Marc Christopher Grau
Last Name
Grau
First name
Marc Christopher
Email
marcchristopher.grau@unisg.ch
6 results
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Item type:Publication, Vocalizing User Feedback: The Impact of Input Modality on Self-Disclosure(2025); ; ; Type:journal articleJournal:Proceedings of the ACM on Computer-Human InteractionVolume:9Issue:7DOI:10.1145/3757701Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, The Impact Of Voice Interfaces On Strategic Misreporting During Self-Disclosure(2026-06-15); ; Voice interfaces are increasingly used for large-scale data collection, such as virtual health checkers, where users disclose personal information. Yet, they may shape self-reports differently than text. Heightened social presence and immediacy of the interaction can foster impression management while reducing time for deliberation. We test whether speaking versus typing changes strategic misreporting in a between-subjects experiment (N = 155) where participants disclosed health behaviors via voice or text interfaces. Voice reduced self-enhancing reports on positively valenced items, suggesting potential strategic misreporting in text conditions. For negatively valenced items, differences were not significant, suggesting more careful deliberation in reporting. However, mediation analyses suggest that lower perceived control in voice can indirectly increase reporting of undesirable behaviors, with signs of gender heterogeneity. Guilt as a potential factor did not show signs of mediation. Based on our results, we discuss design implications and theoretical contributions on modality, control, and strategic misreporting.Type:conference paperJournal:European Conference on Information Systems - Some of the metrics are blocked by yourconsent settings
Item type:Publication, HaessigDB: A Database of Irritable Speech with Intensity Grading(2026-09-27); ; Type:conference paperJournal:Interspeech 2026 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Real-Time Guidance Design: Principles for AI-Augmented Proactive Guidance Systems Supporting Human Dialogue in Customer Service(2025-10-11); Live customer-service calls place heavy demands on agents’ limited attentional resources: they must listen, speak, retrieve information, and follow prescribed procedures simultaneously. Yet most AI support tools remain reactive, relying on user-initiated queries and thus amplifying the very cognitive load they are meant to relieve. To address this issue, we examined AI-augmented proactive guidance embedded directly in service agents’ interfaces. Using a design-science research methodology, we carried out three iterative development and evaluation cycles. These cycles yielded three design principles, rooted in theory and practice, and guided our transition from a low-fidelity prototype to a fully instantiated web-application artefact. Findings from controlled evaluations in the form of semi-structured interviews with customer-service practitioners validate our nascent design theory. Alongside the design principles, we provide a transferable system architecture for practical deployment and outline avenues for further evaluation.Type:conference paper - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Rethinking AI Design Practices: A Reference Architecture for Generative AI SystemsGenerative AI (GenAI) holds the potential to transform various facets of society. While research and practice increasingly explore its diverse use cases and assess its impact, many companies continue to face significant challenges in designing and developing advanced GenAI systems, hindering their ability to derive value from these technologies. To address these challenges and offer guidance on emerging GenAI development practices, we propose a reference architecture that highlights key design decisions. Using a design science research approach, we developed the reference architecture informed by consortium workshops, literature reviews, and expert interviews. The reference architecture is grounded in eight key design requirements and serves as a strategic guide to streamline system design and assist organisational decision-making and communication. Additionally, the architecture contributes to the academic discourse by offering a structured framework for future research on the design peculiarities of GenAI systems, helping bridge the gap between practical implementations and theoretical insights.Type:conference paperJournal:European Conference on Information Systems (ECIS) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Artificial Intelligence to Improve Public Budgeting(2024-12-15); ; ; Accurate public budgeting is essential for efficient resource allocation and societal trust. However, recent studies have shown that public budgets often project deficits but have substantial surpluses. This budgetary slack can lead taxpayers to overpay for services not rendered, delay necessary investments, or distort public perceptions of government efficiency. To avoid such unfortunate outcomes, we study how artificial intelligence (AI) can help decision-makers in the public sector with budgeting. We operationalize our research question using a two-step approach. First, we utilize open data from Swiss financial authorities to train and test an AI model. Our preliminary results validate the potential of AI to predict public accounts better than human experts. Second, we study whether decision-makers effectively utilize the AI model in an experimental scenario. The results of our experimental study indicate that human-AI collaboration could indeed support decision-makers to improve public budgeting by reducing budgetary slack.Type:conference paper