourist groups and have been unable to identify targeted competitiveness improvement strategies for different tourist groups. This study addresses these research gaps by developing an analytical framework that can identify targeted strategies that entail minimal changes to improve the competitiveness of destinations for different tourist groups, based on user-generated data, aspect-level sentiment analysis, and the optimization-based causal counterfactual Al algorithm. The application of the framework is demonstrated through a case study involving four destinations in Australia. The proposed analytical framework and findings are valuable in assisting destinations to improve their competitiveness in today's increasingly competitive experiential tourism market.The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by an Australian Government Research Training Program (RTP) Scholarship20en© 2025 The Author(s)Aspect-level sentiment analysiscausal counterfactual AI algorithmDecision analyticsDestination competitiveness improvementUser-generated data202510.1177/00472875251322512105000247583