Andreas Janson
Title
Dr.
Last Name
Janson
First name
Andreas
Email
andreas.janson@unisg.ch
Phone
+41 71 224 2592
Twitter
https://twitter.com/janson_andreas
100 results
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Item type:Publication, The Anatomy Of A Design Challenge For Initiating AI Innovation(2026-06-15); ; Artificial Intelligence (AI) unlocks value-creation opportunities, but its complexity makes it a challenging design material, demanding well-prepared conditions for successful innovation. One reason organisations struggle to materialize AI's potential is initiating AI innovation, which relies on interdisciplinary teams, internal data integration, incremental development, and context information. The Design Challenge, i.e., problem formulation, serves as key artefact guiding AI innovation, but its conceptualisation remains undertheorized leading to a lack of theoretical foundation and practical guidance. Our study conceptualises the Design Challenge as a boundary object enabling interdisciplinary collaboration and serving as a reference point. We performed a qualitative cross-cases analysis of 42 design challenges to surface seven key elements being verb, object of innovation, internal data, technology, target group, context and potential, generating the Design Challenge's anatomy. This advances theoretical understanding of AI innovation and provides a conceptual schema to craft design challenges enhancing the likelihood of impactful AI innovations.Type:conference paperJournal:European Conference on Information Systems (ECIS) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, When Trust Breaks: A Framework For Analyzing And Understanding Trust Violation And Repair In Sociotechnical Systems(2026-06-15); Trust violations in sociotechnical systems are becoming increasingly visible as AI systems take on advisory and decision-making roles in everyday life. When these systems fail, users must interpret what went wrong and decide whether to continue relying on them. Yet IS research has largely focused on trust formation, leaving the processes through which trust is broken, and potentially repaired, undertheorized. Meanwhile, adjacent fields have produced fragmented insights into trust violations and the mechanisms used to repair them. This paper presents a systematic literature synthesis of 62 studies to understand how trust violations and repair are conceptualized when technology plays a meaningful role. We identify four core stages that structure trust breakdown and recovery: the violation incident, the trustor sensemaking, the violator reaction, and the recalibrated trust outcome. We consolidate these insights into a process-oriented framework and guiding questions, outlining implications for human-AI collaboration and future IS research.Type:conference paperJournal:European Conference on Information Systems (ECIS) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Mining Hidden Prompt Engineering Patterns with Formal Concept Analysis and Association Rules(2026-01-09); Knoth, NilsDesigning effective prompts to guide generative artificial intelligence (GAI) systems, or prompt engineering, has become a crucial skill. However, the underlying prompt patterns have not yet been thoroughly examined. This paper introduces a novel analytical method that combines formal concept analysis (FCA) and association rule mining. This approach is used to systematically analyze prompt engineering behaviors within an empirical dataset of human-AI interactions. Findings reveal hidden prompt patterns linking prompts to GAI outputs, providing insights that traditional analyses cannot offer. Furthermore, we demonstrate that prompting guides, especially those with examples, facilitate more sophisticated prompt engineering behavior and improve GAI output quality. Our work contributes to information systems theory by demonstrating the value of FCA-based structural analysis in human-GAI contexts and to the practice of prompt engineering by offering evidence-based guidance on improving prompt design and prompt engineering skill development.Type:conference paperJournal:Proceedings of the 59th Hawaii International Conference on System Sciences - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Less is More or More is Better? Comparing Static and Generative AI-Based Recovery for Chatbot Service Breakdowns(2025-12-14); Tan, Chee-WeeConversational breakdowns in chatbot-based customer service interactions can impair service quality and brand perception. Our study investigates how static versus generative AI (GAI)-based recovery affects user experience in such scenarios. Using a dual-study design, Study 1 examines two static strategies: Inform (explanation-focused) and Repair (action-focused). We find that Repair significantly enhances recovery quality and service perception, while Inform offers limited benefits when used alone. Study 2 explores the potential of GAI-driven recovery, employing a chatbot capable of nuanced, interactive support. Preliminary findings suggest that GAI-based recovery may increase resolution success, particularly in complex interactions, but could demand higher cognitive effort from users. By comparing both approaches and introducing a sequencing approach, our study provides novel insights into how recovery design and GAI influence service outcomes. Our results inform the design of future chatbot service systems, suggesting that adaptive, generative recovery may complement traditional static recovery strategies under specific conditions.Type:conference paperJournal:International Conference on Information Systems (ICIS) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An Explorative Diary Study of AI-Generated Podcasts in University Education: Benefits, Challenges, and Future Directions(2025-04-26); In this study, we explore the potential of AI-generated podcasts as an educational tool in the evolving landscape of learning media. Podcasts have grown increasingly relevant in education due to their accessibility and ability to integrate learning into everyday life. With the advent of generative artificial Intelligence (AI), there is a unique opportunity for scalable and adaptable creation of learning media. However, with novel technology, there also come new challenges. Thus, we developed fine-tuned AI-generated podcasts using Google NotebookLM, our course materials, and a custom prompt. We conducted a one-month explorative evaluation in the field using a qualitative diary study. Our study reveals that students find the podcasts beneficial for flexible everyday learning but also point toward challenges like a lack of emotional engagement and technical non-English language issues. In sum, our study highlights the current benefits and challenges of AI-generated podcasts and presents an agenda for future research.Type:conference paperJournal:Extended Abstracts of the CHI Conference on Human Factors in Computing Systems (CHI EA ’25)Scopus© Citations 7 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, AI Meets Management: Redefining Leadership and Managerial Roles in the New Era(2025-07-25) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Designing for Trust: Integrating Self-referencing in Large Language Model-Based Health CoachingThis research applies the Design Science Research (DSR) methodology to investigate how self-referencing in Large Language Model (LLM)-based health coaching influences user trust and perceptions of anthropomorphism. We synthesized theory-driven design principles to guide the integration of self-referencing and demonstrated them in a vignette-based prototype. Through a single-factorial between-subjects experiment, analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) and qualitative feedback, we identified a dual effect of self-referencing: while professional self-referencing enhances trust via increased anthropomorphism, overly personal references can directly undermine trust. Based on these findings, we refined our design principles to optimize trust-building in LLM-based coaching. Our contributions provide actionable design guidelines for creating more effective and trustworthy AI-driven health interventions, advancing the understanding of anthropomorphic design in digital coaching contexts.Type:conference paperJournal:Local Solutions for Global Challenges. DESRIST 2025. Lecture Notes in Computer ScienceVolume:Vol 15703Scopus© Citations 1 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Personalized Coaching For Lifestyle Behavior Change through Large Language Models: A Qualitative Study(2025-01-10); This paper investigates the impact of large language model (LLM)-based coaching interventions on enhancing physical activity and nutritional habits, following motivational interviewing guidelines. By exploring user perceptions through qualitative research involving eight interviews, five key themes emerged: the tension between the need for authenticity and reservations about AI humanization, the desire for personalized coaching and autonomy, the necessity of simplifying daily tasks, the aspiration for self-development, and the need for perceived privacy and trust. The findings reveal that perceptions of LLM-based coaching are multifaceted and cannot be easily classified as purely beneficial or concerning; they vary based on the specific implementation. This complexity indicates that certain aspects can simultaneously present both benefits and risks. The paper discusses theoretical implications and offers practical recommendations to enhance the advantages and mitigate the risks associated with LLM-based coaching interventions.Type:conference paperJournal:Hawaii International Conference on System Sciences (HICSS) - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Leveraging Learner Errors in Digital Argumentation Learning: How ALure Helps Students Learn from their Mistakes and Write Better Arguments(2025)Scopus© Citations 5 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Understanding AI’s impact on business value creation.(2025); ; Schäfer, BjörnType:conference paper