A Fuzzy Dynamic Model of Destination Image Evaluation in Relation to Cultural Tourism

The main goal of this research is to develop a fuzzy dynamic model for assessing the level of image of a destination for cultural tourism on the basis of an intellectual analysis of knowledge, the experience of participants in the tourist movement, and the ability to forecast cultural tourism data, using examples of the countries of the Visegrad Group (Czech Republic, Hungary, Poland, Slovakia). To formalize a fuzzy dynamic model, the mathematical apparatus of expert evaluation and intellectual analysis of knowledge, theory of fuzzy sets and fuzzy logic, linear regression, and multicriteria evaluation of alternatives are used. For the first time, a multicriteria two-stage model for evaluating the image of a destination for cultural tourism has been developed. At the first level, cultural tourism data are predicted; at the second level, the problem of multicriteria selection of the destination (region) is solved, considering the wishes of the decision maker regarding cultural aspects related to tourism. For the first time, an information model of destination evaluation and choice in the context of cultural tourism has been developed, which reveals the uncertainty of incoming expert judgments regarding the expected and real experience, impressions, and satisfaction with the destination of cultural tourism, moving from the opinions of the individual to the group opinion, which is represented by a quantitative normalized assessment. A fuzzy dynamic model for evaluating the image of a destination for cultural tourism was verified and tested on real data from 2,343 respondents in the countries of the Visegrad Group. The proposed model offers a flexible analytical framework that captures both subjective tourist perceptions and objective temporal trends, which significantly increases the accuracy of destination image assessment. The results may serve as a practical tool for policymakers and tourism managers seeking to improve strategic planning, enhance regional competitiveness, and strengthen cultural tourism development. Future studies should apply advanced predictive methods, extend the model to other tourism sectors and geographic contexts, and incorporate digital behavioural data (e.g., online reviews and mobility records). Longitudinal data collection would further improve the dynamic tracking of the destination image.