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Integrating culture into the behavioural models of virtual characters requires knowledge from very different disciplines such as cross-cultural psychology and computer science. If culture-related behavioural differences are simulated with a virtual character system, users might not necessarily understand the intent of the designer. This is, in part, due to the influence of culture on not only users, but also designers. To gain a greater understanding of the instantiation of culture in the behaviour of virtual characters, and on this potential mismatch between designer and user, we have conducted two experiments. In these experiments, we tried to simulate one dimension of culture (Masculinity vs. Femininity) in the behaviour of virtual characters. We created four scenarios in the first experiment and six in the second. In each of these scenarios, the same two characters interact with each other. The verbal and non-verbal behaviour of these characters differs depending on their cultural scripts. In two user perception studies, we investigated how these differences are judged by human participants with different cultural backgrounds. Besides expected differences between participants from Masculine and Feminine countries, we found significant differences in perception between participants from Individualistic and Collectivistic countries. We also found that the user’s interpretation of the character’s motivation had a significant influence on the perception of the scenarios. Based on our findings, we give recommendations for researchers that aim to design culture-specific behaviours for virtual characters.
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The user’s experience with a recommender system is significantly shaped by the dynamics of user-algorithm interactions. These interactions are often evaluated using interaction qualities, such as controllability, trust, and autonomy, to gauge their impact. As part of our effort to systematically categorize these evaluations, we explored the suitability of the interaction qualities framework as proposed by Lenz, Dieffenbach and Hassenzahl. During this examination, we uncovered four challenges within the framework itself, and an additional external challenge. In studies examining the interaction between user control options and interaction qualities, interdependencies between concepts, inconsistent terminology, and the entity perspective (is it a user’s trust or a system’s trustworthiness) often hinder a systematic inventory of the findings. Additionally, our discussion underscored the crucial role of the decision context in evaluating the relation of algorithmic affordances and interaction qualities. We propose dimensions of decision contexts (such as ‘reversibility of the decision’, or ‘time pressure’). They could aid in establishing a systematic three-way relationship between context attributes, attributes of user control mechanisms, and experiential goals, and as such they warrant further research. In sum, while the interaction qualities framework serves as a foundational structure for organizing research on evaluating the impact of algorithmic affordances, challenges related to interdependencies and context-specific influences remain. These challenges necessitate further investigation and subsequent refinement and expansion of the framework.
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Creating and testing the first Brand Segmentation Model in Augmented Reality using Microsoft Hololens. Sanoma together with SAMR launched an online brand segmentation tool based on large scale research, The brand model uses several brand values divided over three axes. However they cannot be displayed clearly in a 2D model. The space of BSR Quality Planner can be seen as a 3-dimensional meaningful space that is defined by the terms used to typify the brands. The third axis concerns a behaviour-based dimension: from ‘quirky behaviour’ to ‘standardadjusted behaviour’ (respectful, tolerant, solidarity). ‘Virtual/augmented reality’ does make it possible to clearly display (and experience) 3D. The Academy for Digital Entertainment (ADE) of Breda University of Applied Sciences has created the BSR Quality Planner in Virtual Reality – as a hologram. It’s the world’s first segmentation model in AR. Breda University of Applied Sciences (professorship Digital Media Concepts) has deployed hologram technology in order to use and demonstrate the planning tool in 3D. The Microsoft HoloLens can be used to experience the model in 3D while the user still sees the actual surroundings (unlike VR, with AR the space in which the user is active remains visible). The HoloLens is wireless, so the user can easily walk around the hologram. The device is operated using finger gestures, eye movements or voice commands. On a computer screen, other people who are present can watch along with the user. Research showed the added value of the AR model.Partners:Sanoma MediaMarketResponse (SAMR)
The developments of digitalization and automation in freight transport and logistics are expected to speed-up the realization of an adaptive, seamless, connected and sustainable logistics system. CATALYST determines the potential and impact of Connected Automated Transport (CAT) by testing and implementing solutions in a real-world environment. We experiment on smart yards and connected corridors, to answer research questions regarding supply chain integration, users, infrastructure, data and policy. Results are translated to overarching lessons on CAT implementations, and shared with potential users and related communities. This way, CATALYST helps logistic partners throughout the supply chain prepare for CAT and accelerates innovation.
The developments of digitalization and automation in freight transport and logistics are expected to speed-up the realization of an adaptive, seamless, connected and sustainable logistics system. CATALYST determines the potential and impact of Connected Automated Transport (CAT) by testing and implementing solutions in a real-world environment. We experiment on smart yards and connected corridors, to answer research questions regarding supply chain integration, users, infrastructure, data and policy. Results are translated to overarching lessons on CAT implementations, and shared with potential users and related communities. This way, CATALYST helps logistic partners throughout the supply chain prepare for CAT and accelerates innovation.
