In the current digital era, as global industries transform due to technological advancements, tracking trends in emerging services has assumed increased significance. This study proposes an innovative model that integrates circular picture fuzzy sets (CPFSs) with the Weighted Aggregated Sum Product Assessment (WASPAS) method to evaluate and rank various AI-driven trends within the service industry. The CPFS approach offers enhanced responses to uncertainty, symmetric information, indecision, and varying expert opinions, while the WASPAS method ensures a dependable system for ranking prominent trends. To facilitate the evaluation process, experts and relevant studies were consulted to establish criteria that address technological developments, organizational dynamics, and market fluctuations. A hybrid fuzzy Multi-Criteria Decision-Making (MCDM) framework enabled the analysis of several potential innovations related to AI and their prioritization in the context of digitalized sectors, including healthcare, finance, online shopping, retail, and logistics. This framework is a well-structured and flexible tool for professionals and policymakers seeking to navigate the challenges of identifying new trends within unpredictable digital environments. The findings indicate that the circular picture fuzzy WASPAS approach significantly enhances trend prioritization and fosters strategic thinking in digital innovation. Furthermore, the research provides valuable insights into the complexities of fuzzy decision-making and the promotion of AI-based innovation management.

Cycling travel vlogs-first-person video diaries of long-distance bicycle journeys-are emerging as influential content for sustainable tourism communication, yet how their characteristics shape users' low-carbon travel intention through dual psychological pathways remains unexplored. Grounded in the Stimulus-Organism-Response (S-O-R) framework, this study examines how three vlog characteristics (content dissemination, interactivity, and entertainment) influence low-carbon travel intention via destination image and environmental self-efficacy as dual mediators. Analyzing data from 407 Chinese cycling vlog users using Partial Least Squares Structural Equation Modeling (PLS-SEM), we obtain three key findings. First, content dissemination and interactivity symmetrically affect both mediators, while entertainment asymmetrically affects only destination image. Second, both mediators independently transmit vlog effects with comparable magnitudes. Third, a sequential chain emerges: destination image enhances self-efficacy, which in turn drives intention. These findings challenge the destination-centric tourism marketing paradigm, demonstrating that promoting low-carbon tourism requires simultaneously enhancing destination attractiveness and building capability beliefs-offering new theoretical and practical pathways for digital media-driven sustainable tourism.

The increasing demand for tourism highlights the significance of itinerary planning as a key problem in path optimization. Although population-based metaheuristic algorithms have shown promising performance on this problem, they typically start from scratch without prior knowledge and rely on the optimization of a predefined objective function. This approach can be inefficient and may overlook popular points of interest (POIs), depending on the problem formulation. In this paper, we propose a Large Language Model (LLM)-augmented metaheuristic optimization framework to more effectively address the itinerary planning problem. The proposed method first leverages the extensive internal knowledge of an LLM to intelligently select tourist attractions based on user preferences. These selections are then integrated into a discrete Particle Swarm Optimization (PSO) algorithm to optimize the travel itinerary. During the optimization process, if the generated plans deviate from realistic constraints, the LLM dynamically adjusts the selection of POIs. Finally, the LLM integrates the optimized path and time allocation to generate a comprehensive itinerary. A case study conducted in Nanjing, Jiangsu Province, China, compares the proposed approach with a conventional PSO method and an LLM-only planning strategy. Experimental results demonstrate the superiority and practical value of the proposed LLM-augmented optimization framework.