AISAQUAL-Based Evaluation of AI-Supported E-Tourism Service Quality: An Interval-Valued q-Rung Orthopair Fuzzy Hamacher CIMAS Approach

With the increasing prevalence of AI-supported services, the assessment of service quality in the e-tourism sector has become a more complex and multidimensional process. This study aims to analyse the dimensions of AI-supported e-tourism service quality using an advanced fuzzy multi-criteria decision-making approach that takes into account uncertainty and hesitation in expert judgements. Within this scope, the relative importance levels of the criteria associated with the six fundamental dimensions defined within the AISAQUAL model were modelled using interval-valued q-rung orthopair fuzzy sets (IVq-ROFS), and a flexible and parametric integration process was applied via Hamacher operators. The analytical framework was structured using the CIMAS method, which directly reflects expert experience in the criterion weighting process; a multi-stage evaluation process was conducted by integrating decision-makers’ levels of experience into the weighting mechanism. In this process, linguistic evaluations were converted into fuzzy numbers, combined using the Hamacher product operator, and reduced to precise values via scoring functions to calculate criterion weights. The findings indicate that incorporating uncertainty and interactions between criteria into the model leads to variations in the relative importance ranking of service quality dimensions. Furthermore, it was determined that the proposed approach produces more consistent and discriminatory results compared to classical weighting methods. In conclusion, the study demonstrates that the use of advanced fuzzy decision-making methods in the evaluation of AI-supported service quality can yield more realistic and reliable results.