A Fermatean Fuzzy Opportunity Losses-Based Polar Coordinate Distance Method for Artificial Intelligence-Enhanced Custom-Made Print Advertisements

The increasing adoption of artificial intelligence (AI) across industries has also extended to the field of advertising, where AI is being utilized in various aspects of advertisement design. AI bots employing text-to-image techniques have emerged as an effective approach for generating customized print advertisements. Drawing on the principles of Social Cognitive Theory, this study conceptualizes AI as a cognitive decision-support tool that enhances marketers’ learning, creative judgment, and advertising decision-making through iterative feedback and systematic evaluation. This study aims to develop a decision support system (DSS) for designing AI-enhanced custom-made print ads and selecting the best advertisement from among the generated alternatives. A methodological approach comprising four phases is employed, focusing on the production of advertisement alternatives and their ranking using a multi-attribute group decision-making approach. As DSS, the Fermatean fuzzy (FF)-Entropy-opportunity losses (OPLO)-based polar coordinate distance (POCOD) hybrid method is proposed and developed in this research. Decision-maker weights are determined using FF sets. Criteria weights are calculated based on expert opinions through the FF-Entropy method. The aggregation of expert decision matrices is conducted using the FF-weighted partitioned dual Maclaurin symmetric mean (FFWPDMSM) aggregation operator. The AI-enhanced custom-made print ads are ranked using the FF-OPLO-POCOD method. The applicability of the proposed hybrid method is demonstrated in a case study involving the design of advertisements for a dairy product aimed at a hedonic customer base. The results identify Ads-4 as the most effective AI-generated advertisement among the twelve alternatives. Sensitivity and comparative analyses confirm the robustness and consistency of the proposed framework, with the top-ranked alternative remaining unchanged in all but one sensitivity scenario, thereby demonstrating the stability and reliability of the decision-making process. The study concludes by presenting theoretical and practical implications for AI-assisted advertising and marketing decision-making.