Abstract
Designers aspire to create engaging and desirable experiences. To that end they study users, aiming to better understand their preferences, ways of thinking and desired outcomes. In the service sector this task is more intricate as experiences encompass the whole customer journey, or the sequence of moments of interaction between customer and company. In services, one poorly designed interaction can severely compromise the overall experience. Despite experience holistic nature, current methods address its components separately, failing to provide an overall systematized picture. This paper presents Customer Experience Modeling, a novel multidisciplinary approach to systematize, represent and evaluate customer experiences to guide service and interaction design efforts. We illustrate this method with an application to a multimedia service provider built upon 17 interviews with service users.

Abstract
Purpose - Customer experience has become increasingly important for service organizations that see it as a source of sustainable competitive advantage, and for service designers, who consider it fundamental to any service design project. Design/methodology/approach - Integrating contributions from different fields, CEM was conceptually developed to represent the different aspects of customer experience in a holistic diagrammatic representation. CEM was further developed with an application to a multimedia service. To further develop and build CEM's models, 17 customers of a multimedia service provider were interviewed and the data were analyzed using Grounded Theory methodology. Findings - Combining multidisciplinary contributions to represent customer experience elements enables the systematization of its complex information. The application to a multimedia service highlights how CEM can facilitate the work of multidisciplinary design teams by providing more insightful inputs to service design. Originality/value - CEM supports the holistic nature of customer experience, providing a systematic portrayal of its context and shifting the focus from single experience elements to their orchestration.

Abstract
Achieving sustainable urban mobility requires shifting travelers toward public transport. However, policies often assume uniform preferences, leaving a critical gap in understanding how different travelers prioritize mobility factors. To address this, the study examines behavioral heterogeneity among urban travelers using a data-driven clustering approach based on the relative importance assigned to cost, comfort, environmental sustainability, and flexibility. Using data from 698 respondents in the Asprela area of Porto, Portugal, a mixed-use district combining universities, hospitals, and commercial facilities, the study applies principal component analysis (PCA) and K-means clustering to derive distinct traveler profiles. Unlike segmentation based solely on socio-demographics or observed mode choice, this approach groups individuals according to their underlying value structures. Six clusters were identified, ranging from car-dependent, comfort-oriented users to environmentally conscious and low-engagement groups. The findings show that one-size-fits-all policies are unlikely to address behavioral diversity effectively. Building on these insights, the study proposes tailored and cross-cutting policies to enhance the attractiveness of public transport and promote sustainability. By uncovering latent preference structures, the study contributes to more inclusive and value-informed mobility planning.