Roman Rietsche
Title
Dr.
Last Name
Rietsche
First name
Roman
Email
roman.rietsche@unisg.ch
Phone
+41 71 224 33 25
37 results
Now showing 1 - 10 of 37
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Item type:Publication, How Siemens Empowered Workforce Re- and Upskilling Through Digital Learning(2025-09); ; Gunter BeitingerThe accelerating digital transformation of manufacturing is enhancing automation and production efficiency while requiring employees to develop new skills to meet evolving demands. This case study examines how Siemens embedded a human-centric, bottom-up approach to empower employee re- and upskilling through innovative digital learning. Aligned with Siemens’s actions, we present a four-phase model on leveraging information systems to address skill gaps, enhance adaptability and tackle re- and upskilling challenges. We also provide five recommendations to help organizations foster lifelong learning in dynamic manufacturing environments.Type:journal articleJournal:MIS Quarterly ExecutiveVolume:24Issue:3 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How a Swiss luxury retailer implements process mining to improve data-driven customer excellence(2024); ; ; In today’s digital transformation era, process mining has emerged as a crucial technology, playing an integral part in the digital strategies of many organizations. Despite its significance, implementing process mining to leverage data-driven decision-making and boosting process efficiency presents notable challenges for such companies. This case study delves into the journey of the fictitious Swiss luxury retailer Elysian as they utilize process mining to derive data-driven insights on process inefficiencies and bottlenecks to increase their customer excellence for online retail procurement. The case highlights the capabilities of process mining for organizations. It is among the first to offer students hands-on guidance on process discovery, conformance, and enhancement using real-world data. Students take the role of Lisa Dister, Head of procurement in the business unit home care, who urgently requires improving process transparency after an unsatisfying internal audit result. This immersive experience helps students understand the application of process mining in high-volume data scenarios and equips them with skills in data literacy. Moreover, students are challenged to suggest recommendations for long-term process optimization and reflect on the effectiveness of process mining for tackling procurement issues.Type:journal articleJournal:Journal of Information Technology Teaching Cases - Some of the metrics are blocked by yourconsent settings
Item type:Publication, How to Support Students’ Self-Regulated Learning in Times of Crisis: An Embedded Technology-Based Intervention in Blended Learning Pedagogies(2023-09-22); ; With the increasing prevalence of technology-enhanced learning environments, self-regulated learning (SRL) has become a crucial skill for management students and graduates in the 21st century. Self-regulated learners can take control of their own learning process by setting learning objectives and selecting appropriate learning strategies. As a result of the recent COVID-19 crisis, universities were compelled to shift to online course delivery, which greatly reduced social interaction between educators and learners and challenged educators’ feedback practices. To address this issue, we developed and embedded a technology-based intervention with temporal-proximate and regular formative feedback assessments in a large-scale management course to promote graduate students’ SRL practices. We evaluated the intervention in a quasi-experiment, which found that students with the embedded SRL intervention had higher self-assessment and learning outcome scores and lower absolute self-assessment deviation. Our study makes at least three contributions. First, we shed light on students’ SRL strategies in times of emergency remote learning, highlighting their extensive need for social support and comparison. Second, we extend the literature on SRL and social-cognitive theory by unveiling a hidden effect when embedding temporal-proximate and regular interventions. Third, we contribute an empirically evaluated intervention to foster students’ SRL in blended learning and online pedagogies.Type:journal articleJournal:Academy of Management Learning & Education (AMLE)Volume:22Issue:3Scopus© Citations 22 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Quantum computing(2022-08-05); ; Scopus© Citations 149 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Individualisierung in der beruflichen Bildung durch Hybrid Intelligence. Potentiale und Grenzen(Franz Steiner Verlag, 2021); ; ; - Some of the metrics are blocked by yourconsent settings
Item type:Publication, A pedagogical perspective on Big Data: A conceptual model for digital learning support. Technology, Knowledge and Learning.(Universität St. Gallen, 2019); ; ; Type:journal articleScopus© Citations 40 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Towards Designing an Adaptive Argumentation Learning Tool(Proceedings of the International Conference on Information Systems (ICIS) 2019, 2019-12); Digitalization triggers a shift in the compositions of skills and knowledge needed for students in their future work life. Hence, higher order thinking skills are becoming more important to solve future challenges. One subclass of these skills, which contributes significantly to communication, collaboration and problem-solving, is the skill of how to argue in a structured, reflective and well-formed way. However, educational organizations face difficulties in providing the boundary conditions necessary to develop this skill, due to increasing student numbers paired with financial constraints. In this short paper, we present the first steps of our design science research project on how to design an adaptive IT-tool that helps students develop their argumentation skill through formative feedback in large-scale lectures. Based on scientific learning theory and user interviews, we propose preliminary requirements and design principles for an adaptive argumentation learning tool. Furthermore, we present a first instantiation of those principles.Type:journal article - Some of the metrics are blocked by yourconsent settings
Item type:Publication, DEVELOPING A HYBRID VECTOR-GRAPH RETRIEVAL SYSTEM FOR ENTITY-PRESERVING AND INSPIRING STORYLINE CREATION OF PRESENTATION SLIDES(2025-06-12); ; Effective presentation slide creation is crucial for impactful communication, yet fully automating this task with AI is insufficient. Hybrid human-AI solutions often perform worse than pure AI or human creation due to overreliance on AI. To address this, we develop design principles for configuring human-AI hybrid systems in complex knowledge tasks using a design science research approach. Our prototype, NarrativeNet Weaver, leverages an underutilized corpus of existing presentation slides, applying generative AI advances in hybrid dense embedding and graph-based retrieval techniques. Evaluated through 15 think-aloud sessions and 73 user trials, users with NarrativeNet Weaver exhibit greater engagement and achieve equal or improved slide quality compared to those using a ChatGPT-based chatbot with a vector database. We contribute design knowledge for human-AI systems for complex multimodal content and offer a new approach to retrieving and visualizing existing slides, enhancing the utilization of valuable but underused resources.Type:conference paperJournal:European Conference on Information Systems (ECIS)Volume:2025 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, Efficient Management of LLM-Based Coaching Agents' Reasoning While Maintaining Interaction Quality and Speed(2025); ; Ungar, LyleLLM-based agents improve upon standalone LLMs, which are optimized for immediate intent-satisfaction, by allowing the pursuit of more extended objectives, such as helping users over the long term. To do so, LLM-based agents need to reason before responding. For complex tasks like personalized coaching, this reasoning can be informed by adding relevant information at key moments, shifting it in the desired direction. However, the pursuit of objectives beyond interaction quality may compromise this very quality. Moreover, as the depth and informativeness of reasoning increase, so do the number of tokens required, leading to higher latency and cost. This study investigates how an LLM-based coaching agent can adjust its reasoning depth using a discrepancy mechanism that signals how much reasoning effort to allocate based on how well the objective is being met. Our discrepancy-based mechanism constrains reasoning to better align with alternative objectives, reducing cost roughly tenfold while minimally impacting interaction quality.Type:conference paperJournal:Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI ’25)Scopus© Citations 6 - Some of the metrics are blocked by yourconsent settings
Item type:Publication, An AI Approach for Predicting Audience Reach of Presentation Slides(2024-06-16); ; There is a near overflow of presentation slides on digital platforms, such as SlideShare.net, with 40 million. This presents a challenge in assessing their projected impact due to its high complexity and required expertise. We propose a novel approach using machine learning techniques to predict presentation slide audience reach. We crawled a unique dataset of over 8000 slides and extracted relevant attributes. A model was trained where we are the first to employ both numerical and textual inputs. Initial results with an R² value of 0.579 suggest that the audience reach of presentation slides can be automatically evaluated. Our findings contribute to the current understanding of the assessment of online documents, introducing possibilities for further research, such as focusing on domain-specific applications and incorporating them as tools for decision support in content management systems on sharing platforms.Type:conference paper