Every gamer likely recognizes the feeling of being completely absorbed in a game while losing track of time. This state, often referred to as the flow state, is associated with higher levels of concentration and peak performances. As such, it holds particular relevance for players seeking to enhance their performance. A deeper understanding of the flow state within professional esports could help players access it more effectively. This systematic literature review aims to examine the current state-of-the-art of the flow state in esports.

Literatur

• Harris, D. J., Allen, K. L., Vine, S. J., & Wilson, M. R. (2023). A systematic review and meta-analysis of the relationship between flow states and performance. International review of sport and exercise psychology, 16(1), 693-721.
• Oliveira, W., & Hamari, J. (2025). Flow Experience in Gameful Approaches: A Systematic Literature Review, Scientometric Analysis, and Research Agenda. International Journal of Human–Computer Interaction, 1-27.

Flow is the experience of being fully focused on an activity and fluidity of action. Musicians experiencing flow associate this with greater self-confidence and self-assurance, as well as having a better attentional focus on music making. While flow is desirable, it is a highly personalized state influenced by both intrinsic motivation and the degree of challenge involved in the activity. Exploring flow can improve musicians' current learning strategies and enhance their performance when playing. This seminar thesis will therefore examine the characteristics and effects of the flow state in musicians on the basis of a literature review.

Literatur

• Bloom, A. J., & Skutnick-Henley, P. (2005). Facilitating flow experiences among musicians. American Music Teacher, 54(5)
• Cziksentmihalyi, M. (1990). Flow–the psychology of optimal experience. 1990.
• Tan, L., & Sin, H. X. (2021). Flow research in music contexts: A systematic literature review. Musicae scientiae, 25(4), 399-428.

Stress occurs when a situation is perceived as threatening or challenging for an individual. In recent years higher focus on reducing work-related challenges and an increased awareness of mental health have led to the development of various stress-coping strategies. Among others, biofeedback presents a promising solution by enabling individuals to control physiological activities. Heart rate variability (HRV) biofeedback is one of the most common biofeedback techniques for stress management and can be easily implemented via wearable devices such as smartwatches. This systematic literature review aims to identify HRV biofeedback strategies to cope with work-related stress. 

Literatur

• Fazal, A., Alam, R., Sajid, U., & Batool, S. F. (2024). Comparing the Effectiveness of HRV Biofeedback and Mindfulness for Workplace Stress Reduction. Pakistan Journal of Medical and Health Education, 4(2), 94-101.
• Lehrer, P. M., & Gevirtz, R. (2014). Heart rate variability biofeedback: how and why does it work?. Frontiers in psychology, 5, 756.
• Sveinsdóttir, S. Þ., Lehrer, P., & Jóhannsdóttir, K. R. (2025). Can HRV Biofeedback Training Improve the Mental Resilience of Icelandic Police Officers?. Applied Psychophysiology and Biofeedback, 50(1), 49-64.
• Yu, B., Funk, M., Hu, J., Wang, Q., & Feijs, L. (2018). Biofeedback for everyday stress management: A systematic review. Frontiers in ICT, 5, 23.

Due to the growing prominence of local post-hoc attribution methods for feature importance like SHAP and LIME in the XAI research community, concern about how trustworthy and usable these and comparable methods actually are in practice has also grown. To ensure that the explanations provided by local post-hoc attribution methods actually reflect the ML model’s inner workings, methods to measure and ensure the faithfulness of the explanation to its respective model have been proposed. By incorporating such techniques into XAI systems researchers hope to provide more reliable and predictable explanations.

In the scope of this seminar paper, a literature review will be conducted to explore the state-of-the-art on both faithfulness measures and training methods for increased model fidelity in XAI research.

Literatur

• Kommiya Mothilal, R., Mahajan, D., Tan, C., & Sharma, A. (2021, July). Towards unifying feature attribution and counterfactual explanations: Different means to the same end. In Proceedings of the 2021 AAAI/ACM Conference on AI, Ethics, and Society (pp. 652-663).
• Robnik-Šikonja, M., & Bohanec, M. (2018). Perturbation-based explanations of prediction models. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, (pp. 159-175).

With AI finding more and more use cases in a multitude of practical domains, we can observe a shift in the requirements articulated towards such systems. The scientific community has brought forth AI models with high accuracy, amassed a plethora of knowledge about model architectures, training procedures and also, possible shortcomings of such systems. This increased applicability has motivated research into how domain experts and lay users interact with AI systems in practice and what they require for these interactions to be productive and desirable. One such desideratum is explainability. XAI techniques have already contributed vastly to the discovery of errors in otherwise performant AI models. To make such knowledge accessible not only to the researcher, but also the end user, governments around the world have passed laws demanding that systems working with AI be made “explainable”. 

In the scope of this seminar paper, a literature review will be conducted to explore how legislation demanding explainability in AI systems is discussed in XAI research.

Literatur

• Sovrano, F., Sapienza, S., Palmirani, M., & Vitali, F. (2022). Metrics, explainability and the European AI act proposal. Legal Knowledge and Information Systems, 5(1), 126-138.
• Bibal, A., Lognoul, M., De Streel, A., & Frénay, B. (2021). Legal requirements on explainability in machine learning. Artificial Intelligence and Law, 29(2), 149-169.

Der zunehmende Einsatz generativer künstlicher Intelligenz (KI) verändert die Art und Weise, wie das Lernen im Bildungsalltag organisiert und unterstützt wird. Dabei können Lernende beim Erarbeiten von Inhalten, bei Hausaufgaben oder beim Verstehen komplexer Themen auf digitale Tools zurückgreifen. In der öffentlichen und wissenschaftlichen Diskussion spielen Eltern, die diese Vorgänge begleiten, eine zentrale Rolle. Trotz der wachsenden Relevanz des Themas gibt es bislang nur wenige umfassende und systematische Untersuchungen dazu, wie Eltern den Einsatz generativer KI im Bildungsalltag der Kinder wahrnehmen. Die bisherigen Befunde sind verstreut und die elterliche Perspektive wurde bisher nur am Rande berücksichtigt. Vor diesem Hintergrund zielt die vorliegende Seminararbeit darauf ab, durch eine systematische Literaturrecherche herauszuarbeiten, wie Eltern den Einsatz generativer KI im Bildungsalltag der Kinder in der bestehenden Forschung wahrnehmen, welche Möglichkeiten und Risiken damit verbunden sind und welche Fragen sich für zukünftige Studien ergeben.

Literatur

• Eira, M., Amirkaveh Rasouli, & Charisi, V. (2025). Parents’ Perceptions About the Use of Generative AI Systems by Adolescents. 927–931. https://doi.org/10.1145/3713043.3731508
• Nurhayati, S., Taufikin, T., Judijanto, L. & Musa, S. (2025). Towards Effective Artificial Intelligence-Driven Learning in Indonesian Child Education: Understanding Parental Readiness, Challenges, and Policy Implications. Educational Process: International Journal. doi.org/10.22521/edupij.2025.15.155

Mittels generativer KI verändern sich die Abläufe des Lehrens und Lernens in unterschiedlichen Bildungsstufen zunehmend. In der Diskussion stehen häufig Eltern mit schulpflichtigen Kindern im Mittelpunkt. Doch auch Eltern ohne direkten Bezug zum Bildungssystem sind relevant, da gesellschaftliche Diskurse auch von Eltern ohne schulpflichtige Kinder mitgeprägt werden und Einstellungen zur technologischen Entwicklung im Bildungswesen geäußert werden. Die Sichtweisen dieser Eltern eröffnen eine Außenperspektive, die für die Analyse der gesellschaftlichen Akzeptanz potenziell von hoher Relevanz ist. Bislang gibt es jedoch nur wenige Forschungsergebnisse dazu, wie Eltern ohne schulpflichtige Kinder den Einsatz generativer KI im Bildungswesen einschätzen. Der aktuelle Stand der wissenschaftlichen Auseinandersetzung ist begrenzt und nicht umfassend aufgearbeitet. Vor diesem Hintergrund zielt die vorliegende Seminararbeit darauf ab, durch eine systematische Literaturrecherche herauszuarbeiten, welche Wahrnehmungen und Haltungen von Eltern ohne Kinder in Bezug auf den Einsatz generativer KI im Bildungswesen in der Forschung dokumentiert sind und welche Forschungsbedarfe sich daraus ergeben.

Literatur

• J, K. A., Charland, P., Martineau, J-T., de, Lesage, A., Senecal, S., & Leger, P-M. (2024). Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education. ArXiv.org. arxiv.org/abs/2402.15027

Der Einsatz generativer KI verändert die Strukturen des Bildungswesens zunehmend, wodurch Lernen individualisiert, Wissensvermittlung neugestaltet und pädagogische Rollen neu definiert werden. Durch KI-Systeme erhalten Lernende unmittelbaren Zugang zu personalisierter Unterstützung. Dadurch wird die klassische Rolle von Lehrkräften und Dozierenden als zentrale Wissensquelle zunehmend infrage gestellt. Mithilfe von Systemen wie ChatGPT können Lernende bei Aufgaben unterstützt werden, die traditionell von Lehrkräften übernommen wurden, beispielsweise bei der Erklärung komplexer Inhalte oder der Bereitstellung von Lernhilfen. Dadurch verändert sich die Rolle der Lehrkräfte hin zu einer stärkeren Lernbegleitung und weg von der direkten Wissensvermittlung. Obwohl die Debatte um die Zukunft der Lehrerrolle intensiv geführt wird, ist bislang kaum erforscht, wie Eltern diese Veränderungen wahrnehmen und bewerten. In der wissenschaftlichen Diskussion werden bisher nur vereinzelt Erkenntnisse aus Studien berücksichtigt. Vor diesem Hintergrund zielt die vorliegende Seminararbeit darauf ab, durch qualitative Interviews herauszuarbeiten, wie Eltern die potenzielle Verschiebung der Lehrerrolle durch den Einsatz generativer KI wahrnehmen, welche Chancen und Risiken damit verbunden sind und welche Muster sich in den elterlichen Einschätzungen erkennen lassen.

Literatur

• Söderström, U., Hedström, E., Lambertsson, K., & Mejtoft, T. (2024). ChatGPT in education: Teachers’ and Students’ views. Proceedings of the European Conference on Cognitive Ergonomics 2024, 1–10. https://doi.org/10.1145/3673805.3673828
• Seiradakis, E. V. (2024). Unpacking Experts’ Opinions on ChatGPT Potential Assistive Roles and Risks in Early Childhood Special Education. Communications in Computer and Information Science, 380–392. https://doi.org/10.1007/978-3-031-63235-8_25
• Eira, M., Amirkaveh Rasouli, & Charisi, V. (2025). Parents’ Perceptions About the Use of Generative AI Systems by Adolescents. 927–931. doi.org/10.1145/3713043.3731508

Der zunehmende Einsatz generativer künstlicher Intelligenz (KI) in Bildungseinrichtungen verändert Gestaltung und Ablauf von Lehr- und Lernprozessen. Mithilfe von Systemen wie ChatGPT sind personalisierte Unterstützung, schnelle Informationsverarbeitung und adaptive Lernangebote möglich. Von diesen Entwicklungen sind nicht nur Lernende und Lehrkräfte, sondern auch Eltern betroffen, die eine wichtige Rolle bei der Begleitung schulischer Bildung einnehmen. Trotz der Relevanz elterlicher Einstellungen für die gesellschaftliche Akzeptanz und Einbindung generativer KI in Schulen gibt es bislang nur eine geringe Anzahl Untersuchungen zu diesem Forschungsbereich. Die vorliegenden Studien sind fragmentiert und liefern keine umfassenden Befunde zur Breite elterlicher Wahrnehmungen. Vor diesem Hintergrund zielt die vorliegende Seminararbeit darauf ab, ein Forschungsmodell anhand einer quantitativen Datenerhebung abzuleiten, das die Einflussfaktoren von Eltern in Bezug auf die Nutzung generativer KI in Schulen untersucht.

Literatur

• Eira, M., Amirkaveh Rasouli, & Charisi, V. (2025). Parents’ Perceptions About the Use of Generative AI Systems by Adolescents. 927–931. https://doi.org/10.1145/3713043.3731508
• J, K. A., Charland, P., Martineau, J-T., de, Lesage, A., Senecal, S., & Leger, P-M. (2024). Multi-stakeholder Perspective on Responsible Artificial Intelligence and Acceptability in Education. ArXiv.org. arxiv.org/abs/2402.15027

Künstliche Intelligenz (KI) gewinnt im Arbeitskontext zunehmend an Bedeutung und verändert sowohl Arbeitsprozesse als auch die Wahrnehmung beruflicher Rollen. Dabei rückt die Frage in den Fokus, wie Menschen ihre Beziehung zu KI gestalten und ob sie diese Technologien als Teil ihres beruflichen Selbstverständnisses begreifen. Das Konzept der AI Identity beschreibt genau diesen Aspekt und baut auf der etablierten IT-Identity Theory auf, die erklärt, wie Individuen Technologie in ihre Identität integrieren. Vor diesem Hintergrund eröffnet AI Identity neue Perspektiven darauf, wie sich Beschäftigte mit KI identifizieren und welche Implikationen dies für Organisationen hat. 

Diese Seminararbeit untersucht, wie sich das Konzept der AI Identity aus der IT-Identity Theory entwickelt hat. Durch eine systematische Literaturrecherche soll der aktuelle Forschungsstand dargestellt und zentrale Gemeinsamkeiten sowie Unterschiede herausgearbeitet werden.

Literatur

• Carter, M., Petter, S., Grover, V. & Thatcher, J. B. (2020). Information Technology Identity: A Key Determinant of IT Feature and Exploratory Usage. MIS Quarterly, 44(3), 983–1021. doi.org/10.25300/misq/2020/14607
• Qin, M., Qiu, S., Li, S. & Jiang, Z. (2025). Research on the impact of employee AI identity on employee proactive behavior in AI workplace. Industrial Management & Data Systems. doi.org/10.1108/imds-03-2024-0211

Künstliche Intelligenz (KI) verändert zunehmend die Arbeitswelt und beeinflusst, wie Beschäftigte ihre Rolle und ihr Selbstverständnis im Beruf gestalten. Das Konzept der AI Identity bietet dabei eine wichtige Grundlage, um zu verstehen, wie stark sich Mitarbeitende mit KI identifizieren. Während eine ausgeprägte AI Identity zu Vorteilen wie mehr Engagement und Eigeninitiative führen kann, weisen Forschungen auch auf mögliche Spannungsfelder hin, etwa Konflikte oder unethisches Verhalten. Entscheidend ist daher die Frage, welche persönlichen Faktoren sowie welche Rahmenbedingungen in Organisationen die Entwicklung einer AI Identity begünstigen oder hemmen.

Diese Seminararbeit widmet sich der Frage, welche Bedingungen die Entstehung und Ausprägung von AI Identity beeinflussen. Durch Experteninterviews sollen individuelle Faktoren (z. B. Nutzungserfahrung, Vertrauen in KI) sowie organisationale Bedingungen (z. B. Unternehmenskultur, Arbeitsumfeld) untersucht werden, die die Identifikation mit KI fördern oder hemmen können.

Literatur

• Cao, L., Chen, C., Dong, X., Wang, M. & Qin, X. (2023). The dark side of AI identity: Investigating when and why AI identity entitles unethical behavior. Computers in Human Behavior, 143, 107669. doi.org/10.1016/j.chb.2023.107669
• Qin, M., Qiu, S., Li, S. & Jiang, Z. (2025). Research on the impact of employee AI identity on employee proactive behavior in AI workplace. Industrial Management & Data Systems. doi.org/10.1108/imds-03-2024-0211
• Zhou, J., Lu, Y. & Chen, Q. (2024). GAI Identity Threat: When and Why Do Individuals Feel Threatened? Information & Management, 104093. https://doi.org/10.1016/j.im.2024.104093 

Der Einsatz von KI-Technologien führt in vielen Organisationen zu einer spürbaren Veränderung beruflicher Rollen. Beschäftigte übernehmen neue Verantwortlichkeiten, gewöhnen sich an automatisierte Prozesse und erweitern dabei ihre Fähigkeiten im Umgang mit KI. Mit dem Konzept der AI Identity wird untersucht, in welchem Maße Mitarbeitende KI in ihr berufliches Selbstverständnis integrieren. Diese Entwicklung kann zu produktiven Anpassungen, aber auch zu Spannungen führen, da die Verbindung zwischen beruflicher Identität und AI Identity sowohl förderliche als auch problematische Auswirkungen haben kann.

Diese Seminararbeit untersucht, wie sich AI Identity auf die berufliche Rolle von Beschäftigten auswirkt. Durch Experteninterviews mit Fachkräften sollen Einblicke gewonnen werden, wie KI den Arbeitsalltag verändert und welche Anpassungsprozesse dadurch entstehen.

Literatur

• Cao, L., Chen, C., Dong, X., Wang, M. & Qin, X. (2023). The dark side of AI identity: Investigating when and why AI identity entitles unethical behavior. Computers in Human Behavior, 143, 107669. https://doi.org/10.1016/j.chb.2023.107669
• Kassa, B. Y. & Worku, E. K. (2025). The Impact of Artificial Intelligence on Organizational Performance: The Mediating Role of Employee Productivity. Journal Of Open Innovation Technology Market And Complexity, 100474. doi.org/10.1016/j.joitmc.2025.100474
• Mirbabaie, M., Brünker, F., Frick, N. R. J. M. & Stieglitz, S. (2021). The rise of artificial intelligence – understanding the AI identity threat at the workplace. Electronic Markets, 32(1), 73–99. doi.org/10.1007/s12525-021-00496-x
• Qin, M., Qiu, S., Li, S. & Jiang, Z. (2025). Research on the impact of employee AI identity on employee proactive behavior in AI workplace. Industrial Management & Data Systems. doi.org/10.1108/imds-03-2024-0211

Immer mehr Unternehmen setzen auf KI, um ihre Wettbewerbsfähigkeit zu sichern und ihre Prozesse effizienter zu gestalten. Mit dieser Entwicklung verändert sich auch, wie Mitarbeitende ihre Arbeit wahrnehmen und welchen Stellenwert Technologien in ihrem Selbstverständnis einnehmen. Eine stark ausgeprägte AI Identity kann dabei positive Impulse liefern, etwa durch mehr Motivation oder eine höhere Innovationskraft. Gleichzeitig bestehen Risiken wie Abhängigkeit, Überidentifikation oder sogar unethisches Verhalten. Daraus ergibt sich für Organisationen die zentrale Aufgabe, die Balance zwischen den Chancen und Gefahren von AI Identity zu finden.

Diese Seminararbeit analysiert Chancen und Risiken, die mit einer starken AI Identity verbunden sind. Durch Experteninterviews sollen sowohl positive Effekte (z. B. Motivation, Innovationskraft) als auch mögliche Risiken (z. B. Abhängigkeit, Überidentifikation) aufgezeigt werden.

Literatur

• Cao, L., Chen, C., Dong, X., Wang, M. & Qin, X. (2023). The dark side of AI identity: Investigating when and why AI identity entitles unethical behavior. Computers in Human Behavior, 143, 107669. doi.org/10.1016/j.chb.2023.107669
• Richter, J. & Schaller, R. (2025). AI Identity Threats and Reinforcement in Organizations: A Theoretical Model of Professional Role Identity Implications. Proceedings Of The . . . Annual Hawaii International Conference On System Sciences/Proceedings Of The Annual Hawaii International Conference On System Sciences. doi.org/10.24251/hicss.2025.023

Große Sprachmodelle (LLMs), die auf Transformer-Architekturen basieren, haben in den letzten Jahren enorme Fortschritte in der Sprachverarbeitung ermöglicht. Ihre Stärke liegt in der Generierung kohärenter, kontextsensitiver Texte auf Basis riesiger Datenmengen. Allerdings stoßen LLMs an Grenzen, wenn es um Aktualität, Faktenverlässlichkeit und Zugriff auf domänenspezifisches Wissen geht.

Retrieval Augmented Generation (RAG) adressiert diese Herausforderungen, indem es die generativen Fähigkeiten von LLMs mit einem Retrieval-Modul kombiniert. Dieses Modul ermöglicht den dynamischen Zugriff auf externe Dokumentensammlungen, sodass Antworten nicht nur plausibel, sondern auch faktenbasiert und aktueller sind.

Die Seminararbeit stellt beide Ansätze gegenüber: Wie unterscheiden sich Transformer-basierte LLMs und RAG-Architekturen in Aufbau, Funktionsweise und Leistungsfähigkeit? Welche Vor- und Nachteile ergeben sich in Bezug auf Genauigkeit, Interpretierbarkeit und Skalierbarkeit? Und in welchen Anwendungsfeldern ist der Einsatz von RAG klassischen LLMs überlegen – etwa im Wissensmanagement oder in der Domänenanpassung?

Ziel ist es, ein differenziertes Verständnis für die komplementären Stärken und Grenzen beider Ansätze zu entwickeln und so Perspektiven für die Weiterentwicklung leistungsfähiger KI-Systeme aufzuzeigen.

Literatur

• Arslan, M., Ghanem, H., Munawar, S., & Cruz, C. (2024). A Survey on RAG with LLMs. Procedia computer science, 246, 3781-3790.
• Yu, H., Gan, A., Zhang, K., Tong, S., Liu, Q., & Liu, Z. (2024, August). Evaluation of retrieval-augmented generation: A survey. In CCF Conference on Big Data (pp. 102-120). Singapore: Springer Nature Singapore.

Im Seminar wird erarbeitet, wie RAG-Architekturen technisch funktionieren: Von der Indexierung großer Dokumentmengen über semantische Vektorrepräsentationen bis hin zur dynamischen Kombination von Retrieval- und Generationsschritt. Gleichzeitig wird diskutiert, welche Herausforderungen sich stellen – etwa hinsichtlich der Qualität und Kuratierung von Wissensquellen, der Reduktion von Halluzinationen oder der Skalierbarkeit.

Ein besonderer Fokus liegt auf praktischen Anwendungsfeldern: Wie können Unternehmen RAG einsetzen, um internes Wissen effizient nutzbar zu machen? Welche Rolle spielt RAG bei der Entwicklung von Chatbots, Suchsystemen oder Assistenzanwendungen? Und wie verändert diese Technologie die Erwartungen an zukünftige KI-Systeme?

Ziel des Seminars ist es, ein fundiertes Verständnis für die Funktionsweise, Potenziale und Grenzen von RAG zu entwickeln und dessen Bedeutung für die nächste Generation wissensbasierter KI-Lösungen zu reflektieren.

Literatur

• Arslan, M., Ghanem, H., Munawar, S., & Cruz, C. (2024). A Survey on RAG with LLMs. Procedia computer science, 246, 3781-3790.
• Yu, H., Gan, A., Zhang, K., Tong, S., Liu, Q., & Liu, Z. (2024, August). Evaluation of retrieval-augmented generation: A survey. In CCF Conference on Big Data (pp. 102-120). Singapore: Springer Nature Singapore.

Software modeling is often considered a central part of software development. Among others, software models shall help bridging communication barriers among different stakeholders and support documenting central design decisions. The positive impact of software modeling is, however, sometimes questioned and depends on many contextual factors (Fettke 2009; Lukyanenko & Parsons 2013; Nugroho & Chaudron 2009). In order to analyze the potential impact of software modeling, then, potential impact factors need to be analyzed within a particular context.

This seminar paper shall identify potential impact factors of software modeling, explicate to what contextual parameters they relate, and how the different impact factors may be interrelated. For this purpose, available case studies on software modeling should be consulted (e.g., Gomaa 2011; Nugroho and Chaudron 2009).

Literatur

• Fettke P (2009) How Conceptual Modeling Is Used. Communications of the Association for Information Systems 25
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Gomaa H (2011) Software Modeling & Design: UML, Use Cases, Patterns, & Software Architectures. Cambridge University Press: Cambridge
• Leung F, Bolloju N (2005) Analyzing the Quality of Domain Models Developed by Novice Systems Analysts. Proceedings of the 38th Annual Hawaii International Conference on System Sciences
• Lindland OI, Sindre G, Sølvberg A (1994) Understanding Quality in Conceptual Modeling. IEEE Software 11(2):42–49
• Lukyanenko R, Parsons J (2013) Is Traditional Conceptual Modeling Becoming Obsolete? Conceptual Modeling - ER 2013: 32th International Conference, ER 2013 Hong-Kong, China, November 11-13, 2013, Proceedings, pp 61–73
• Nugroho A, Chaudron MRV (2009) Evaluating the Impact of UML Modeling on Software Quality: An Industrial Case Study. Model Driven Engineering Languages and Systems: 12th International Conference, MODELS 2009, Denver, CO, USA, October 4-9, 2009, Proceedings, pp 181–195
• Schütte R, Seufert S, Wulfert T (2022) IT-Systeme Wirtschaftlich Verstehen und Gestalten: Methoden, Paradoxien, Grundsätze. Springer: Berlin, Heidelberg

Generative Künstliche Intelligenz (GenAI) und algorithmische Entscheidungsfindung verändern gegenwärtig die Art und Weise von Arbeit, Entscheidungsprozessen und organisationaler Governance (Budhwar et al., 2023). Bei der Rekrutierung von Mitarbeitern materialisiert sich dieser Wandel besonders sichtbar: von genAI-unterstützten bzw. -geführten Interviews über automatisierte Screening- und Ranking-Verfahren bis hin zu generativen Systemen, die Empfehlungen, Begründungen oder gar autonom Entscheidungen im Rahmen des „Algorithmic Decision-Makings (ADM)“ produzieren (Hewage, 2023; Jaser & Petrakaki, 2023). Entsprechend verschwimmen im sozio-technischen Recruitment-Kontext zunehmend die Grenzen zwischen menschlichen und maschinellen Entscheidungsträgern.
Während die betriebswirtschaftliche und informationstechnische Debatte stark von Anbieter und Organisationsperspektiven geprägt ist, fehlt es an theoriebildenden Einsichten zur Erfahrungs- und Wahrnehmungswelt von Bewerbenden gegenüber GenAI im Recruitment-Prozess; insbesondere hinsichtlich der Wahrnehmung gegenüber ADM. Das Seminar adressiert diese Forschungslücke.

Ziel ist es, die Wahrnehmung von Bewerbenden auf GenAI/ADM im Recruiting induktiv zu rekonstruieren und konzeptuell zu ordnen. Auf Basis bereits extrahierter Posts aus Reddit (z. B. r/recruiting, r/recruitingHell) sollen im Rahmen einer Social Media Analyse (siehe Steiglitz et al., 2018) die folgenden Forschungsfragen beantwortet werden:

1. Wie nehmen Bewerbende GenAI/ADM in unterschiedlichen Sub-Prozesses (z. B. Ausschreibung, Telefoninterview, etc.) des Recruitings wahr (z. B. Fairness, Intransparenz, Entfremdung)?
2. Welche Konsequenzen in Form von Erfahrungs- und Interaktionsmustern berichten Bewerbende (z. B. im Umgang mit KI-gestützten Interviews, Strategien gegenüber Applicant Tracking Systemen)?

Für die Auswertung empfiehlt sich die Gioia-Methodik im Sinne eines Grounded-Theory Ansatzes. Erwartet wird ein Beitrag zur HR-Praxis und IS-Debatte um sozio-technische (Re-)Konfigurationen algorithmischer Personalarbeit.

Literatur

• Budhwar, P., Chowdhury, S., Wood, […]. (2023). Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT. Human Resource Management Journal, 33(3), 606–659. doi.org/10.1111/1748-8583.12524
• Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2013). Seeking qualitative rigor in inductive research: Notes on the Gioia methodology. Organizational research methods, 16(1), 15-31.
• Hewage, A. (2023). Exploring the Applicability of Artificial Intelligence in Recruitment and Selection Processes: A Focus on the Recruitment Phase. Journal of Human Resource and Sustainability Studies, 11, 603-634. https://doi.org/10.4236/jhrss.2023.113034
• Jaser, Z., & Petrakaki, D. (2023). Are You Prepared to Be Interviewed by an AI? Harvard Business Review, February 7. hbr.org/2023/02/are-you-prepared-to-be-interviewedby-an-ai
• Stieglitz, S., Mirbabaie, M., Ross, B., & Neuberger, C. (2018). Social media analytics–Challenges in topic discovery, data collection, and data preparation. International journal of information management, 39, 156-168

Over the past decade, research on Artificial Intelligence (AI) in recruitment and selection has expanded rapidly across Information Systems (IS), Human Resource Management (HRM), and Computer Science (e.g., Agarwal et al., 2023). Not least due to further rapid advances in generative AI (e.g., ChatGPT, Gemini AI, etc.), research focusing on AI and recruitment and selection proliferated: A recent bibliometric synthesis documents a marked post-2017 acceleration in publications, an annual growth rate exceeding 20 percent, and a dense conceptual nexus around “machine learning,” “e-recruitment,” and “decision-making,” while simultaneously concluding that the field needs direction and deeper interdisciplinarity beyond fragmented silos of thought (Lawande, 2024). While Lawande’s (2024) review offers a fruitful starting point, methodological limitations hinder wider applicability of the results. For instance, the search strategy relies on a narrow query design, centered on the conjunction of “recruitment” AND “artificial intelligence,” with the author recommending broader synonym sets for future work.

Against this backdrop, the aim of this seminar is to produce a rigorous, reproducible bibliometric review (see Block & Fisch, 2020; Donthu et al., 2021) that: (1) maps the intellectual, social, and conceptual structure of AI in recruitment and selection; (2) traces its thematic evolution; and (3) derives an agenda for interdisciplinary research at the intersection of AI and recruitment and selection.

Suitable tools for analysis are – among others – PyBiBx (Pereira et al., 2025), VOSviewer (Van Eck & Waltman, 2011), and/or Bibliometrix with Biblioshiny. For an overview of suitable tools, please see Moral-Munoz et al. (2020).

Literatur

• Agarwal, S., Gupta, A., & Roshani, P. (2023). Redefining HRM with artificial intelligence and machine learning. In ‘The adoption and effect of artificial intelligence on human resources management’, part a (pp. 1-13). Emerald Publishing Limited.
• Block, J. H., & Fisch, C. (2020). Eight tips and questions for your bibliographic study in business and management research. Management Review Quarterly, 70(3), 307-312. ▪ Donthu, N., Kumar, S., Mukherjee, D., Pandey, N., & Lim, W. M. (2021). How to conduct a bibliometric analysis: An overview and guidelines. Journal of business research, 133, 285-296.
• Lawande, N. (2024). Exploring the Trends of Artificial Intelligence in Recruitment: A Bibliometric Study. International Journal of Supply & Operations Management, 11(3).
• Moral-Muñoz, J. A., Herrera-Viedma, E., Santisteban-Espejo, A., & Cobo, M. J. (2020). Software tools for conducting bibliometric analysis in science: An up-to-date review. Profesional de la Información, 29(1).
• Pereira, V., Basilio, M. P., & Santos, C. H. T. (2025). PyBibX–a Python library for bibliometric and scientometric analysis powered with artificial intelligence tools. Data Technologies and Applications, 59(2), 302-337.
• Van Eck, N. J., & Waltman, L. (2011). Text mining and visualization using VOSviewer. arXiv preprint arXiv:1109.2058.

The evolution of digital platforms has revolutionized the recruitment landscape, particularly in the technology sector, where both the dynamics of hiring and the candidate experience are under increasing scrutiny (Höllig et al., 2024). In this context, employee-generated reviews (e.g., from Kununu or Glassdoor) have emerged as a vital source of insight into the operational efficacy and perceived shortcomings of recruitment processes (e.g., Jung & Suh, 2019). This seminar embarks on an empirical investigation aimed at bridging the gap between theoretical best practices and the lived experiences of job applicants as reflected in online reviews from Kununu. The primary objective of this seminar is to conduct a comprehensive social media analysis of employee reviews focused on recruitment processes at technology firms. By leveraging data scraped from Kununu, this seminar seeks to systematically identify and synthesize the significant pain points that impede candidate engagement. The gathered insights from this analysis are expected to contribute to the development of robust, empirically grounded theory-building concepts and to guide managerial recommendations on “how to tackle the recruitment labyrinth”. Methodologically, the seminar should adopt a qualitative framework that emphasizes the iterative and data-driven nature of grounded theory (e.g., Strauss & Corbin, 1990; Strübing & Strübing, 2021). In detail, the analysis will involve coding and categorizing the textual data from Kununu reviews, allowing for the emergence of patterns and themes that articulate the complexities of the recruitment experience. This process is designed to not only articulate the challenges and deficiencies within current recruitment practices but also to finally distill actionable insights and managerial recommendations aimed at enhancing the overall efficacy of recruitment processes in technology firms.

Reviews from Kununu (e.g., a databasis of already scraped reviews from selected TechDax firms) will be handed out to the student (i.e., there is no need to scrape the data yourself) 

Literatur

• Höllig, C. E., Tumasjan, A., & Lievens, F. (2024). What drives employers’ favorability ratings on employer review platforms? The role of symbolic, personal, and emotional content. International Journal of Selection and Assessment, 32(4), 579-593.
• Jung, Y., & Suh, Y. (2019). Mining the voice of employees: A text mining approach to identifying and analyzing job satisfaction factors from online employee reviews. Decision Support Systems, 123, 113074.
• Strauss, A., & Corbin, J. (1990). Basics of qualitative research (Vol. 15). Newbury Park, CA: Sage.
• Strübing, J., & Strübing, J. (2021). Was ist grounded theory? (pp. 9-37). Springer Fachmedien Wiesbaden.

Artificial Intelligence (AI) is a broad field of computer science concerned with creating systems that can perform tasks traditionally requiring human intelligence, such as perception, reasoning, learning, and problem-solving (Russell, 2016). Over the past decade, AI technologies have advanced rapidly, leading to transformative applications across medicine, energy, business, and creative industries. Despite this rapid development, the overall impact of AI on organizations, society, and the economy remains unclear. While some effects, such as automation and efficiency gains, are readily observable, other consequences, such as shifts in innovation processes, labor markets, and organizational structures, are less well understood and documented (Filippucci et al., 2024).

Machine learning (ML) is a core branch of Artificial Intelligence that enables computers to learn patterns and make predictions from data without being explicitly programmed. ML has gained increasing importance across disciplines due to its ability to handle complex data and support decision-making in ways that were previously not possible (Russell, 2016).

Potential application areas are diverse and highly relevant to society and business. In medicine, for example, ML algorithms are used for cancer detection, where they can identify anomalies in imaging or patient data with remarkable accuracy (McCarthy et al., 2004). In the energy sector, ML supports the forecasting of electricity demand, which is essential for maintaining grid stability and ensuring a sustainable energy supply (Lotfi, 2025). These examples show how ML-Algorithms can be used to improve process times and thus efficiency. Companies adopting ML technologies often report improvements in efficiency, cost reduction, and decision quality (Russel, 2016).

At the same time, ML can foster innovation by enabling new products, services, and business models. One famous example for that can be the development of self-driving cars (Ni et al., 2020). This ambidexterity is a general theme, often observed in IT systems.

However, there is no collection and categorization of those effects, which are induced by ML Algorithms. Thus, making it non-transparent which impacts are ML specific, and which are also observable in normal IT systems. The topic therefore aims at identifying, categorizing, and critically reflecting on the impacts of ML-Algorithms to provide a clearer overview and better differentiation of those effects.

Literatur

• Filippucci, F. et al. (2024), “The impact of Artificial Intelligence on productivity, distribution and growth: Key mechanisms, initial evidence and policy challenges”, OECD Artificial Intelligence Papers, No. 15, OECD Publishing, Paris, doi.org/10.1787/8d900037-en.
• Jordan, M. I., & Mitchell, T. M. (2015). Machine learning: Trends, perspectives, and prospects. Science, 349(6245), 255–260. https://doi.org/10.1126/science.aaa8415
• Lotfi, H., Vafadoost, P., Rokhsati, H., & Parsadust, H. (2025). An optimized machine learning approach for short-term electricity demand forecasting considering environmental and historical influencing factors. Discover Applied Sciences, 7(9). https://doi.org/10.1007/s42452-025-07681-z
• McCarthy, J. F., Marx, K. A., Hoffmann, P. E., Gee, A. G., O’Neil, P., Ujwal, M. L., & Hotchkiss, J. (2004). Applications of Machine Learning and High‐Dimensional Visualization in Cancer Detection, Diagnosis, and Management. Annals of the New York Academy of Sciences, 1020(1), 239–262. https://doi.org/10.1196/annals.1310.020
• Ni, J., Chen, Y., Chen, Y., Zhu, J., Ali, D., & Cao, W. (2020). A Survey on Theories and Applications for Self-Driving Cars Based on Deep Learning Methods. Applied Sciences, 10(8), 2749. https://doi.org/10.3390/app10082749
• Russell, S. (2016). Artificial intelligence (P. Norvig, Ed.; Third edition.). Pearson. 

Artificial Intelligence (AI) is a broad field of computer science concerned with creating systems that can perform tasks traditionally requiring human intelligence, such as perception, reasoning, learning, and problem-solving (Russell, 2016). Generative Artificial Intelligence (GenAI) is a novel and rapidly evolving branch of Artificial Intelligence that focuses on creating new content such as text, images, audio, or software code, based on patterns learned from large-scale datasets (Feuerriegel et al., 2023). Unlike traditional AI systems, which primarily classify or predict, Generative AI systems are capable of producing novel outputs that often resemble human-created artifacts (Feuerriegel et al., 2023). This creative capacity has attracted significant attention in both academic and business contexts.

Potential application areas are broad and expanding. Use cases can range between the automatic generation of product descriptions (Ghaffari et al., 2024) or building new business models (Kanbach et al., 2024). It shows that GenAI can be used to speed up processes and to allow for the persuasion of new business models.

However, the effects of Generative AI are not yet systematically categorized. This lack of structured understanding makes it difficult to distinguish which impacts are specific to Generative AI and which overlap with other IT-based innovations. The topic therefore aims at identifying, categorizing, and critically reflecting on the impacts of Generative AI to provide a clearer overview and better differentiation.

Literatur

• Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2023). Generative AI. Business & Information Systems Engineering, 66(1), 111–126. doi.org/10.1007/s12599-023-00834-7
• Ghaffari, S., Yousefimehr, B., & Ghatee, M. (2024). Generative-AI in E-Commerce: Use-Cases and Implementations. 2024 20th CSI International Symposium on Artificial Intelligence and Signal Processing (AISP), 1–5. doi.org/10.1109/aisp61396.2024.10475266
• Kanbach, D. K., Heiduk, L., Blueher, G., Schreiter, M., & Lahmann, A. (2023). The GenAI is out of the bottle: generative artificial intelligence from a business model innovation perspective. Review of Managerial Science, 18(4), 1189–1220. doi.org/10.1007/s11846-023-00696-z
• Russell, S. (2016). Artificial intelligence (P. Norvig, Ed.; Third edition.). Pearson.

The concept of Information Systems Business Value plays a central role in research on the economic and organizational impact of IT (Masli, 2011). IS Business Value refers to the effects of IT-related investments on the performance and capabilities of companies (Schryen, 2013). While the importance of digital technologies for competitive advantage is widely acknowledged, the question of how exactly IT investments contribute to measurable improvements remains complex (Porter, 2001). However, the calculation of IS Business Value is not trivial, as it requires capturing both direct outcomes, such as efficiency gains, and indirect effects, such as improved decision-making, innovation capacity, or organizational flexibility, while at the same time identifying and correctly measuring related expenditures (Schütte et al., 2022).

Research in the field of Information Systems has shown that the value of IT is strongly context dependent. Identical technologies can lead to very different results depending on organizational structures, complementary resources, or strategic alignment (Schütte et al., 2022). This makes the identification and measurement of IS Business Value a challenging but highly relevant research domain. From an IS perspective, understanding IS Business Value is crucial, as it connects technological developments with business outcomes and informs managerial decision-making. Studying IS Business Value is also important because the nature of IT and its role in companies is constantly changing. Emerging technologies such as artificial intelligence, cloud computing, or platform ecosystems raise new questions about how business value can be created and sustained. 

For the Wirtschaftsinformatik, which bridges the gap between computer science and management, analyzing these developments is essential to provide both theoretical insights and practical guidance for organizations navigating digital transformation. In this regard, a purely conceptual discussion is not sufficient. What is needed is a broader overview based on existing cases and IT implementations. An analysis should examine how organizations have calculated the business value of IT in practice, which approaches and metrics were applied, and what outcomes these evaluations produced. By comparing and generalizing across such cases, a more comprehensive understanding can be gained of how companies determine (or fail to determine) the business value of IT. 

Literatur

• Masli, A., Richardson, V. J., Sanchez, J. M., & Smith, R. E. (2011). The Business Value of IT: A Synthesis and Framework of Archival Research. Journal of Information Systems, 25(2), 81–116. doi.org/10.2308/isys-10117
• Porter, M. E. (2001). Strategy and the Internet. Harvard Business Review, 79(3), 63–78.
• Schryen, G. (2013). Revisiting IS business value research: what we already know, what we still need to know, and how we can get there. European Journal of Information Systems, 22(2), 139–169. doi.org/10.1057/ejis.2012.45
• Schütte, R., Seufert, S., & Wulfert, T. (2022). IT-Systeme wirtschaftlich verstehen und gestalten. Springer Fachmedien Wiesbaden GmbH.

Die zunehmende Integration von KI-Systemen in den Arbeitsalltag verspricht eine signifikante Steigerung der Produktivität und Effizienz. Ein zentraler Mechanismus hierfür ist das sogenannte „Cognitive Offloading", bei dem Menschen kognitiv anspruchsvolle Aufgaben an technologische Hilfsmittel delegieren. Dieses Konzept beschreibt die systematische Auslagerung oder Vereinfachung kognitiv anspruchsvoller Prozesse und ist tief in den Grundprinzipien menschlicher Kognition verwurzelt. Forschungsergebnisse haben gezeigt, dass das Gehirn evolutionär darauf programmiert ist, mentale Anstrengungen zu minimieren und verfügbare Energie effizient zu allokieren. Diese kognitive Ökonomie erklärt sowohl die intuitive Attraktivität von KI-Tools als auch deren produktivitätssteigernde Wirkung.

Cognitive Offloading ist grundsätzlich ein natürlicher und oft vorteilhafter Prozess, der Kapazitäten für kreativere und strategischere Tätigkeiten freisetzt. Problematisch wird es jedoch, wenn die unreflektierte Übernahme KI-generierter Outputs anstelle einer kritischen Auseinandersetzung mit Problemen tritt. Neue empirische Studien fanden Hinweise darauf, dass die Nutzung von KI-Systemen zu einer Schwächung des autonomen Denkens und der kritischen Urteilsfähigkeit führen kann (Gerlich, 2025; Stadler, Bannert & Sailer, 2024).

Diese Entwicklung zeigt sich insbesondere dann, wenn Nutzer aufhören, KI-Ergebnisse systematisch zu validieren. Das Resultat ist eine graduelle Abnahme der Fähigkeit zur unabhängigen Problemanalyse. Die wissenschaftliche Herausforderung liegt in der Identifikation jener Bedingungen, unter denen kognitives Auslagern produktiv bleibt, ohne in dysfunktionale Abhängigkeitsmuster zu münden.

Vor diesem Hintergrund soll in der vorliegenden Seminararbeit eine systematische Literaturrecherche durchgeführt werden, um die zugrundeliegenden kognitiven und psychologischen Mechanismen des Cognitive Offloadings zu beleuchten. Das Ziel ist es, die positiven Effekte den Risiken eines langfristigen Kompetenzverlustes gegenüberzustellen und die Bedingungen zu identifizieren, unter denen ein gesunder und produktiver Umgang mit KISystemen erzielt werden kann.

Literatur

• Gerlich, M. (2025). AI tools in society: Impacts on cognitive offloading and the future of critical thinking. Societies, 15(1), 6.
• Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688.
• Stadler, M., Bannert, M., & Sailer, M. (2024). Cognitive ease at a cost: LLMs reduce mental effort but compromise depth in student scientific inquiry. Computers in Human Behavior, 160, 108386

Die Integration von Künstlicher Intelligenz in kreative Prozesse, von der Bildgenerierung bis zur Entwicklung von Marketinginhalten, verändert traditionelle Arbeitsabläufe grundlegend. KI wird zunehmend nicht mehr als reines Werkzeug, sondern als potenzieller Partner im Innovationsprozess betrachtet (Grilli & Pedota, 2024). Dies ermöglicht eine neue Form der Mensch-KI-Kollaboration, die Effizienz mit Intuition und Urteilsvermögen verbindet. Ziel dieser Partnerschaft ist eine Synergie, bei der Innovationen entstehen, die weder der Mensch noch die KI allein hätte erreichen können.

Die vorliegende Seminararbeit verfolgt das Ziel, anhand einer strukturierten Literaturrecherche die Rolle von KI als Co-Creator im Innovationsprozess zu beleuchten. Es soll untersucht werden, wie KI durch die Bereitstellung unkonventioneller Lösungen und die Reduzierung der kognitiven Belastung die menschliche Kreativität anregt. In diesem Zuge sollen Schlüsselfaktoren für eine effektive Zusammenarbeit identifiziert werden, aus denen in einem zweiten Schritt Empfehlungen abgeleitet werden, wie Unternehmen KI strategisch einsetzen können, um die Innovationsfähigkeit zu steigern und menschliche Fähigkeiten zu stärken.

Literatur

• Amabile, T. M., & Pratt, M. G. (2016). The dynamic componential model of creativity and innovation in organizations. Research in Organizational Behavior, 36, 157–183.
• Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70.
• Grilli, L., & Pedota, M. (2024). Creativity and artificial intelligence: A multilevel perspective. Creativity and innovation management, 33(2), 234-247.
• Raisch, S., & Krakowski, S. (2021). Artificial intelligence and management: The automation–augmentation paradox. Academy of Management Review, 46(1), 192–210.

Der Aufbau und die Weiterentwicklung von IT-Kompetenzen gehören zu den zentralen Herausforderungen der digitalen Transformation. Unternehmen und Bildungseinrichtungen stehen vor der Aufgabe, Mitarbeitende und Studierende gezielt auf künftige Anforderungen vorzubereiten. Mit dem Einzug von Künstlicher Intelligenz entstehen unter anderem neue Möglichkeiten, Lernprozesse zu personalisieren, Kompetenzprofile automatisch zu analysieren oder Weiterbildungsmaßnahmen adaptiv anzupassen.

Die Nutzung von Künstlicher Intelligenz im Bereich des Aufbaus von IT-Kompetenzen reicht dabei von der Ideenkreierung für Lerninhalte bis hin zur automatisierten Erstellung von Lernmaterialien. Intelligente Systeme können Lernpfade vorschlagen, Wissenslücken identifizieren und individuelle Lernprozesse unterstützen. Dies eröffnet Chancen für eine stärker datengetriebene und zukunftsorientierte Kompetenzentwicklung.

Ziel dieser Seminararbeit ist es, mittels einer strukturierten Literaturrecherche ein Zukunftsszenario für die Anwendung von KI im Bereich des IT-Kompetenzaufbaus zu entwickeln. Dabei sollen aktuelle Ansätze analysiert, Potenziale und Grenzen aufgezeigt sowie zentrale Erfolgsfaktoren für den effektiven Einsatz von KI im Capability Building identifiziert werden. 

Literatur

• Holmes, W., Bialik, M., & Fadel, C. (2023). Artificial intelligence in education. 10.58863/20.500.12424/4276068.
• Zawacki-Richter, O., Marín, V., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1) 1-27. 10.1186/s41239-019-0171-0.
• Pedro, F., Subosa, M., Rivas, A., Valverde, P., et al. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. Working Papers on Education Policy. United Nations Educational, Scientific and Cultural Organization. gcedclearinghouse.org/sites/default/files/resources/190175eng.pdf
• World Economic Forum. (2023). The Future of Jobs Report 2023. Genf: WEF
• SFIA Foundation. (2024). SFIA 9: The global skills and competency framework. London: SFIA Foundation.
• Wimmer, B., Mayr, I., & Händler, T. (2024). Navigating the Landscape of Digital Competence Frameworks: A Systematic Analysis of AI Coverage and Adaptability. In CSEDU (1) (pp. 653-667).

Das IT-Consulting befindet sich im Wandel: Die zunehmende Verfügbarkeit und Leistungsfähigkeit von Künstlicher Intelligenz verändert die Art und Weise, wie Beratungsleistungen erbracht werden. Klassische Tätigkeiten wie die Analyse von Systemarchitekturen, die Optimierung von Geschäftsprozessen oder die Erstellung von Migrationsszenarien und der Programmierung selbst beruhten bisher stark auf menschlicher Expertise. Mit KI-gestützten Anwendungen eröffnen sich neue Möglichkeiten – von der automatisierten Datenanalyse über die Simulation komplexer Szenarien bis hin zur Generierung von Handlungsempfehlungen (Davenport & Miller, 2022).

Dabei entsteht ein Spannungsfeld zwischen Effizienzsteigerung durch Automatisierung und der weiterhin unverzichtbaren menschlichen Beratungskompetenz. KI kann Beraterinnen und Berater unterstützen, Routineaufgaben zu reduzieren, schneller zu fundierten Ergebnissen zu gelangen und Entscheidungsprozesse datenbasiert zu untermauern. Gleichzeitig wirft der Einsatz von KI die Frage auf, wie sich Rollenprofile, Wertschöpfungsmodelle und das Verhältnis von Mensch und Maschine im Consulting langfristig verändern werden.

Ziel dieser Seminararbeit ist es, auf Grundlage einer strukturierten Literaturrecherche zukünftige Anwendungsfelder von KI im IT-Consulting zu identifizieren. Darüber hinaus sollen Chancen und Risiken des KI-Einsatzes für Beratungsunternehmen sowie die Implikationen für Beratungsprozesse und Rollenprofile aufgearbeitet und kritisch reflektiert werden.

Literatur

• Davenport, T. H., & Miller, S. M. (2022). Working with AI: real stories of human-machine collaboration. MIT Press.
• Bezuidenhout, C., Abbas, R., Mehmet, M. & Heffernan, T. (2025). Artificial Intelligence in Professional Services: A Systematic Review and Foundational Baseline for Future Research. Journal Of Information & Knowledge Management. doi.org/10.1142/s0219649225500091
• Tredinnick, L. (2017). Artificial intelligence and professional roles. Business Information Review, 34(1), 37-41. doi.org/10.1177/0266382117692621

Anwendungen der Generativen Künstlichen Intelligenz (GKI) eröffnen neue Möglichkeiten der Ko-Kreation in der Interaktion zwischen Mensch und Maschine in Forschung und Praxis. Der zunehmende Einsatz von GKI bringt neue Kommunikations- und Interaktionsformen mit sich. Vor diesem Hintergrund hat sich das Prompt Engineering als zentrale Disziplin etabliert, um das Potenzial generativer KI-Technologien wirksam auszuschöpfen und insbesondere bei der Interaktion mit Sprachmodellen zielführende Abfragen zu entwickeln (Bozkurt, 2024). Prompt Engineering ist ein spezielles Gebiet innerhalb des Themenfelds der KI, das sich mit der Optimierung von Eingabeaufforderungen für Sprachmodelle wie z. B. ChatGPT, Claude oder Gemini beschäftigt (Koch, 2025). Es besteht aus verschiedenen Techniken zur zielgerichteten, präzisen und effektiven Formulierung von Prompts (Koch, 2025).

In diesem Zusammenhang lassen sich bestimmte Erfolgsfaktoren und Prinzipien unter verschiedenen kategoriellen Gesichtspunkten (z.B. strukturgebende oder datenbezogene Faktoren) für die mehrwertstiftende Gestaltung von Prompts unterscheiden.

Ziel dieser Seminararbeit ist es, mittels einer strukturieren Literaturrecherche, relevante Erfolgsfaktoren für die zielführende Gestaltung von Prompts zu identifizieren sowie diese systematisch aufzubereiten und zu sortieren. Darüber hinaus soll eine kritische Reflektion über die Vor- und Nachteile der Anwendung der identifizierten Faktoren beim Prompting in verschiedenen (Unternehmens- bzw. Anwendungs-)kontexten stattfinden.

Literatur

• Geroimenko, V. (2025). Key Principles of Good Prompt Design. In: The Essential Guide to Prompt Engineering. SpringerBriefs in Computer Science. Springer, Cham. doi.org/10.1007/978-3-031-86206-9_2
• Bozkurt, A. (2024). Tell Me Your Prompts and I Will Make Them True: The Alchemy of Prompt Engineering and Generative AI. Open Praxis, 16(2), pp. 111–118. DOI: doi.org/10.55982/ openpraxis.16.2.661
• Bsharat, S. M., Myrzakhan, A., & Shen, Z. (2023). Principled Instructions Are All You Need for Questioning LLaMA-1/2, GPT-3.5/4. arXiv. DOI: doi.org/10.48550/arXiv.2312.16171
• Koch, D., Kohne, A. & Brechbühler, N. (2025): Prompt Engineering im Unternehmen – eine Einführung. Wettbewerbsvorteile durch generative KI und Large Language Models. Wiesbaden: Springer Fachmedien Wiesbaden; Imprint Springer
• Wilbers, S., Espinosa-Leal, L., Sand, R. v. d., & Reiff-Stephan, J. (2023). Overall prompting effectiveness for optimising human-machine interaction in cyber-physical systems. Journal of Integrated Design and Process Science: Transactions of the SDPS, Official Journal of the Society for Design and Proc, 27(3-4), 211-220. doi.org/10.1177/10920617241295859

Fraud detection and prevention pose critical challenges for retail organizations, as in Germany, for example, 98 percent of all thefts are not detected and therefore not reported, leading to high financial losses (EHI Retail Institute, 2025). Artificial Intelligence (AI) technologies, such as pattern recognition or anomaly detection algorithms, offer promising solutions to identify fraudulent activities quickly and accurately (Akinyemi, 2025).

This seminar paper involves a structured literature review focused on the application of AI methods for fraud detection within retail contexts. Different AI techniques should be identified that are used to detect various types of retail fraud, such as payment fraud or inventory theft. Furthermore, the effectiveness and challenges (e.g., data quality, privacy concerns) should be discussed. Consequently, the aim of this seminar paper is to synthesize current academic knowledge on AI-driven fraud prevention strategies in retail, providing insights how AI may enhance security, reduce risks, and support operational integrity in retail businesses.

Literatur

• Akinyemi, O. (2025). Leveraging AI for Retail Security: Real-Time Detection and Prevention of theft and Fraud. SSRN Electronic Journal. doi.org/10.2139/ssrn.5236070
• Biceková, A., Onufráková, N., & Babič, F. (2024). Application of Classification Models on Fraud Detection in Retail (pp. 239–244). International Symposium on Computational Intelligence and Informatics. doi.org/10.1109/cinti63048.2024.10830907
• Chaudhary, S. (2025). AI in Retail: Advanced Technologies for Fraud and Loss Prevention. International Journal on Science and Technology, 16(1). doi.org/10.71097/ijsat.v16.i1.2912
• EHI Retail Institute (2025). Mehr Verluste durch Ladendiebstahl - EHI Retail Institute. Retrieved September 21, 2025 from www.ehi.org/presse/mehr-verlustedurch-ladendiebstahl-erhoehte-sicherheitsbudgets/.

AI-based agents – autonomous software systems capable of performing tasks and making decisions – are gaining increasing attention in retail as tools to improve operational efficiency, enhance customer experience, and support business decision-making. For example, recommendation agents can support customers with their weekly (online) grocery shopping by identifying preferences based on previous purchases and thereby providing personalized purchasing recommendations (Rohden and Espartel, 2024). While there are numerous potential adoption scenarios discussed in academic research, these cases are often fragmented and lack a comprehensive overview.

Accordingly, a structured literature review should be conducted to identify adoption scenarios of intelligent agents within the retail sector. The review will focus on identifying the key retail tasks (e.g., pricing, inventory management) that these AI-based agents are designed to support or automate as well as analyze the proposed type of agent (e.g., chatbot, recommendation engines, optimization agents). Additionally, challenges (e.g., fraudulent product returns) that arise when agents performing these tasks should be identified and discussed (Noble and Mende, 2023). 

Literatur

• Noble, S.M., Mende, M. The future of artificial intelligence and robotics in the retail and
  service sector: Sketching the field of consumer-robot-experiences. J. of the Acad. Mark. Sci. 51, 747–756 (2023). https://doi.org/10.1007/s11747-023-00948-0
• Petrescu, M., Krishen, A. S., Gironda, J. T., & Fergurson, J. R. (2024). Exploring AI technology and consumer behavior in retail interactions. Journal of Consumer Behaviour. https://doi.org/10.1002/cb.2386
• Rohden, S. F., & Espartel, L. B. (2024). Consumer reactions to technology in retail: choice uncertainty and reduced perceived control in decisions assisted by recommendation agents. Electronic Commerce Research, 24(2), 901–923. https://doi.org/10.1007/s10660-024-09808-7
• Signorile, R. (2002). Simulation of a multiagent system for retail inventory control: a case study. SIMULATION, 78(5), 304-311. doi.org/10.1177/0037549702078005552

Artificial Intelligence (AI) promises to enable personalized customer experiences at scale for online as well as offline retail channels. Among the most prominent AI applications are recommendation systems, which aim to tailor product offerings, content, and services based on customer preferences, behaviors, and contextual data. Therefore, AI-based recommendation systems are transforming retail by offering personalized product suggestions that enhance customer experience and drive purchasing behavior (Duwadi and Cautinho, 2024). These systems employ a variety of methods – ranging from collaborative filtering to hybrid models – that analyze customer data to deliver relevant and timely recommendations for both online and offline retail channels (Hwangbo et al., 2018). Therefore, this seminar paper should conduct a structured literature review focused on the different methods used in AI recommendation systems within retail, as well as their impact on customers (e.g., perceived relevance, trust, convenience). Additionally, challenges faced by recommendation systems (e.g., algorithmic bias, privacy concerns) should be discussed. 

Literatur

• Duwadi, S., & Cautinho, C. (2024). ChatGPT based recommendation system for retail shops. Procedia Computer Science, 237, 253–260. doi.org/10.1016/j.procs.2024.05.103
• Habil, S., El-Deeb, S., & El-Bassiouny, N. (2023). AI-Based Recommendation Systems: the ultimate solution for market prediction and targeting. In Springer eBooks (pp. 683–704). doi.org/10.1007/978-3-031-14961-0_30
• Hwangbo, H., Kim, Y. S., & Cha, K. J. (2018). Recommendation system development for fashion retail e-commerce. Electronic Commerce Research and Applications, 28, 94–101. doi.org/10.1016/j.elerap.2018.01.012
• Stalidis, G., Karaveli, I., Diamantaras, K., Delianidi, M., Christantonis, K., Tektonidis, D., Katsalis, A., & Salampasis, M. (2023). Recommendation Systems for e-Shopping: Review of Techniques for retail and Sustainable marketing. Sustainability, 15(23), 16151. doi.org/10.3390/su152316151

Diese Arbeit soll sich nicht an klassischen empirischen oder technischen Untersuchungen orientieren, sondern vielmehr die philosophische Dimension der Frage nach Intelligenz ergründen. Dabei steht die Auseinandersetzung mit der Natur menschlicher und künstlicher Intelligenz im Mittelpunkt. 

In dieser Arbeit sollen Studierende das Konzept des „Lernens“ aus einer philosophischen Perspektive untersuchen und es mit dem maschinellen Lernen vergleichen. Während menschliches Lernen oft mit Verstehen und Bewusstsein assoziiert wird, basiert maschinelles Lernen auf statistischen Algorithmen, die Muster erkennen, ohne dass ein „Verstehen“ im traditionellen Sinn stattfindet. Leitfragen für die Arbeit könnten sein:

• Was bedeutet „Lernen“ aus philosophischer Sicht, und wie unterscheidet es sich vom Prozess des maschinellen Lernens?
• Ist maschinelles Lernen wirklich eine Form des Lernens, oder handelt es sich lediglich um Mustererkennung?
• Können Maschinen jemals „verstehen“ oder handelt es sich bei maschinellem Lernen immer nur um eine Simulation von Lernen?
• Welche philosophischen Implikationen hat der Unterschied zwischen menschlichem und maschinellem Lernen für das Verständnis von Intelligenz und Kognition?

Diese Fragen dienen als Orientierung und können gerne weiter angepasst oder erweitert werden. Die Studierenden sind eingeladen, ihre eigene Perspektive zu entwickeln und mögliche Grenzen des maschinellen Lernens aus philosophischer Sicht zu reflektieren.

Literatur

• Jones, N. (2014). The learning machines. Nature, 505(7482), 146.
• Thagard, P. (1990). Philosophy and machine learning. Canadian Journal of Philosophy, 20(2), 261-276.
• Kaminski, A. (2020). Gründe geben. Maschinelles Lernen als Problem der Moralfähigkeit von Entscheidungen (pp. 151-174). Springer Fachmedien Wiesbaden.
• Rogalla, I. (2024). Maschinen lernen nicht!–„Machine learning “-Algorithmen entzaubert. In Künstliche Intelligenz, Mensch und Gesellschaft: Soziale Dynamiken und gesellschaftliche Folgen einer technologischen Innovation (pp. 131-150). Wiesbaden: Springer Fachmedien Wiesbaden.

Benchmarking has evolved from a management practice rooted in performance measurement and industrial competition to a strategic tool widely applied in information systems. It can be applied on multiple levels—from hardware benchmarking to organizational and strategic benchmarking—yet in this context the focus lies on processes, organizational structures, capabilities, and strategy. Initially used to compare organizational processes and identify best practices, benchmarking has expanded conceptually to include qualitative and quantitative dimensions of evaluation. Rather than centering on technical products or system performance alone, the emphasis is on how benchmarking supports organizations in assessing their processes, governance practices, and strategic alignment.  It enables organizations to measure technological progress, improve decision-making, and enhance competitiveness through data-driven comparisons.

The student’s task is to examine the historical emergence of benchmarking, its conceptual frameworks, and its transformation into a methodology for evaluating information systems. The research will begin by tracing the origins of benchmarking in management theory and practice, followed by an exploration of its methodological approaches. Special attention will be given to the role of benchmarking in process improvement, IT governance, organizational capability development, and strategic transformation. By analyzing case studies and theoretical models, the paper will provide a structured understanding of benchmarking as both a managerial and technological tool.

Literatur

• Bhutta, K. S., & Huq, F. (1999). Benchmarking–best practices: an integrated approach. Benchmarking: An International Journal, 6(3), 254-268.
• Ebner, K., Mueller, B., & Ahlemann, F. (2019). Understanding the success of strategic IT benchmarking—Exploring the role of the individual level. Information & Management, 56(5), 640-656.
• Lacity, M. C., & Hirschheim, R. (1995). Benchmarking as a strategy for managing conflicting stakeholder perceptions of information systems. The Journal of Strategic Information Systems, 4(2), 165-185.
• Sim, S. E., Easterbrook, S., & Holt, R. C. (2003, May). Using benchmarking to advance research: A challenge to software engineering. In 25th International Conference on Software Engineering, 2003. Proceedings. (pp. 74-83). IEEE.

Benchmarking in Information Systems (IS) involves structured, comparative assessments that can be applied on many levels—from technical performance to organizational processes and strategic capabilities. In this course, the focus lies on processes, organizational structures, capabilities, and strategy rather than on software, hardware, or products. Methods for benchmarking span from designing controlled scenarios and selecting performance metrics to collecting data, analyzing results, and iterating improvements. These methods are particularly valuable for evaluating organizational IS processes (e.g., IT service delivery, process efficiency, governance practices, and strategic alignment), and can be complemented by insights from more technical benchmarking approaches when needed.

The student’s task is to explore these methodologies: how are benchmarks created (benchmark test suites, scenario selection, KPI definition), how are they applied (internal vs. external benchmarking, performance vs. practice benchmarking), and how are outcomes analyzed and validated. The research will compare methods from organizational performance management, IS governance, and strategic management contexts, in addition to selected empirical studies, discussing strengths and limitations of each method. Finally, case studies or methodological guidelines will illustrate how benchmarking methods translate into actionable improvements for processes, organizational capabilities, and strategies.

Literatur

• Becher, K., Beck, M., & Strufe, T. (2019). An enhanced approach to cloud-based privacy-preserving benchmarking. In 2019 International Conference on Networked Systems (NetSys) (pp. 1-8). IEEE.
• Goncharuk, A. G., Lazareva, N. O., & Alsharf, I. A. (2015). Benchmarking as a performance management method. Polish Journal of Management Studies, 11.
• Hasselbring, W. (2021). Benchmarking as empirical standard in software engineering research. In Proceedings of the 25th International Conference on Evaluation and Assessment in Software Engineering (pp. 365-372).
• Town, J. S. (1995). Benchmarking and Performance Measurement.

Benchmarking has long been embraced in Information Systems (IS) as a method for measuring and improving processes, organizational capabilities, and strategic outcomes through comparison with best-in-class peers. It can be applied on different levels—from hardware benchmarking to strategic benchmarking—but in this context the emphasis lies on organizational and managerial objects rather than on technical products or systems. However, its application in IS is not without challenges and limitations. Critical issues include difficulties in ensuring data comparability, contextual misalignment between benchmark sources and target settings, methodological rigor lapses, and risks in replication and interpretation.

The student’s task is to systematically examine the limitations of benchmarking within IS—identifying conceptual, methodological, and practical pitfalls—and to critically evaluate how these challenges impact the validity and utility of benchmarking efforts. Special attention should be paid to challenges in process benchmarking, organizational benchmarking, capability assessments, and strategy benchmarking. Drawing on empirical studies, cross-domain critiques, and theoretical literature, the paper should provide recommendations for more robust and context-aware benchmarking approaches in IS.

Literatur

• Amaral, P., & Sousa, R. (2009). Barriers to internal benchmarking initiatives: an empirical investigation. Benchmarking: An International Journal, 16(4), 523-542.
• Ettorchi-Tardy, A., Levif, M., & Michel, P. (2012). Benchmarking: a method for continuous quality improvement in health. Healthcare policy, 7(4), e101.
• Lapão, L. V. (2015). The challenge of benchmarking health systems: is ICT innovation capacity more systemic than organizational dependent?. Israel journal of health policy research, 4(1), 43.
• van der Kouwe, E., Heiser, G., Andriesse, D., Bos, H., & Giuffrida, C. (2019, June). SoK: Benchmarking flaws in systems security. In 2019 IEEE European Symposium on Security and Privacy (EuroS&P) (pp. 310-325). IEEE.

As Information Systems (IS) increasingly operate in environments defined by Volatility, Uncertainty, Complexity, and Ambiguity (VUCA), traditional benchmarking approaches may fall short. In such dynamic and unpredictable conditions, organizations must adapt their processes, capabilities, and strategies rapidly, insights must be timely, and comparators may become outdated almost as soon as they are identified. Benchmarking can in principle be applied at different levels—from hardware benchmarking to strategic benchmarking—but in this context the focus is on organizational and managerial levels rather than on software, hardware, or products. This paper explores the future potential of benchmarking in IS, re-envisioned to support resilience, agility, and real-time learning. It will examine how benchmarking methodologies might evolve—through adaptive benchmarks, real-time analytics, crowdsourced metrics, AI-driven dynamic baselines, and domain-specific VUCA-aware measurement frameworks.

The student’s task is to critically assess existing literature on benchmarking in processes, organizational structures, capabilities, and strategies under dynamic IS contexts, propose evolutionary pathways and appropriate methodological modifications, and evaluate how VUCA conditions reshape the design and utility of benchmarking in IS.

Literatur

• Bennett, N., & Lemoine, G. J. (2014). What a difference a word makes: Understanding threats to performance in a VUCA world. Business horizons, 57(3), 311-317.
• Herbst, N., Krebs, R., Oikonomou, G., Kousiouris, G., Evangelinou, A., Iosup, A., & Kounev, S. (2016). Ready for rain? A view from SPEC research on the future of cloud metrics. arXiv preprint arXiv:1604.03470.
• Rdiouat, Y., Bahsani, S., Lakhdissi, M., & Semma, A. (2021). Measuring and improving information systems agility through the balanced scorecard approach. arXiv preprint arXiv:2109.07281.
• Zhan, J., Wang, L., Gao, W., & Ren, R. (2019). Benchcouncil's view on benchmarking ai and other emerging workloads. arXiv preprint arXiv:1912.00572.

Benchmarking in Information Systems (IS) can be supported by a wide range of tools, from structured assessment frameworks to advanced analytics platforms. These tools enable organizations to systematically compare processes, organizational structures, capabilities, and strategies against internal or external benchmarks. By automating data collection, providing visualization dashboards, and offering standardized metrics, tools make benchmarking more scalable, transparent, and actionable. At the same time, the diversity of tools raises questions about methodological quality, adaptability to different organizational contexts, and alignment with strategic objectives.

The student’s task is to identify and critically examine tools used for benchmarking in IS. This includes (1) surveying existing categories of tools (commercial, open-source, academic frameworks), (2) analyzing their functionalities and methodological underpinnings, (3) assessing strengths and limitations in relation to processes, organization, capabilities, and strategies, and (4) discussing future potential for tool support in benchmarking—such as integration with AI, real-time analytics, or VUCA-aware adaptability.

Literatur

• Abideen, D. K., Yunusa-Kaltungo, A., Cheung, C., & Manu, P. (2025). Development and evaluation of a maturity assessment tool for integrating building information modelling into operations and maintenance phase of buildings. Developments in the Built Environment, 21, 100619.
• Cognet, B., Pernot, J. P., Rivest, L., & Danjou, C. (2023). Systematic comparison of digital maturity assessment models. Journal of Industrial and Production Engineering, 40(7), 519-537.
• Ettorchi-Tardy, A., Levif, M., & Michel, P. (2012). Benchmarking: a method for continuous quality improvement in health. Healthcare policy, 7(4), e101.
• Willmington, C., Belardi, P., Murante, A. M., & Vainieri, M. (2022). The contribution of benchmarking to quality improvement in healthcare. A systematic literature review. BMC health services research, 22(1), 139.

Reference modeling has emerged as a critical concept in the design, deployment, and evolution of information systems (IS). It involves creating abstract, reusable models that capture core structures and behaviors common across a domain or class of systems. Rather than focusing on hardware or software architectures, the emphasis in this context lies on processes, organizational structures, and governance mechanisms. Reference models provide standardized blueprints—such as frameworks for process modeling, organizational governance, or enterprise management—that help organizations accelerate IS development, ensure interoperability, and support alignment with business goals. By offering conceptual consistency and facilitating knowledge transfer, reference models serve both as methodological tools and guiding frameworks in complex organizational contexts.

The student’s task is to explore the origins and evolution of reference modeling, its theoretical underpinnings, and its practical applications in IS. The research will begin by defining reference models and tracing their historical roots in management and organizational modeling. It will then analyze how reference modeling supports the design and implementation of IS via standardization, modularity, and reuse. Through comparison of major frameworks and case studies, the paper will yield a structured analysis of how reference modeling contributes to process improvement, organizational efficiency, governance, and strategic alignment in information systems.

Literatur

• Bernus, P., Noran, O., & Molina, A. (2015). Enterprise architecture: Twenty years of the GERAM framework. Annual Reviews in Control, 39, 83-93.
• Fettke, P., & Loos, P. (2003). Classification of reference models: a methodology and its application. Information systems and e-business management, 1(1), 35-53.
• Fettke, P., Loos, P., & Zwicker, J. (2005). Business process reference models: Survey and classification. In International Conference on Business Process Management (pp. 469-483). Berlin, Heidelberg: Springer Berlin Heidelberg.
• Frank, U. (2014). Multi-perspective enterprise modeling: foundational concepts, prospects and future research challenges. Software & Systems Modeling, 13(3), 941-962

Reference modelling in Information Systems empowers organizations to capture reusable processes, organizational structures, and governance practices that can be adapted for multiple scenarios. This topic explores the methods and methodological frameworks employed in constructing and applying reference models—ranging from automatic generation based on existing instances to manual design guided by design principles such as instantiation, aggregation, and specialization. The creation methods include identifying commonalities and variabilities, multi-view modeling, reuse-oriented frameworks, and holistic, traceability-focused approaches. Usage methods address how reference models are validated, customized (e.g., via configuration, instantiation), and evaluated for quality and fit in organizational and governance contexts.

The student’s task is to analyze these methods comprehensively: how they support efficient model creation, how they guide model adaptation and verification, and how they impact usability in process and organizational design within IS. The paper will compare techniques—for example, design-principle-driven creation versus automated extraction—and assess them through methodological critique and examples.

Literatur

• Gray, J., & Rumpe, B. (2021). Reference models: how can we leverage them?. Software and Systems Modeling, 20(6), 1775-1776.
• Sanchez-Puchol, F., Pastor-Collado, J. A., & Borrell, B. (2017). Towards an unified information systems reference model for higher education institutions. Procedia computer science, 121, 542-553.
• vom Brocke, J. (2009). Design principles for reference modelling: reusing information models by means of aggregation, specialisation, instantiation and analogy. In Innovations in Information Systems Modeling: Methods and Best Practices (pp. 269-296). IGI Global Scientific Publishing.
• Winter, R., & Schelp, J. (2006, April). Reference modeling and method construction: a design science perspective. In Proceedings of the 2006 ACM symposium on Applied computing (pp. 1561-1562).

Reference modeling has become a cornerstone in IS development, offering reusable, standardized models intended to streamline process design, organizational structures, and governance practices. Despite these benefits, reference modeling also faces limitations: misfit with specific organizational contexts, over-generalization, difficulty ensuring model relevance over time, and challenges in maintaining a balance between abstraction and practical applicability. This paper critically evaluates reference modeling in IS by examining conceptual weaknesses, methodological constraints, and practical challenges with a particular focus on organizational and governance applications rather than technical architectures.

The student’s task is to review empirical studies, theoretical critiques, and methodological frameworks in order to identify where reference modeling succeeds and where it falls short. The paper should provide a nuanced assessment and propose ways to make reference modeling more context-aware, adaptive, and effective in supporting processes, organizations, and governance within IS.

Literatur

• Becker, J., & Delfmann, P. (2007). Reference modeling. Heidelberg: Springer.
• Fettke, P., & Loos, P. (2007). Perspectives on reference modeling. In Reference modeling for business systems analysis (pp. 1-21). IGI Global Scientific Publishing.
• Frank, U. (2007). Evaluation of reference models. In Reference modeling for business systems analysis (pp. 118-140). IGI Global Scientific Publishing.
• Pousttchi, K. (2008). A modeling approach and reference models for the analysis of mobile payment use cases. Electronic Commerce Research and Applications, 7(2), 182-201.

In an era defined by volatility, uncertainty, complexity, and ambiguity (VUCA), Information Systems (IS) must adapt more dynamically than ever before. Reference modeling — traditionally a tool for standardization, reuse, and abstraction — needs to evolve to remain relevant. In this context, the focus lies on how reference models can support processes, organizational structures, and governance practices rather than technical system architectures. This topic investigates how reference models can become more resilient and agile under VUCA conditions, emphasizing continuous adaptation, real-time customization, and multi-context applicability.

The student’s task is to explore existing methods in reference modeling, identify gaps in dealing with VUCA challenges, and propose future directions: e.g., modular, adaptive, multi-layered reference models for processes, organizations, and governance integrated with real-time feedback loops. By drawing on literature about complexity-ready modeling, agile frameworks, and resilient systems, the paper aims to conceptualize next-generation reference modeling methodologies for dynamic IS environments.

Literatur

• Lechner, J., & Schlüter, N. (2023). Sustainable and resilient system development in a VUCA-World: an empirical study to develop a process orientated method of risk and technical change management in automotive industry. Proceedings of the Design Society, 3, 3255-3264.
• Mettler, T., & Rohner, P. (2009, May). Situational maturity models as instrumental artifacts for organizational design. In Proceedings of the 4th international conference on design science research in information systems and technology (pp. 1-9).
• vom Brocke, J. (2009). Design principles for reference modelling: reusing information models by means of aggregation, specialisation, instantiation and analogy. In Innovations in Information Systems Modeling: Methods and Best Practices (pp. 269-296). IGI Global Scientific Publishing.
• Winter, R., & Schelp, J. (2006, April). Reference modeling and method construction: a design science perspective. In Proceedings of the 2006 ACM symposium on Applied computing (pp. 1561-1562).

Reference modeling provides structured, reusable frameworks that capture best practices in processes, organizational structures, and governance. These models serve as blueprints that guide the design and configuration of Information Systems (IS), ensuring alignment between organizational needs and technical implementations. By offering standardized representations of organizational practices, reference models bridge the gap between abstract design concepts and operational IS solutions. This makes them a key instrument for translating strategic objectives and governance requirements into effective systems.

The student’s task is to investigate how reference models function as foundations for organizational and IS design. The research will analyze the transition “from the reference model to the running system,” focusing on methods for model configuration, adaptation, and implementation. Through case studies and methodological comparisons, the paper will evaluate how reference models contribute to consistency, efficiency, and alignment in IS-enabled organizational design.

Literatur

• Ascher, D., Heiland, E., Schnell, D., Hillmann, P., & Karcher, A. (2022). Methodology for holistic reference modeling in systems engineering. arXiv preprint arXiv:2211.11453.
• David, A. (2001). Models implementation: a state of the art. European Journal of Operational Research, 134(3), 459-480.
• Nilsen, P. (2020). Making sense of implementation theories, models, and frameworks. In Implementation Science 3.0 (pp. 53-79). Cham: Springer International Publishing.
• vom Broke, J., & Thomas, O. (2006). Reference modeling for organizational change: applying collaborative techniques for business engineering.

Maturity models have become influential frameworks for assessing, guiding, and improving organizational capabilities within the Information Systems (IS) domain. Building on early models such as the Capability Maturity Model (CMM), maturity models structure development across sequential levels, each reflecting increasing process sophistication and performance. In IS, these models support organizational growth in areas like process maturity, digital transformation, IT governance, and service delivery. They offer both diagnostic capability—by identifying current maturity—and prescriptive guidance—by suggesting actions for improvement.

The student’s task is to explore the historical evolution and theoretical foundations of maturity models, particularly in the context of IS. The research will compare foundational models (e.g., CMM/CMMI) with more recent, domain-specific frameworks such as IS maturity and performance measurement models. By analyzing empirical studies and literature reviews, the paper will evaluate how maturity models are constructed (levels, dimensions, validation) and how they influence IS strategy, capability development, and continuous improvement.

Literatur

• Carvalho, J. V., Rocha, Á., & Abreu, A. (2016). Maturity models of healthcare information systems and technologies: a literature review. Journal of medical systems, 40(6), 131.
• Pöppelbuß, J., & Röglinger, M. (2011). What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management.
• Proença, D., & Borbinha, J. (2016). Maturity models for information systems-a state of the art. Procedia Computer Science, 100, 1042-1049.
• Stoiber, C., Stöter, M., Englbrecht, L., Schönig, S., & Häckel, B. (2023). Keeping your maturity assessment alive: A method for the continuous tracking and assessment of organizational capabilities and maturity. Business & Information Systems Engineering, 65(6), 703-721.

Maturity models serve as structured frameworks enabling organizations to assess and improve capabilities in various IS domains (e.g., IT management, analytics, process maturity). This topic explores methodological approaches for designing, validating, and applying maturity models within the IS context. It covers steps including problem definition, construct identification, level and dimension design, validation (e.g., expert input, empirical testing), and application procedures (e.g., assessment method, scoring, benchmarking).

The student’s task is to dissect how maturity models are methodologically constructed and used in IS: what design principles guide them, how rigor is ensured, what forms of validation exist, and how they are deployed for diagnostics or strategic improvement. By comparing frameworks and methodological guidance, the paper will critically assess best practices and challenges in maturity modeling for IS.

Literatur

• Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT management: A procedure model and its application. Business & information systems engineering, 1(3), 213-222.
• Mettler, T., & Ballester, O. (2021, December). Maturity Models in Information Systems: A Review and Extension of Existing Guidelines. In ICIS.
• Pöppelbuß, J., & Röglinger, M. (2011). What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management.
• Proença, D., & Borbinha, J. (2016). Maturity models for information systems-a state of the art. Procedia Computer Science, 100, 1042-1049.

Maturity models are ubiquitous in Information Systems (IS) research and practice, offering structured frameworks to assess and guide organizational capabilities. While they promise clarity and actionable roadmaps, maturity models are also subject to growing criticism regarding methodological rigor, contextual validity, and sustainability of their outcomes. This paper undertakes a critical assessment of maturity models in IS, spotlighting issues such as static snapshots versus continuous change, poor validation, over-generalization, and limited longitudinal evidence of impact. 

The student’s task is to review empirical and theoretical critiques, evaluate the strengths and weaknesses of current approaches, and suggest strategies for enhancing the validity, adaptability, and long-term relevance of maturity models in IS contexts.

Literatur

• Adekunle, S. A., Aigbavboa, C., Ejohwomu, O., Ikuabe, M., & Ogunbayo, B. (2022). A critical review of maturity model development in the digitisation era. Buildings, 12(6), 858.
• Mettler, T., & Ballester, O. (2021, December). Maturity Models in Information Systems: A Review and Extension of Existing Guidelines. In ICIS.
• Proença, D., & Borbinha, J. (2016). Maturity models for information systems-a state of the art. Procedia Computer Science, 100, 1042-1049.
• Stoiber, C., Stöter, M., Englbrecht, L., Schönig, S., & Häckel, B. (2023). Keeping your maturity assessment alive: A method for the continuous tracking and assessment of organizational capabilities and maturity. Business & Information Systems Engineering, 65(6), 703-721.

Organizations increasingly manage Information Systems (IS) in environments characterized by volatility, uncertainty, complexity, and ambiguity (VUCA). While traditional maturity models provide structured roadmaps for capability assessment and improvement, they often assume stable contexts and linear progressions. In a VUCA world, such assumptions are challenged: models must adapt to shifting conditions, support resilience, and enable continuous reassessment. Artificial Intelligence (AI) offers new opportunities to enhance maturity assessments by enabling real-time data integration, predictive analytics, dynamic benchmarking, and adaptive feedback loops that respond to volatile environments.

The student’s task is to critically analyze how maturity models in IS can evolve to remain relevant under VUCA conditions, with a special emphasis on AI support. The research will begin by outlining the foundations of IS maturity models and their methodological design. It will then assess their limitations in dynamic, unpredictable environments and explore how AI-enabled techniques—such as automated data collection, continuous monitoring, predictive modeling, and context-sensitive adaptation—can address these limitations. By synthesizing insights from existing literature, the paper will propose how AI-supported maturity models can contribute to agility, resilience, and strategic decision-making in VUCA contexts.

Literatur

• Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT management: A procedure model and its application. Business & information systems engineering, 1(3), 213-222.
• Bennett, N., & Lemoine, G. J. (2014). What a difference a word makes: Understanding threats to performance in a VUCA world. Business horizons, 57(3), 311-317.
• Pöppelbuß, J., & Röglinger, M. (2011). What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management.
• Proença, D., & Borbinha, J. (2016). Maturity models for information systems-a state of the art. Procedia Computer Science, 100, 1042-1049.

Maturity models provide structured frameworks that describe how organizations can systematically develop their processes, capabilities, and governance practices over time. They serve not only as diagnostic tools but also as guiding instruments for organizational and IS design. By identifying maturity levels and mapping pathways for improvement, maturity models help organizations translate abstract assessments into concrete interventions, which can ultimately be implemented in operational systems. In this way, they act as a bridge from conceptual evaluation to running IS solutions that align with organizational objectives and strategies.

The student’s task is to investigate how maturity models function as foundations for organizational and IS design. The research will analyze the transition “from the maturity model to the running system,” focusing on how maturity levels inform the design, configuration, and implementation of IS-enabled processes and structures. Through case studies and methodological comparisons, the paper will evaluate how maturity models contribute to organizational efficiency, governance, and strategic alignment in practice.

Literatur

• Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing maturity models for IT management: A procedure model and its application. Business & information systems engineering, 1(3), 213-222.
• Pöppelbuß, J., & Röglinger, M. (2011). What makes a useful maturity model? A framework of general design principles for maturity models and its demonstration in business process management.
• Proença, D., & Borbinha, J. (2016). Maturity models for information systems-a state of the art. Procedia Computer Science, 100, 1042-1049.
• Rosemann, M., & De Bruin, T. (2005). Towards a business process management maturity model. In ECIS 2005 proceedings of the thirteenth European conference on information systems (pp. 1-12). Verlag and the London School of Economics.

Mit der zunehmenden Integration von generativer KI in verkörperte Systeme (z.B. humanoide Roboter, tierähnliche Roboter) rückt die Frage nach der Akzeptanz solcher Technologien in den Vordergrund. Ein zentrales Phänomen ist hierbei der sogenannte Uncanny Valley-Effekt, der beschreibt, dass Systeme, die Menschen/Tieren sehr ähnlich sehen oder sich ähnlich verhalten, bei Beobachtenden ambivalente Gefühle bis hin zu Ablehnung auslösen.

Das Ziel dieser Arbeit ist es, systematisch zu untersuchen, welche Faktoren das Auftreten oder die Abschwächung des Uncanny Valley-Effekts bei embodied GenAI beeinflussen. Da jedoch die Forschung zu embodied GenAI noch am Anfang steht, fokussiert diese Arbeit zunächst darauf die bisherige Forschung zu den zwei Kerndimensionen von embodied GenAI zu untersuchen: Embodiment (Roboter) und Intelligenz (Generative AI).

Forschungsfragen: 

• Welche Faktoren tragen laut bisheriger Forschung zum Auftreten des Uncanny Valley-Effekts bei Robotern bei?
• Welche Faktoren tragen laut bisheriger Forschung zum Auftreten des Uncanny Valley-Effekts bei Generative AI bei?
• Welche Implikationen ergeben sich daraus für das Design embodied GenAI-Systeme, um den Uncanny Valley-Effekt möglichst gering zu halten?

Methodik:

Die Arbeit basiert auf einer Systematic Literature Review (SLR). Es werden Forschungsarbeiten zu zwei Bereichen systematisch erfasst, kategorisiert und analysiert:

• Uncanny Valley im Kontext von Robotik
• Uncanny Valley im Kontext von Generative AI

Auf dieser Basis werden Faktoren herausgearbeitet, die für embodied GenAI-Systeme relevant sind, und in einem konzeptionellen Modell integriert, das Designempfehlungen für akzeptanzfördernde Systeme ableitet.

Erwarteter Beitrag:

Die Arbeit liefert eine theoriegeleitete Übersicht zu Akzeptanzbarrieren und -förderern für embodied GenAI im Kontext des Uncanny Valley. Daraus ergeben sich Gestaltungsempfehlungen, wie Systeme entwickelt werden können, die soziale Nähe fördern, aber zugleich die kritische Schwelle zur „Unheimlichkeit“ vermeiden.

Literatur

• Mori, M., MacDorman, K. F., & Kageki, N. (2012). The uncanny valley [from the field]. IEEE Robotics & automation magazine, 19(2), 98-100.
• Saygin, A. P., Chaminade, T., Ishiguro, H., Driver, J., & Frith, C. (2012). The thing that should not be: predictive coding and the uncanny valley in perceiving human and humanoid robot actions. Social cognitive and affective neuroscience, 7(4), 413-422.
• Fitzpatrick, S., Byrne, A. K., Headley, A., Sanders, J. G., Petrie, H., Jenkins, R., & Baker, D. H. (2025). Neural correlates of the uncanny valley effect for robots and hyper-realistic masks. PloS one, 20(3), e0311714.
• Kang, H., Santos, T. F. D., Moussa, M. B., & Magnenat-Thalmann, N. (2025). Mitigating the Uncanny Valley Effect in Hyper-Realistic Robots: A Student-Centered Study on LLM-Driven Conversations. arXiv preprint arXiv:2503.16449.
• Schwind, V., Leicht, K., Jäger, S., Wolf, K., & Henze, N. (2018). Is there an uncanny valley of virtual animals? A quantitative and qualitative investigation. International Journal of Human-Computer Studies, 111, 49-61.
• Zhang, J., Li, S., Zhang, J. Y., Du, F., Qi, Y., & Liu, X. (2020, July). A literature review of the research on the uncanny valley. In International conference on human-computer interaction (pp. 255-268). Cham: Springer International Publishing.
• Rosenthal-Von Der Pütten, A. M., & Krämer, N. C. (2014). How design characteristics of robots determine evaluation and uncanny valley related responses. Computers in Human Behavior, 36, 422-439.

Während klassische Chatbots primär auf textbasierter Kommunikation beruhen, eröffnet die digitale Verkörperung von Generativer KI neue Dimensionen der Interaktion. Statt lediglich über Text oder Sprache zu kommunizieren, erhalten KI-Systeme einen virtuellen Körper – sei es nur ein Gesicht, ein Torso oder ein vollständiger Avatar. Mit Mimik und Gestik kann so eine deutlich natürlichere und intuitivere Form des Austauschs entstehen, die über reine Textdialoge hinausgeht.

Diese Seminararbeit untersucht das Konzept des digitalen Embodiments von Generativer KI. Der Fokus liegt auf der Frage, in welchen Anwendungsszenarien der Einsatz digitaler Avatare einen Mehrwert gegenüber klassischen Chatbots bietet, wo die Grenzen liegen und welche Auswirkungen dies auf Nutzerinnen und Nutzer hat.

Von besonderem Interesse sind dabei folgende Themenfelder:

• Vorteile gegenüber rein textbasierten Chatbots: Welche qualitativen Unterschiede in der Kommunikation entstehen durch Mimik, Gestik und nonverbale Ausdrucksformen? Wie verändert sich dadurch die Wahrnehmung von Natürlichkeit, Glaubwürdigkeit und Nähe?
• Anwendungsszenarien: In welchen Bereichen lohnt sich digitales Embodiment? Diskutiert werden Szenarien wie virtuelle Patienten in der medizinischen Ausbildung, realistische Simulationen menschlichen Verhaltens in Trainings- und Schulungsumgebungen, interaktive Bildungsanwendungen, digitale Beratung oder auch immersive Kundenerlebnisse. Zugleich soll betrachtet werden, in welchen Kontexten ein rein text- oder sprachbasierter Ansatz weiterhin ausreichend oder gar vorzuziehen ist.
• Einfluss auf den Menschen: Welche psychologischen Effekte entstehen durch den Einsatz digital verkörperter KI? Wie wirken sich Mimik und Gestik auf Vertrauen, Empathie und Akzeptanz aus? Welche Chancen ergeben sich, aber auch welche Risiken, etwa im Hinblick auf Täuschung, Manipulation oder emotionale Überforderung?
• Weitere Potenziale: Neben klassischen Interaktionsszenarien soll auch die Rolle digitaler Avatare in immersiven virtuellen Räumen untersucht werden, etwa in der Simulation komplexer sozialer Situationen, bei der Entwicklung von Serious Games oder in der psychologischen Forschung, wo digitale Verkörperung neue Experimentierräume eröffnet.

Die Methodik der Arbeit basiert auf einer systematischen Literaturanalyse. Nach einer initialen Einarbeitung in die Thematik wird anhand eines gegebenen Suchstrings in enger Abstimmung sukzessive eine Suchstrategie erarbeitete (Datenbanken, Erweiterung des Suchstrings um relevante Schlüsselbegriffe). Anschließend erfolgt eine strukturierte Datenbanksuche, um den aktuellen Forschungsstand umfassend zu erfassen und kritisch auszuwerten.

Ziel der Seminararbeit ist es, ein fundiertes Bild über die Chancen und Herausforderungen digital verkörperter Generativer KI zu gewinnen. Damit wird ein Beitrag geleistet, die Weiterentwicklung von KI-Anwendungen nicht nur technisch, sondern auch in ihrer Wirkung auf den Menschen besser zu verstehen – und die Frage zu beantworten, wann sich digitale Avatare tatsächlich lohnen.

Literatur

• Gilbert, R. L., & Forney, A. (2015). Can avatars pass the Turing test? Intelligent agent perception in a 3D virtual environment. International Journal of Human-Computer Studies, 73, 30-36.
• Lippitsch, A., Steglich, J., Ludwig, C., Kellner, J., Hempel, L., Stoevesandt, D., & Thews, O. (2024). Development and evaluation of a software system for medical students to teach and practice anamnestic interviews with virtual patient avatars. Computer Methods and Programs in Biomedicine, 244, 107964.
• Priya, B., & Sharma, V. (2023). Exploring users' adoption intentions of intelligent virtual assistants in financial services: An anthropomorphic perspectives and socio-psychological perspectives. Computers in Human Behavior, 148, 107912.
• Qin, H. X., & Hui, P. (2023, July). Empowering the metaverse with generative ai: Survey and future directions. In 2023 IEEE 43rd international conference on distributed computing systems workshops (ICDCSW) (pp. 85-90). IEEE.
• Seymour, M., Yuan, L., Riemer, K., & Dennis, A. R. (2025). Less artificial, more intelligent: Understanding affinity, trustworthiness, and preference for digital humans.  Information  Systems Research, 36(2), 1096-1128.
• Yi, A. Y., & Huey, T. J. (2025). AI Avatars in Action: A Review of Applications, Challenges, and Future Potential. Grenze International Journal of Engineering & Technology (GIJET), 11.

Embodied Generative AI wird in den kommenden Jahren erheblichen Einfluss auf unsere Gesellschaft haben. Einsatzfelder reichen von Haushaltsunterstützung über Produktion, Sicherheit und Überwachung bis hin in das Gesundheitswesen und autonomen Fahrzeugen. Mit dieser zunehmenden Verbreitung wächst die Bedeutung, Vertrauen in solche Systeme aufzubauen. Vertrauen ist ein zentrales Element für Akzeptanz und effektive Nutzung. Es beeinflusst maßgeblich, wie Menschen mit Embodied Generative AI interagieren und inwiefern sie bereit sind, deren Entscheidungen zu akzeptieren. Die Herausforderung besteht darin, Systeme so zu gestalten und zu kommunizieren, dass sie als vertrauenswürdig wahrgenommen werden, ohne blindes Vertrauen zu fördern, das Risiken ausblendet.

Embodied Generative AI ist ein hybrides Technologiekonstrukt, das sowohl auf Fortschritten der Generativen KI als auch auf Entwicklungen in der Robotik basiert. Diese Kombination bringt neue Faktoren hervor, die das Vertrauen der Nutzer beeinflussen.

Ziel der Seminararbeit ist es, auf Basis einer systematischen Literaturanalyse die aktuelle Forschungslandschaft zu Vertrauensfaktoren aus den Bereichen Generative AI und Robotik zu untersuchen. Dabei sollen Faktoren identifiziert werden, die für die jeweiligen Technologien relevant sind und in Kombination das Vertrauen in Embodied Generative AI prägen. Ergänzend kann ein Blick auf Akzeptanztheorien, die bereits auf ähnliche Technologien angewendet oder weiterentwickelt wurden, wertvolle Einsichten liefern.

Die Ergebnisse sollen in einer Taxonomie münden, die unterschiedliche Vertrauensaspekte strukturiert erfasst und einordnet.

Literatur

• Aoki, N. (2021). The importance of the assurance that “humans are still in the decision loop” for public trust in artificial intelligence. Computers in Human Behavior, 114.
• Berente, N., Gu, B., Recker, J., Santhanam, R. (2021). Managing Artificial Intelligence. MIS Quarterly, 45(3).
• Georganta, E. & Ulfert, A.-S. (2024). My colleague is an AI! Trust differences between AI and human teammates. Team Performance Management.
• Fahnenstich, H., Rieger, T., Roesler, E. (2024). Trusting under risk – comparing human to AI decision support agents. Computers in Human Behavior, 153.
• Lukyanenko, R., Maass, W. & Storey, V.C. (2022). Trust in artificial intelligence: From a Foundational Trust Framework to emerging research opportunities. Electronic Markets, 32.
• Schaschek, M. & Engel, S. (2023). Measuring Trustworthiness of AI Systems: A Holistic Maturity Model. ICIS 2023 Proceedings.
• Yang, R., Wibowo, S. (2022). User trust in artificial intelligence: A comprehensive conceptual framework. Electronic Markets, 32.
• Marangunić, N. & Granić, A. (2015). Technology acceptance model: A literature review from 1986 to 2013. Universal Access in the Information Society, 14(1).
• Chang, A. (2012). UTAUT and UTAUT 2: A review and agenda for future research. Journal the WINNERS, 13(2).
• Cheng, M., Yin, C., Zhang, J., Nazarian, S., Deshmukh, J., & Bogdan, P. (2021). A general trust framework for multi-agent systems. AAMAS Proceedings.

Digitale Plattformen sind zentrale Treiber moderner Geschäftsmodelle. Ob E-Commerce (Amazon, Zalando etc.), FinTech (PayPal, Stripe), Dienstleistungen und Mobilität (Uber, Airbnb etc.) oder Innovations- und App-Ökosysteme (z. B. Apple App Store) – sie koordinieren Wertschöpfung über lose gekoppelte, unabhängige Akteure hinweg. Schlüsseltechnologien wie Cloud- und Edge-Computing, Blockchain, Open-Banking-APIs und datengetriebene Personalisierung unterstützen diese Prozesse.

Diese Verlagerung der Wertschöpfung bedarf einer gezielten Orchestrierung der Ökosystemteilnehmer, um das Leistungsversprechen des Ökosystems zu sichern. Da Komplementäre nicht der hierarchischen Kontrolle der Plattformbetreiber unterstehen, steuern diese das Ökosystem über strategische Koordinations- und Governance-Mechanismen statt über direkte Kontrolle. Schwache Governance kann strukturelle Lücken schaffen, Wertschöpfung mindern und Netzwerkeffekte schwächen. Trotz hoher praktischer Bedeutung ist der wissenschaftliche Diskurs hierzu begrenzt. Die Arbeit schließt diese Lücke durch eine systematische Literaturrecherche und entwickelt darauf aufbauend eine Forschungsagenda.

Forschungsfragen:

• RQ1: Was sind die zentralen Themen der IS-Forschung im Kontext von Plattformen bzw. Ökosystemen und Governance?
• RQ2: Welche Aspekte der Plattform Governance sollten künftig erforscht werden?

Methodisches Vorgehen: Systematische Literaturrecherche [RW1] in führenden Fachzeitschriften (Basket of Eight) und auf Konferenzen (ICIS, ECIS, PACIS, AMCIS, HICSS).

Zielsetzung: Die Arbeit verfolgt das Ziel, den aktuellen Stand der Forschung systematisch aufzuarbeiten, zentrale Forschungströme der intraorganisationalen Governance zu identifizieren und diese im Rahmen eines wissenschaftlichen Diskurses darzulegen. Ein besonderes Augenmerk gilt dabei den theoretischen Perspektiven, aus denen Governance-Mechanismen bislang untersucht wurden. Darauf aufbauend wird eine Forschungsagenda entwickelt, die potenzielle zukünftige Forschungsschwerpunkte systematisch zusammenfasst.

 [RW1]Suchstring ("Platform" OR "Ecosystem") AND ("governance")

Literatur

• Hein, Andreas (2020): Digital Platform Ecosystems: Emergence and Value Co-Creation Mechanisms. Technischen Universität München, München.
• Jacobides, Michael G.; Cennamo, Carmelo; Gawer, Annabelle (2018): Towards a theory of ecosystems. In Strat. Mgmt. J. 39 (8), pp. 2255–2276. DOI: 10.1002/smj.2904.
• Tiwana, Amrit (2014): Platform Ecosystems: Elsevier, Kapitel 6
• Hein, Andreas; Schreieck, Maximilian; Wiesche, Manuel; Krcmar, Helmut (2016): Multiple-Case Analysis on Governance Mechanisms of Multi-Sided Platforms. In : Multikonferenz Wirtschaftsinformatik. Ilmenau, Germany.
• Staub, Nicola; Haki, Kazem; Aier, Stephan; Winter, Robert (2022): Governance Mechanisms in Digital Platform Ecosystems: Addressing the Generativity-Control Tension. In Communications of the Association for Information Systems 51 (1), pp. 906–939. DOI: 10.17705/1CAIS.05137.
• Bandara, Wasana; Furtmueller, Elfi; Gorbacheva, Elena; Miskon, Suraya; Beekhuyzen, Jenine (2015): Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. In CAIS 37. DOI: 10.17705/1CAIS.03708.
• vom Brocke, Jan; Simons, Alexander; Niehaves, Björn; Riemer, Kai; Plattfaut, Ralf; Cleven, Anne (2009): Reconstructing the giant: On the importance of rigour in documenting the literature search process. In : 17th European Conference on Information Systems (ECIS 2009). Verona, Italy, pp. 2206–2217. Available online at aisel.aisnet.org/ecis2009/161.
• Webster, Jane; Watson, Richard T. (2002): Analyzing the Past to Prepare for the Future: Writing a Literature Review. In MIS Quarterly 26 (2), pp. xiii–xxiii.

Die Entwicklung fortgeschrittener Agentic IS, wie bspw. KI-Agenten in der Softwareentwicklung, autonome Fahrzeuge oder diagnostische Assistenten in der Medizin, sorgt zunehmend für Herausforderungen bei der Zuordnung von Verantwortlichkeiten. Auf formaler Ebene versuchen Regularien wie der EU AI Act rechtliche Grundlagen zu schaffen, jedoch erweist sich die eindeutige Zuordnung zwischen Nutzer, Entwickler des KI-Systems oder anderen Akteuren für Entscheidungen und deren Konsequenzen als problematisch. Gleichzeitig entwickelt jeder Nutzer eine individuelle, wahrgenommene Verantwortlichkeit, die bspw. durch persönliche Erfahrungen, das Vertrauen in das System und das Verständnis der Technologie geprägt wird. Diese subjektiv empfundene Verantwortung kann jedoch erheblich von den formalen Verantwortlichkeitsstrukturen abweichen, da die gestiegene Autonomie und Intransparenz von Agentic IS das Gefühl der Kontrolle und Zuständigkeit der menschlichen Akteure beeinflusst. 

Ziel dieser Seminararbeit ist es, mittels einer systematischen Literaturrecherche die unterschiedlichen Ausprägungen (bspw. formal vs. wahrgenommen) und Perspektiven (bspw. Verantwortlichkeit vs. Rechenschaftspflicht) sowie deren Einflussfaktoren und Auswirkungen kritisch zu analysieren.

Literatur

• Bartsch, S.C., Nguyen, L.H., Schmidt, JH. et al. (2025): The Present and Future of Accountability for AI Systems: A Bibliometric Analysis. Inf Syst Front. doi.org/10.1007/s10796-025-10636-9
• Leonardi, P. M. (2025): Homo agenticus in the age of agentic AI: Agency loops, power displacement, and the circulation of responsibility. Information and Organization, 35(3), 100582. doi:10.1016/j.infoandorg.2025.100582
• León, G. A., Chiou, E. K., & Wilkins, A. (2020): Accountability Increases Resource Sharing: Effects of Accountability on Human and AI System Performance. International Journal of Human–Computer Interaction, 37(5), 434–444. doi.org/10.1080/10447318.2020.1824695
• Nguyen, Long Hoang; Lins, Sebastian; Renner, Maximilian; and Sunyaev, Ali (2024): Unraveling the Nuances of AI
• Accountability: A Synthesis of Dimensions Across Disciplines. ECIS 2024 Proceedings.
• Tong, Jiawei; Marx, Julian; Cui, Tingru; and Turel, Ofir (2025): BRIDGING THE AI ACCOUNTABILITY GAP: THE ROLE OF COLLABORATION PARADIGMS AND REASON RESPONSIVENESS. ECIS 2025 Proceedings.

Eine realistische Einschätzung der eigenen Fähigkeiten sowie der Fähigkeiten des KI-Systems ist fundamental für effektive Mensch-KI-Zusammenarbeit. Dieses "Metawissen" bezeichnet die Fähigkeit, sowohl die eigenen Kapazitäten als auch die der kollaborativen Partner präzise zu bewerten und darauf basierend informierte Entscheidungen über Aufgabenverteilung und Verantwortlichkeiten zu treffen. In Mensch-KI-Teams ermöglicht Metawissen den Beteiligten zu entscheiden, wann sie mit einem Agentic IS zusammenarbeiten sollten und wie sie die Ausgaben des jeweils anderen interpretieren, akzeptieren oder in Frage stellen. Dieses Metawissen über kognitive Prozesse – sowohl die eigenen als auch die des Agentic IS – ermöglicht nicht nur eine strategische Aufgabenteilung, sondern auch eine kontinuierliche Überwachung und Anpassung der gemeinsamen Arbeitsweise. Während Über- oder Unterschätzung der eigenen Fähigkeiten (oder auch die des Agentic IS) zu suboptimaler Zusammenarbeit führen (bspw. durch blindes Vertrauen oder grundsätzliche Ablehnung von Agentic IS), unterstützt ausreichendes Metawissen eine ausgewogene Arbeitsteilung.

Ziel dieser Seminararbeit ist es, mittels systematischer Literaturrecherche die Auswirkungen und Ausprägungen metakognitiver Fähigkeiten in Mensch-KI-Teams, deren Einflussfaktoren sowie Governance-Ansätze für die Entwicklung und Messung dieser Kompetenzen zu untersuchen.

Literatur

• Colville, S., & Ostern, N. (2024): Trust and Distrust in GAI Applications: The Role of AI Literacy and Metaknowledge. ICIS 2024 Proceedings.
• Fügener, A., Grahl, J., Gupta, A., & Ketter, W. (2021): Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research, 33(2), 678–696. doi:10.1287/isre.2021.1079
• Han, Y., & Dunning, D. (2024): Metaknowledge of Experts Versus Nonexperts: Do Experts Know Better What They Do and Do Not Know? Journal of Behavioral Decision Making, 37(2), e2375. doi:10.1002/bdm.2375
• Jussupow, E., Spohrer, K., Heinzl, A., & Gawlitza, J. (2021): Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence. Information Systems Research, 32(3), 713–735. doi:10.1287/isre.2020.0980
• Strunk, J., Banh, L., Nissen, A., Strobel, G., & Smolnik, S. (2024): To Delegate or Not to Delegate? Factors Influencing Human-Agentic IS Interaction. ICIS 2024 Proceedings.

Die erfolgreiche Einführung von Generativer KI in Organisationen erfordert bestimmte Fähigkeiten, die über verschiedene Dimensionen hinweg entwickelt werden müssen. Dazu zählen etwa die Perspektive der Menschen und Mitarbeitenden, Prozesse, Technologien, Daten und der Organisation als solches, welche nötige Fähigkeiten und Strukturen erfordern, die jedoch zur Weiterentwicklung erstmal bewertet werden müssen. Während allgemeine KI-Reifegradmodelle bereits Orientierung bieten, spiegeln sie die spezifischen Eigenschaften von GenAI – wie ihre interaktive und kreative Nutzung – nur unzureichend wider.

Ziel dieser Seminararbeit ist es daher, im Rahmen einer systematischen Literaturrecherche (SLR) wissenschaftliche Arbeiten zu analysieren, die sich mit organisationalen Fähigkeiten und Reifegradmodellen im Kontext von KI oder speziell GenAI befassen. Auf dieser Grundlage sollen zentrale Fähigkeiten und Merkmale herausgearbeitet werden, mit denen sich der Reifegrad einer Organisation im Umgang mit GenAI bestimmen lässt. Die Ergebnisse sollen ein vertieftes Verständnis der „GenAI Adoption Journey“ ermöglichen und Organisationen bei der systematischen Bewertung und Weiterentwicklung ihrer GenAI-Fähigkeiten unterstützen.

Literatur

• Banh, L. (2025). Developing a Generative AI Maturity Model for Supporting the Organizational Adoption Journey. Wirtschaftsinformatik 2025 Proceedings.
• Becker, J., Knackstedt, R., & Pöppelbuß, J. (2009). Developing Maturity Models for IT Management. Business & Information Systems Engineering, 1(3), 213–222. doi.org/10.1007/s12599 -009-0044-5
• Dennis, A. R., Lakhiwal, A., & Sachdeva, A. (2023). AI Agents as Team Members: Effects on Satisfaction, Conflict, Trustworthiness, and Willingness to Work With. Journal of Management Information Systems, 40(2), 307–337. doi.org/10.1080/07421222.2023.2196773
• Hansen, H. F., Lillesund, E., Mikalef, P., & Altwaijry, Ν. (2024). Understanding Artificial Intelligence Diffusion through an AI Capability Maturity Model. Information Systems Frontiers. doi.org/10.1007/s10796-024-10528-4
• Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. The Academy of Management Review, 46(1),192–210. https://doi.org/10.5465/amr.2018.0072
• Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects. Business Horizons. https://doi.org/10.1016/j.bushor.2024.04.009 

Generative KI verändert zunehmend die Art, wie Menschen Aufgaben bearbeiten und Entscheidungen treffen. Nutzerinnen und Nutzer übertragen dabei häufig zeitintensive oder kognitiv anspruchsvolle Tätigkeiten an KI-Systeme – etwa beim Strukturieren von Informationen, beim Erstellen von Texten oder beim Entwickeln von Ideen. Diese Verhaltensänderung an kognitiver Entlastung lässt sich als bewusste Auslagerung mentaler Prozesse an externe Hilfsmittel (KI) betrachten. Während frühere Formen der kognitiven Entlastung etwa im Notizenmachen, in der Informationssuche oder im Einsatz digitaler Assistenten bestanden, eröffnen generative KI-Tools durch ihre interaktive, flexible und kreative Nutzung neue Möglichkeiten der Zusammenarbeit zwischen Mensch und Maschine in Domänen, die bisher geringe technologische Automatisierung erfahren haben.

Bisherige Forschung konzentrierte sich oft auf Fragen der Akzeptanz oder auf Effizienzgewinne durch Technologieeinsatz. Weniger bekannt sind jedoch die kognitiven und verhaltensbezogenen Auswirkungen der Zusammenarbeit mit generativer KI: Wie verändert die Interaktion mit GenAI die mentale Belastung? In welchem Maße steigert kognitive Entlastung auch die wahrgenommene oder messbare Produktivität? Und welche Risiken entstehen, wenn Aufgaben zu stark ausgelagert werden? Gerade in der Arbeitswelt, in der Produktivität und Wohlbefinden gleichermaßen bedeutsam sind, stellt sich die Frage, wie GenAI-Technologien das Denken, Entscheiden und Handeln von Individuen beeinflussen. Dieser Betrachtungswinkel ist insbesondere von Relevanz, da eine gesteigerte Zusammenarbeit zwischen Mensch und KI in den nächsten Jahren aufgrund von Demografie und Fachkräftemangel zu erwarten ist.

Ziel der Seminararbeit ist es daher, diese Zusammenhänge auf Basis einer systematischen Literaturrecherche (SLR) zu untersuchen. Relevante Beiträge aus den Bereichen Wirtschaftsinformatik, Human-Computer-Interaction (HCI) und Psychologie sollen identifiziert, analysiert und systematisch zusammengefasst werden. Im Fokus steht die Frage, inwiefern generative KI kognitive Entlastung ermöglicht und das Verhalten von Nutzerinnen und Nutzern in komplexen Aufgabenstellungen verändert.

Literatur

• Anthony, C., Bechky, B. A., & Fayard, A.-L. (2023). “Collaborating” with AI: Taking a System View to Explore the Future of Work. Organization Science, 34(5), 1672–1694. doi.org/10.1287/orsc.2022.1651
• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1 
• Fügener, A., Grahl, J., Gupta, A., Ketter, W. (2022). Cognitive Challenges in Human–Artificial Intelligence Collaboration: Investigating the Path Toward Productive Delegation. Information Systems Research, vol. 33, 678–696. doi: 10.1287/isre.2021.1079
• Grinschgl, S., Neubauer, A.C. (2022): Supporting Cognition With Modern Technology: Distributed Cognition Today and in an AI-Enhanced Future. Frontiers in artificial intelligence, vol. 5, 908261. doi: 10.3389/frai.2022.908261
• Noy, S., Zhang, W. (2023): Experimental evidence on the productivity effects of generative artificial intelligence. Science (New York, N.Y.), vol. 381, 187–192. doi: 10.1126/science.adh2586
• Seeber, I., Bittner, E., Briggs, R. O., de Vreede, T., de Vreede, G.-J., Elkins, A., Maier, R., Merz, A. B., Oeste-Reiß, S., Randrup, N., Schwabe, G., & Söllner, M. (2020). Machines as teammates: A research agenda on AI in team collaboration. Information & Management, 57(2), 103174. https://doi.org/10.1016/j.im.2019.103174
• Wang, W., Gao, G., & Agarwal, R. (2023). Friend or Foe? Teaming Between Artificial Intelligence and Workers with Variation in Experience. Management Science. https://doi.org/10.1287/mnsc.2021.00588 

The use of digital data fosters digital resilience by equipping firms with analytical capabilities to predict, recover, and learn from a shock (Tremblay et al., 2023). As digital data becomes an increasingly vital resource, the technologies that enable its collection and analysis emerge as essential tools for managing knowledge about emergent major shocks and build resilience (Boh et al., 2023).

Potential Research Questions:

• How does data-driven decision making contribute to digital resilience during crises?
• How do digital platforms and data infrastructures support resilience in times of disruption?
• How can companies use digital tools and data to stay resilient during crises?

Literatur

• Boh, W., Constantinides, P., Padmanabhan, B., & Viswanathan, S. (2023). Building Digital Resilience Against Major Shocks. MIS Quarterly, 47, 343–361.
• Lindström, N. B., Razmerita, L., Prokopenko, S., & Popovich, N. (2024). Building Digital Resilience in Major Shocks: How Ukrainian Organizations Enact Digital Transformation in Times of War. 6813–6822. doi.org/10125/107202
• Marije H. Bakker, José H. Kerstholt, Marco van Bommel & Ellen Giebels (2019) Decision-making during a crisis: the interplay of narratives and statistical information before and after crisis communication, Journal of Risk Research, 22:11, 1409-1424, DOI: 10.1080/13669877.2018.1473464
• Sutcliffe, K., & Vogus, T. (2003). Organizing for Resilience. In Positive Organizational Scholarship: Foundations of a New Discipline (pp. 94–110). Berrett-Koehler.
• Tim, Y., & Leidner, D. E. (2023). Digital Resilience: A Conceptual Framework for Information Systems Research. Journal of the Association for Information Systems, 24(5), 1184–1198. https://doi.org/10.17705/1jais.00842
• Tremblay, M., Kohli, R., & Rivero, C. (2023). Data is the New Protein: How the Commonwealth of Virginia Built Digital Resilience Muscle and Rebounded from Opioid and COVID Shocks. MIS Quarterly, 47(1), 423–450.

When entrepreneurs face crises, they must innovate through product, service, network or process innovation (Xu et al., 2021). The main goal of crisis management is to restore alignment to the disturbed system by responding to a crisis (Kahn et al., 2013; Williams et al., 2017). Therefore, there is an overlap between crisis management and resilience, as resilience refers to an organization's capacity to return to its initial form and continue to operate reliably in the face of adversity (Williams et al., 2017; Van der Vegt et al., 2015).

Potential Research Questions:

• How does digital resilience differ from traditional crisis management in addressing long-term transformation versus short-term stability?
• In what ways do the concepts of digital resilience and organisational crisis management converge?

Literatur

• Boh, W., Constantinides, P., Padmanabhan, B., & Viswanathan, S. (2023). Building Digital Resilience Against Major Shocks. MIS Quarterly, 47, 343–361.
• Kahn, W. A., Barton, M. A., & Fellows, S. (2013). Organizational Crises and the Disturbance of Relational Systems. Academy of Management Review, 38(3), 377–396. doi.org/10.5465/amr.2011.0363
• Krishnan, C. S. N., Ganesh, L. S., & Rajendran, C. (2022). Entrepreneurial Interventions for crisis management: Lessons from the Covid-19 Pandemic’s impact on entrepreneurial ventures. International Journal of Disaster Risk Reduction, 72, 102830. doi.org/10.1016/j.ijdrr.2022.102830
• Lindström, N. B., Razmerita, L., Prokopenko, S., & Popovich, N. (2024). Building Digital Resilience in Major Shocks: How Ukrainian Organizations Enact Digital Transformation in Times of War. 6813–6822. doi.org/10125/107202
• Liu, Y., Xu, X., Jin, Y., & Deng, H. (2023). Understanding the Digital Resilience of Physicians during the COVID-19 Pandemic: An Empirical Study. MIS Quarterly, 47(1), 391–422. doi.org/10.25300/MISQ/2022/17248
• Tim, Y., & Leidner, D. E. (2023). Digital Resilience: A Conceptual Framework for Information Systems Research. Journal of the Association for Information Systems, 24(5), 1184–1198. doi.org/10.17705/1jais.00842
• Van Der Vegt, G. S., Essens, P., Wahlström, M., & George, G. (2015). Managing Risk and Resilience. Academy of Management Journal, 58(4), 971–980. doi.org/10.5465/amj.2015.4004
• Vogus, T. J., & Sutcliffe, K. M. (2007). Organizational resilience: Towards a theory and research agenda. 2007 IEEE International Conference on Systems, Man and Cybernetics, 3418–3422. doi.org/10.1109/ICSMC.2007.4414160
• Williams, T. A., Gruber, D. A., Sutcliffe, K. M., Shepherd, D. A., & Zhao, E. Y. (2017). Organizational Response to Adversity: Fusing Crisis Management and Resilience Research Streams. Academy of Management Annals, 11(2), 733–769. doi.org/10.5465/annals.2015.0134

Definition — fast‑but‑wrong: Rapid model output that increases speed at the cost of validity, causing plausible but incorrect decisions; mitigated by governance, telemetry, and staged autonomy (see Noy & Zhang 2023; Otis et al. 2024).

Dynamic Capabilities Theory (Teece, Pisano, & Shuen, 1997): Explains how ventures sense, seize, and reconfigure resources under uncertainty. This theory directly underpins ISAFE’s constructs (fit, interoperability, governance).

Potential Research Questions:

• What governance mechanisms (e.g., evidence gating, tiered autonomy, telemetry) mitigate risks of “fast-but-wrong” scaling?
• How do human–AI collaboration competences moderate the effectiveness of agentic AI in scaling ventures?

Literatur

• Weber, M., Beutter, M., Weking, J., Boehm, M., Krcmar, H. (2022). AI startup business models. Bus Inf Syst Eng. 64(1):91–109. doi:10.1007/s12599-021-00732-w.
• Shepherd, D.A., Majchrzak, A. (2022). Machines augmenting entrepreneurs: Opportunities (and threats) at the nexus of AI & entrepreneurship. J Bus Venturing. 37(4):106227. doi:10.1016/j.jbusvent.2022.106227.
• Chalmers, D., MacKenzie, N.G., Carter, S. (2021). Artificial intelligence and entrepreneurship. Entrep Theory Pract. 45(5):1028–1053. doi:10.1177/1042258720934581.
• Townsend, D.M., Hunt, R.A., Rady, J., Manocha, P., Jin, J.H. (2024). Are the futures computable? Knightian uncertainty & AI. Acad Manage Rev. doi:10.5465/amr.2022.0237.
• Townsend, D.M., Hunt, R.A. (2019). Entrepreneurial action, creativity, & judgment in the age of AI. J Bus Venturing Insights. 11:e00126. doi:10.1016/j.jbvi.2019.e00126.
• Fossen, F.M., McLemore, T., Sorgner, A. (2024). Artificial Intelligence and Entrepreneurship. IZA Discussion Paper 17055.

AI is transforming how entrepreneurs discover, evaluate, and exploit opportunities. Generative AI extends beyond prediction to creativity, enabling new forms of opportunity recognition, business model innovation, and ecosystem participation. However, the limitations and opportunities of generative AI under true Knightian uncertainty remain underexplored.

Entrepreneurial Effectuation Theory (Sarasvathy, 2001): Explains how entrepreneurs act under uncertainty by leveraging available means rather than predefined goals. Generative AI aligns well with this approach: it supports idea generation, experimentation, and opportunity recognition, while also highlighting boundaries where human judgment remains essential.

Potential Research Question: How does generative AI reshape entrepreneurial opportunity recognition and decision-making under uncertainty?

Sub Questions: 

• How does generative AI influence the production and evaluation of entrepreneurial ideas?
• What are the boundary conditions where human judgment remains irreplaceable despite AI’s predictive and creative capabilities?
• How do regional ecosystems and regulation (e.g., EU AI Act) mediate the impact of generative AI on opportunity-driven vs. necessity-driven entrepreneurship?

Literatur

• Fossen, F. M., McLemore, T., & Sorgner, A. (2024). Artificial Intelligence and Entrepreneurship.
• Townsend, D. M., & Hunt, R. A. (2019). Entrepreneurial action under uncertainty: AI as a prediction machine?
• Chalmers, D., Matthews, R., & Hyslop, A. (2021). The implications of AI for digital entrepreneurship: External enablers and constraints.

This topic critically examines the prevailing "scaling law" paradigm in AI, which posits that performance improves simply by increasing model size, data, and computation. Instead of relying on brute-force scaling, this research focuses on developing more efficient and intelligent AI architectures. The central goal is to explore methods that promote abstraction and the creation of compact, generalizable world models from less data, a process known as coarse-graining.

Potential Research Questions:

• Is it possible to design AI architectures that explicitly encourage coarse-graining and abstraction, rather than hoping it appears at an immense scale?
• What would it look like, internally, for an LLM to develop a "factored, unified" representation instead of the "entangled" ones they currently learn? How could we detect such a "coarse-graining" event? 

Literatur

• Chen, L., Davis, J. Q., Hanin, B., Bailis, P., Stoica, I., Zaharia, M., & Zou, J. (2024). Are more llm calls all you need? towards scaling laws of compound inference systems. arXiv preprint arXiv:2403.02419.
• Goel, Y., Sengupta, A., & Chakraborty, T. (2025). Position: Enough of Scaling LLMs! Lets Focus on Downscaling. arXiv preprint arXiv:2505.00985.
• Liu, R., Gao, J., Zhao, J., Zhang, K., Li, X., Qi, B., … & Zhou, B. (2025). Can 1b llm surpass 405b llm? rethinking compute-optimal test-time scaling. arXiv preprint arXiv:2502.06703.
• Xue, F., Fu, Y., Zhou, W., Zheng, Z., & You, Y. (2023). To repeat or not to repeat: Insights from scaling llm under token-crisis. Advances in Neural Information Processing Systems, 36, 59304-59322.
• Yousri, R., & Safwat, S. (2023, November). How Big Can It Get? A comparative analysis of LLMs in architecture and scaling. In 2023 International Conference on Computer and Applications (ICCA) (pp. 1-5). IEEE.

This seminar explores the central argument of the current debate in NeSy community that the inherent weaknesses of Large Language Models (LLMs), such as hallucination and a lack of factual understanding, can be mitigated by integrating them with symbolic AI systems like Knowledge Graphs (KGs). The focus is on the complementary nature of these technologies, where the statistical, text-processing power of LLMs is anchored to the structured, verifiable, and human-scrutable knowledge base provided by KGs. Instead of relying solely on the statistical patterns learned during its training, a grounded LLM is architecturally linked to a Knowledge Graph. This allows the model to treat the KG as an external, reliable "fact-checker" or "long-term memory." When faced with a factual query, the LLM can learn to: (1) Retrieve; (2) Verify; and (3) Incorporate. The debate centers on whether this neuro-symbolic approach is the most viable path toward creating trustworthy, fact-based AI.

Potential Research Questions:

• How effectively can knowledge graphs reduce the rate and severity of "hallucinations" in LLMs when answering factual queries?
• What types of knowledge (e.g., commonsense, procedural, domain-specific) are most difficult to represent in a knowledge graph and thus remain a challenge for this neuro-symbolic approach?
• Will future LLMs, trained on even larger and more diverse datasets, eventually learn factual consistency and make explicit KGs obsolete, or is a symbolic "scaffold" always necessary for true reliability?

Literatur

• Pan, S., Luo, L., Wang, Y., Chen, C., Wang, J., & Wu, X. (2024). Unifying large language models and knowledge graphs: A roadmap. IEEE Transactions on Knowledge and Data Engineering, 36(7), 3580-3599.
• Ibrahim, N., Aboulela, S., Ibrahim, A., & Kashef, R. (2024). A survey on augmenting knowledge graphs (KGs) with large language models (LLMs): models, evaluation metrics, benchmarks, and challenges. Discover Artificial Intelligence, 4(1), 76.
• Lavrinovics, E., Biswas, R., Bjerva, J., & Hose, K. (2025). Knowledge graphs, large language models, and hallucinations: An nlp perspective. Journal of Web Semantics, 85, 100844.
• Guan, X., Liu, Y., Lin, H., Lu, Y., He, B., Han, X., & Sun, L. (2024, March). Mitigating large language model hallucinations via autonomous knowledge graph-based retrofitting. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 38, No. 16, pp. 18126-18134).

This seminar topic explores the integration of Generative Artificial Intelligence (GenAI) into the field of knowledge management. Drawing on recent perspectives (Alavi et al., 2024; Benbya et al., 2024), we will analyze how GenAI is poised to revolutionize the core processes of knowledge creation, storage, transfer, and application. The discussion will navigate the dual nature of this technology, examining both its promise as a catalyst for innovation and productivity, and the perils it presents, such as AI bias, ethical dilemmas, and the risk of devaluing human expertise. Rather than focusing solely on a "human-in-the-loop" model for validation, this seminar will adopt a broader knowledge management lens to critique the evolving relationship between human experts and AI systems. We will question how organizations can strategically manage this powerful new technology to augment, rather than simply automate, their knowledge ecosystems.

Potential Research Questions:

• How does the integration of GenAI transform the traditional knowledge management lifecycle (creation, storage, transfer, application), and what new challenges and opportunities emerge at each stage?
• Beyond simple validation, what are the evolving roles and required skills for human knowledge workers in an era where GenAI can generate, synthesize, and disseminate information?
• What organizational strategies and ethical frameworks are necessary to mitigate the risks of GenAI in knowledge management, such as misinformation, intellectual property disputes, and the erosion of tacit knowledge?

Literatur

• Alavi, M., & Leidner, D. E. (2024). A Knowledge Management Perspective of Generative Artificial Intelligence. Journal of the Association for Information Systems, 25(1), 1-12.
• Benbya, H., Strich, F., & Tamm, T. (2024). Navigating Generative Artificial Intelligence Promises and Perils for Knowledge and Creative Work. Journal of the Association for Information Systems, 25(1), 23-36.
• Mosqueira-Rey, E., Hernández-Pereira, E., Alonso-Ríos, D., Bobes-Bascarán, J., & Fernández-Leal, Á. (2023). Human-in-the-loop machine learning: a state of the art. Artificial Intelligence Review, 56(4), 3005-3054.
• Kumar, S., Datta, S., Singh, V., Datta, D., Singh, S. K., & Sharma, R. (2024). Applications, challenges, and future directions of human-in-the-loop learning. IEEE Access, 12, 75735-75760.
• Monarch, R. (2021). Human-in-the-Loop Machine Learning. Manning Publications.

Managing digital transformation is a strategic imperative for organizations to stay competitive in the digital age (Hanelt et al., 2021; Vial, 2019). Digital transformation opens up numerous opportunities for organizations to innovate processes, products, services, and business models and can promote competitiveness in the digital age (Kraus et al., 2022; Verhoef et al., 2021). However, digital transformation is accompanied by numerous organizational challenges, such as the adaptation of organizational structures, leadership, business strategies, or even organizational culture (Vial, 2019), and is therefore a complex issue that in practice often fails (Oludapo et al. 2024; Wade & Shan 2020). 

Since digital transformation is not a static phenomenon, but can be viewed as a dynamic process that evolves over time (Carroll et al., 2023) and requires continuous strategic adaptation (Hanelt et al., 2021), it is crucial to gain a deeper understanding of the drivers and outcomes of digital transformation processes to ensure their long-term success.

As part of this seminar paper, this research topic will be explored empirically using a qualitative approach. To this end, semi-structured expert interviews will be conducted with interview partners who are involved in navigating digital transformation in an organizational context. 

Literatur

• Carroll, N., Conboy, K., Hassan, N. R., Hess, T., Junglas, I., & Morgan, L. (2023). Problematizing assumptions on digital transformation research in the information systems field. Communications of the Association for Information Systems, 53(1), 508-531.
• Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of management studies, 58(5), 1159-1197.
• Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International journal of information management, 63, 102466.
• Oludapo, S., Carroll, N., & Helfert, M. (2024). Why do so many digital transformations fail? A bibliometric analysis and future research agenda. Journal of Business Research, 174, 114528.
• Verhoef, P. C., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J. Q., Fabian, N., & Haenlein, M. (2021). Digital transformation: A multidisciplinary reflection and research agenda. Journal of business research, 122, 889-901.
• Vial, G. (2019). Understanding digital transformation: A review and a research agenda. The Journal of Strategic Information Systems, 28(2), 118-144.
• Wade, M., & Shan, J. (2020). Covid-19 Has Accelerated Digital Transformation, but May Have Made it Harder Not Easier. MIS Quarterly Executive, 19(3), 7.

Managing digital transformation is a strategic imperative to stay competitive in the digital age, but in practice often fails (Oludapo et al. 2024; Wade & Shan 2020). Since digital transformation is not a static phenomenon, but can be viewed as a dynamic process that evolves over time (Carroll et al., 2023), it is crucial to gain a deeper understanding of the drivers and outcomes of digital transformation processes to ensure their long-term success. 

Due to the interdisciplinary nature of the digital transformation research stream, the current state of research is potentially fragmented (Hanelt et al., 2021; Kraus et al., 2022).

A typical focus is on understanding and designing the digital transformation of an organization. However, digital transformations increasingly take place at an interorganizational or industry level (Mann et al., 2022; Plekhanov et al., 2023). Nevertheless, digital transformation at the industry level is still underexplored in information systems research. Understanding digital transformation on the industry level is crucial for policymakers and stakeholder organizations alike, as macro-environmental aspects like industry trends have a strong impact on organizations (Mergel et al., 2019).

As part of this seminar, the current state of research on drivers and outcomes of digital transformation phenomena at the industry level will be aggregated by conducting a systematic literature analysis.

Literatur

• Carroll, N., Conboy, K., Hassan, N. R., Hess, T., Junglas, I., & Morgan, L. (2023). Problematizing assumptions on digital transformation research in the information systems field. Communications of the Association for Information Systems, 53(1), 508-531.
• Hanelt, A., Bohnsack, R., Marz, D., & Antunes Marante, C. (2021). A systematic review of the literature on digital transformation: Insights and implications for strategy and organizational change. Journal of management studies, 58(5), 1159-1197.
• Kraus, S., Durst, S., Ferreira, J. J., Veiga, P., Kailer, N., & Weinmann, A. (2022). Digital transformation in business and management research: An overview of the current status quo. International journal of information management, 63, 102466.
• Mann, G., Karanasios, S., & Breidbach, C. F. (2022). Orchestrating the digital transformation of a business ecosystem. The Journal of Strategic Information Systems, 31(3), 101733. 
• Mergel, I., Edelmann, N., & Haug, N. (2019). Defining digital transformation: Results from expert interviews. Government information quarterly, 36(4), 101385.
• Oludapo, S., Carroll, N., & Helfert, M. (2024). Why do so many digital transformations fail? A bibliometric analysis and future research agenda. Journal of Business Research, 174, 114528.
• Plekhanov, D., Franke, H., & Netland, T. H. (2023). Digital transformation: A review and research agenda. European Management Journal, 41(6), 821-844. 
• Wade, M., & Shan, J. (2020). Covid-19 Has Accelerated Digital Transformation, but May Have Made it Harder Not Easier. MIS Quarterly Executive, 19(3), 7.

In pursuit of a common, objective, and measurable perspective on the concept of charisma, current research by Antonakis et al. (2016) defines charisma as “values-based, symbolic, and emotion-laden leader signaling” (p. 304). They further provide empirical evidence that charisma can be trained and manipulated (Antonakis et al., 2022). Charismatic leaders inspire others by speaking optimistically about the future and by conveying positive ideals or aspirations to their followers that are linked to desired outcomes (Neufeld et al., 2007). Their behavior encourages followers to willingly succumb to their influence by communicating values and inspiring via a compelling vision that appeals to followers. Hereby, they leverage their own leadership role in providing purpose and making events meaningful to followers (House & Howell, 1992). According to Antonakis et al. (2016; 2011) the charismatic effect is based on the leader (a) justifying the mission by appealing to values that distinguish right from wrong; (b) communicating in a symbolic way to make the message clear and vivid, and also symbolizing and embodying the moral unity of the collective per se; and (c) demonstrating conviction and passion for the mission via emotional displays. People follow such leaders not only because of their formal authority but also because they perceive the character of the leader as exceptional (Antonakis et al., 2011). 

Experiments of Nieken (2023) and Antonakis et al. (2022) on charisma´s effect on follower behavior present current studies entailing charismatic leadership tactics (CLT), that can be transferred in the context of IS discipline focusing on DT processes and success criteria (Pfister & Lehmann, 2023). With clear criteria along twelve verbal and non-verbal dimensions, charismatic behavior can be identified, manipulated, and trained.

As part of this seminar paper, an (online) experiment has to be designed and conducted to compare the influence of charisma / charismatic leadership on DT success/performance.

Literatur

• Antonakis, J., Bastardoz, N., Jacquart, P., & Shamir, B. (2016). Charisma: An Ill-Defined and Ill-Measured Gift. Annual Review of Organizational Psychology and Organizational Behavior, 3(1), 293–319.
• Antonakis, J., d’Adda, G., Weber, R. A., & Zehnder, C. (2022). “Just Words? Just Speeches?” On the Economic Value of Charismatic Leadership. Management Science, 68(9), 6355–6381.
• Antonakis, J., Fenley, M., & Liechti, S. (2011). Can charisma be taught? Tests of two interventions. Academy of Management Journal, 10(3), 374–396.
• House, R. J., & Howell, J. M. (1992). Personality and charismatic leadership. The Leadership Quarterly, 3(2), 81–108.
• Neufeld, D. J., Dong, L., & Higgins, C. (2007). Charismatic leadership and user acceptance of information technology. European Journal of Information Systems, 16(4), 494–510.
• Nieken, P. (2023). Charisma in the gig economy: The impact of digital leadership and communication channels on performance. The Leadership Quarterly, 34(6), 101631.
• Pfister, P., & Lehmann, C. (2023). Measuring the Success of Digital Transformation in German SMEs. Journal of Small Business Strategy, 33(1).

In pursuit of a common, objective, and measurable perspective on the concept of charisma, current research by Antonakis et al. (2016) defines charisma as “values-based, symbolic, and emotion-laden leader signaling” (p. 304). They further provide empirical evidence that charisma can be trained and manipulated (Antonakis et al., 2022). Charismatic leaders inspire others by speaking optimistically about the future and by conveying positive ideals or aspirations to their followers that are linked to desired outcomes (Neufeld et al., 2007). Their behavior encourages followers to willingly succumb to their influence by communicating values and inspiring via a compelling vision that appeals to followers. Hereby, they leverage their own leadership role in providing purpose and making events meaningful to followers (House & Howell, 1992). According to Antonakis et al. (2016; 2011) the charismatic effect is based on the leader (a) justifying the mission by appealing to values that distinguish right from wrong; (b) communicating in a symbolic way to make the message clear and vivid, and also symbolizing and embodying the moral unity of the collective per se; and (c) demonstrating conviction and passion for the mission via emotional displays. People follow such leaders not only because of their formal authority but also because they perceive the character of the leader as exceptional (Antonakis et al., 2011). 

Experiments of Nieken (2023) and Antonakis et al. (2022) on charisma's effect on follower behavior present current studies entailing charismatic leadership tactics (CLT), that can be transferred in the context of IS discipline focusing on employees´ desired and undesired responses within and towards DT (Weber et al., 2022). With clear criteria along twelve verbal and non-verbal dimensions, charismatic behavior can be identified, manipulated, and trained.

As part of this seminar paper, an (online) experiment has to be designed and conducted to compare the influence of charisma / charismatic leadership on positive or negative reactions/responses in DT.

Literatur

• Antonakis, J., Bastardoz, N., Jacquart, P., & Shamir, B. (2016). Charisma: An Ill-Defined and Ill-Measured Gift. Annual Review of Organizational Psychology and Organizational Behavior, 3(1), 293–319.
• Antonakis, J., d’Adda, G., Weber, R. A., & Zehnder, C. (2022). “Just Words? Just Speeches?” On the Economic Value of Charismatic Leadership. Management Science, 68(9), 6355–6381.
• Antonakis, J., Fenley, M., & Liechti, S. (2011). Can charisma be taught? Tests of two interventions. Academy of Management Journal, 10(3), 374–396.
• House, R. J., & Howell, J. M. (1992). Personality and charismatic leadership. The Leadership Quarterly, 3(2), 81–108.
• Neufeld, D. J., Dong, L., & Higgins, C. (2007). Charismatic leadership and user acceptance of information technology. European Journal of Information Systems, 16(4), 494–510.
• Nieken, P. (2023). Charisma in the gig economy: The impact of digital leadership and communication channels on performance. The Leadership Quarterly, 34(6), 101631.
• Weber, E., Büttgen, M., & Bartsch, S. (2022). How to take employees on the digital transformation journey: An experimental study on complementary leadership behaviors in managing organizational change. Journal of Business Research, 143, 225–238.

As part of this seminar paper, a systematic literature review will be conducted to assess the state of the art on academic literature in the IS discipline (e.g., from 2012 and upwards on) on the different usages of the SEM approach why, what and how the method was applied in respective studies. 

Literatur

• Becker, J.-M., Klein, K., & Wetzels, M. (2012). Hierarchical Latent Variable Models in PLS-SEM: Guidelines for Using Reflective-Formative Type Models. Long Range Planning, 45(5-6), 359–394.
• Cheah, J.-H., Ting, H., Ramayah, T., Memon, M. A., Cham, T.-H., & Ciavolino, E. (2019). A comparison of five reflective–formative estimation approaches: reconsideration and recommendations for tourism research. Quality & Quantity, 53(3), 1421–1458.
• Lohmöller, J. -B. (1989). Latent Variable Path Modeling with Partial Least Squares. Heidelberg, Physica, 1989.
• Ringle, C. M., Sarstedt, M., & Straub, D. W. (2012). Editor's Comments: A Critical Look at the Use of PLS-SEM in "MIS Quarterly". MIS Quarterly, 36(1), iii.
• Sarstedt, M., Hair, J. F., Cheah, J.-H., Becker, J.-M., & Ringle, C. M. (2019). How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM. Australasian Marketing Journal, 27(3), 197–211.
• Wold, H. (1974). Causal Flows with Latent Variables: Partings of Ways in the Light of NIPALS Modelling. European Economic Review, 5(1), 67-86.
• Wold, H. (1982). Soft Modeling: The Basic Design and Some Extensions, in: Systems Under Indirect Observations, Partii, К. G. Jöresko and H. Wold (eds.), Amsterdam, North-Holland.

IT Governance bezieht sich auf die Prozesse, Strukturen und Mechanismen, die sicherstellen, dass die Informationstechnologie (IT) einer Organisation mit den strategischen Zielen übereinstimmt, Risiken minimiert und die Effizienz maximiert. Die Definition und das Verständnis von IT Governance variieren je nach Quelle und Perspektive, was zu einer Vielzahl von Konzepten und Modellen führt.

Vor diesem Hintergrund, zielt die Seminararbeit darauf ab, eine systematische Analyse der existierenden Definitionen von IT Governance durchzuführen. Dabei wird eine Literaturrecherche durchgeführt, um die unterschiedlichen Ansätze und Interpretationen des Begriffs zu identifizieren und zu vergleichen. Ziel ist es, die zentralen Merkmale von IT Governance herauszuarbeiten und ein tieferes Verständnis für die Vielschichtigkeit dieses Konzepts zu entwickeln. Die Arbeit beleuchtet auch, wie sich die Definitionen im Laufe der Zeit entwickelt haben und welche Auswirkungen diese unterschiedlichen Auffassungen auf die Praxis haben.

Literatur

• Borghoff, Vincent; Plattfaut, Ralf (2022): Steering the Robots: An Investigation of IT Governance Models for Lightweight IT and Robotic Process Automation. In: Andrea Marrella, Raimundas Matulevičius, Renata Gabryelczyk, Bernhard Axmann, Vesna Bosilj Vukšić, Walid Gaaloul et al. (Hg.): Business Process Management: Blockchain, Robotic Process Automation, and Central and Eastern Europe Forum, Bd. 459. Cham: Springer International Publishing (Lecture Notes in Business Information Processing), S. 170–184.
• Huang, R., Zmud, R.W., Price, R.L. (2009): IT Governance Practices in Small and Medium-Sized Enterprises: Recommendations from an Empirical Study. In: Dhillon, G., Stahl, B.C., Baskerville, R. (eds) Information Systems – Creativity and Innovation in Small and Medium-Sized Enterprises. IFIP Advances in Information and Communication Technology, vol 301. Springer, Berlin, Heidelberg.
• Pereira, Ruben; Mira da Silva, Miguel (2012): A Literature Review: Guidelines and Contingency Factors for IT Governance. European, Mediterranean & Middle Eastern Conference on Information Systems 2012, Munich, June 7-8, Germany.

Compliance-Anforderungen spielen eine entscheidende Rolle in der IT Governance, insbesondere bei der Entwicklung und Implementierung von leichtgewichtigen IT-Lösungen wie Robotic Process Automation (RPA) oder No-Code-Anwendungen. Diese Technologien ermöglichen es auch Nutzern ohne tiefgehende technische Kenntnisse, IT-Lösungen zu erstellen, was neue Herausforderungen für die Einhaltung gesetzlicher und regulatorischer Vorgaben mit sich bringt.

Die Seminararbeit untersucht, wie gesetzliche und regulatorische Anforderungen die Governance-Modelle von leichtgewichtigen IT-Lösungen wie RPA und No-Code-Anwendungen beeinflussen. Dabei wird eine Ableitung relevanter Compliance-Faktoren vorgenommen, die für die effektive Governance dieser Technologien notwendig sind. Ziel ist es, zu analysieren, inwieweit solche Anforderungen in den Entwicklungs- und Betriebsprozess integriert werden müssen, um rechtliche Risiken zu minimieren und die Integrität der Systeme zu gewährleisten.

Literatur

• Asatiani, Aleksandre; Hakkarainen, Tuuli; Paaso, Kimmo; Penttinen, Esko (2023): Security by envelopment – a novel approach to data-security-oriented configuration of lightweight-automation systems. In: Eur J Inf Syst 33 (5), S. 631-653.
• Bai, X., Nunez, M. & Kalagnanam, J. R. (2012): Managing data quality risk in accounting information systems. In: Information Systems Research 23 (2), S. 453–473.
• Bygstad, Bendik (2015): The Coming of Lightweight IT. In: Jörg Becker, Jan Vom Brocke und Marco de Marco (Hg.): Proceedings of the 23rd European Conference on Information Systems. 23rd European Conference on Information Systems. Münster, Germany, 2015.
• Sadiq, S. & Governatori, G. (2014): Managing Regulatory Compliance in Business Processes, Springer Berlin Heidelberg, S. 265–288.
• Herm, L.-V., Janiesch, C., Reijers, H. A. & Seubert, F. (2021): From symbolic rpa to intelligent rpa: Challenges for developing and operating intelligent software robots, Springer, Cham, S. 289–305.
• Kartseva, V., Hulstijn, J., Gordijn, J. & Tan, Y.-H. (2010): Control patterns in a health-care network. In: European Journal of Information Systems 19 (3), S. 320–343.
• Plattfaut, Ralf; Borghoff, Vincent (2022): Robotic Process Automation – A Literature-Based Research Agenda. In: Journal of Information Systems 36 (2), S. 173-191. 

Zentralisierte und dezentralisierte IT Governance-Modelle repräsentieren zwei unterschiedliche Ansätze zur Steuerung und Kontrolle von IT-Ressourcen und -Prozessen in einer Organisation. Während zentralisierte Modelle eine starke zentrale Steuerung und einheitliche Entscheidungsprozesse fördern, bieten dezentralisierte Modelle mehr Flexibilität und Autonomie für einzelne Einheiten oder Abteilungen.

Die Seminararbeit vergleicht zentralisierte und dezentralisierte IT Governance-Modelle hinsichtlich ihrer Struktur, Vorteile und Herausforderungen. Ziel ist es, die unterschiedlichen Ansätze zu analysieren, um zu verstehen, welche Auswirkungen diese Modelle auf die IT-Organisation, Entscheidungsprozesse und die Umsetzung von IT-Strategien haben. Die Arbeit wird verschiedene Literaturquellen heranziehen, um die Vor- und Nachteile beider Modelle zu beleuchten und praktische Beispiele sowie Erfolgsfaktoren für die Implementierung zu diskutieren.

Literatur

• Bygstad, Bendik (2017): Generative innovation: a comparison of lightweight and heavyweight IT. In: J Inf Technol 32 (2), S. 180–193.
• Huang, R., Zmud, R.W., Price, R.L. (2009): IT Governance Practices in Small and Medium-Sized Enterprises: Recommendations from an Empirical Study. In: Dhillon, G., Stahl, B.C., Baskerville, R. (eds) Information Systems – Creativity and Innovation in Small and Medium-Sized Enterprises. IFIP Advances in Information and Communication Technology, vol 301. Springer, Berlin, Heidelberg.
• Sambamurthy, V.; Zmud, R.W. (1999): Arrangements for Information Technology Governance: A Theory of Multiple Contingencies. MIS Quarterly, 23 (2), S. 261-290.

Robotic Process Automation (RPA) und Low-Code/No-Code (LCNC)-Plattformen gewinnen zunehmend an Bedeutung, da sie es Fachbereichen ermöglichen, Prozesse effizient zu automatisieren und Anwendungen eigenständig zu entwickeln. Diese Technologien zeichnen sich durch ihre Benutzerfreundlichkeit und schnelle Umsetzbarkeit aus, stellen Organisationen jedoch vor neue Herausforderungen hinsichtlich Steuerung, Kontrolle und Einbindung in bestehende IT-Governance-Strukturen. Unter dem Begriff Governance von RPA und LCNC versteht man die Prozesse, Verantwortlichkeiten, Mechanismen und Richtlinien, die sicherstellen sollen, dass diese Technologien den strategischen Zielen der Organisation dienen, Risiken minimieren und zugleich Flexibilität ermöglichen.

Vor diesem Hintergrund zielt die Seminararbeit darauf ab, die unterschiedlichen Definitionen und Ansätze von Governance im Kontext von RPA und LCNC systematisch zu analysieren. Mithilfe einer Literaturrecherche sollen bestehende Modelle und Frameworks untersucht, zentrale Erfolgsfaktoren identifiziert und Gemeinsamkeiten sowie Unterschiede zwischen Governance-Ansätzen für klassische IT und Lightweight IT in Form von RPA und LCNC herausgearbeitet werden. Ziel ist es, ein vertieftes Verständnis für die besonderen Governance-Anforderungen von RPA und LCNC zu entwickeln und deren Implikationen für die Praxis zu beleuchten.

Literatur

• Bygstad, B., and Iden, J. 2017. “A Governance Model for Managing Lightweight IT,” in Recent Advances in Information Systems and Technologies, Á. Rocha, A. M. Correia, H. Adeli, L. P. Reis and S. Costanzo (eds.), Cham: Springer International Publishing, pp. 384-393.
• Borghoff, V., and Plattfaut, R. 2022. “Steering the Robots: An Investigation of IT Governance Models for Lightweight IT and Robotic Process Automation,” Springer, Cham, pp. 170-184.
• Eulerich, M., Waddoups, N., Wagener, M., and Wood, D. A. 2024. “Development of a Framework of Key Internal Control and Governance Principles for Robotic Process Automation (RPA),” Journal of Information Systems (38:2), pp. 29-49.
• Petersen, J., and Schröder, H. 2020. “Entwicklung einer Robotic Process Automation (RPA)-Governance,” HMD Praxis der Wirtschaftsinformatik (57:6), pp. 1130-1149.
• Plattfaut, R., Borghoff, V., Godefroid, M., Koch, J., Trampler, M., & Coners, A. (2022). The critical success factors for robotic process automation. Computers in Industry, 138, 103646.
• Plattfaut, R. (2022). Robotic process automation – Eine Herausforderung für IT-Governance. In C. Feldmann (Hrsg.), Praxishandbuch Robotic Process Automation (RPA) (S. 401–410). Springer. 

Die Gestaltung von Social-Media-Plattformen und die Dynamik sozialer Interaktionen beeinflussen maßgeblich, wie Nutzerinnen und Nutzer Inhalte wahrnehmen und verarbeiten. Während Algorithmen, Informationsarchitekturen und Interaktionsmechanismen bestimmte Formen der Aufmerksamkeit und des Engagements fördern (Chen & Wei, 2019; Kotlarsky et al., 2023), wirken soziale Einflüsse wie Communities, Peers oder Influencer als zentrale Treiber von Haltungen und Verhaltensweisen (Vaast & Pinsonneault, 2022; Leong et al., 2015). Zusammengenommen bestimmen diese Faktoren, ob Social Media nachhaltige Achtsamkeit (Sustainability Mindfulness) fördert oder hemmt.

Die Seminararbeit verfolgt das Ziel, die bestehende Literatur zu sozialen Einflüssen und plattformspezifischen Merkmalen im Zusammenhang mit Nachhaltigkeit systematisch zu sichten und integrativ zusammenzuführen. Dabei sollen zentrale Wirkmechanismen identifiziert, Gemeinsamkeiten und Unterschiede zwischen den Forschungssträngen herausgearbeitet sowie konzeptionelle Lücken benannt werden. So kann ein fundiertes Verständnis dafür entwickelt werden, wie Plattformdesign und soziale Dynamiken in sozialen Medien zur Förderung nachhaltiger Achtsamkeit beitragen können.

Literatur

• Leong, C., Pan, S., Ractham, P., & Kaewkitipong, L. (2015). ICT-Enabled Community Empowerment in Crisis Response: Social Media in Thailand Flooding 2011. Journal of the Association for Information Systems, 16(3), 174–212. doi.org/10.17705/1jais.00390
• Chen, X., & Wei, S. (2019). Enterprise social media use and overload: A curvilinear relationship. Journal of Information Technology, 34(1), 22–38.
• Chen, Y.-S., Chang, C.-H., Yeh, S.-L., & Cheng, H.-I. (2015). Green shared vision and green creativity: The mediation roles of green mindfulness and green self-efficacy. Quality & Quantity, 49(3), 1169–1184.
• Kotlarsky, J., Oshri, I., & Sekulic, N. (2023). Digital Sustainability in Information Systems Research: Conceptual Foundations and Future Directions. Journal of the Association for Information Systems, 24(4), 936–952.
• Furnari, S., Crilly, D., Misangyi, V. F., Greckhamer, T., Fiss, P. C., & Aguilera, R. V. (2021). Capturing Causal Complexity: Heuristics for Configurational Theorizing. Academy of Management Review, 46(4), 778–799.
• Vaast, E., & Pinsonneault, A. (2022). Dealing with the social media polycontextuality of work. Information Systems Research, 33(4), 1428-1451.

Agentic AI is gaining increasing importance. Agentic AI enables AI systems to autonomously pursue complex goals, learn adaptively from environmental feedback, and independently initiate goal-oriented actions (Mukherjee and Chang 2025). Unlike conventional AI models, which respond exclusively to external inputs, Agentic AI systems proactively regulate their behavior, continuously refine their strategies, and independently orchestrate multistage decision-making processes (Murugesan 2025). This raises important questions about the affordances (i.e., possibilities for action and constraints) that Agentic AI provides in organizational contexts, as well as potential risks, unintended consequences, and ethical implications.

The seminar paper is expected to conceptually explore which affordances Agentic AI provides in organizational settings. Students should identify and categorize these affordances (e.g., automation of complex processes, novel human–machine interaction, strategic decision support) and critically discuss the tensions and risks they create (e.g., control vs. autonomy, trust vs. opacity, efficiency vs. ethical challenges).

Literatur

• Leonardi (2011). When Flexible Routines Meet Flexible Technologies: Affordance, Constraint, and the Imbrication of Human and Material Agencies. MIS Quarterly, 35(1), 147.
• Faraj, S., Azad, B. (2012). The Materiality of Technology: An Affordance Perspective. In: Paul M. Leonardi, Bonnie A. Nardi und Jannis Kallinikos (eds.): Materiality and Organizing. Oxford University PressOxford, 237–258.
• Mukherjee, A., Chang, H. H. (2025). Agentic AI: Autonomy, Accountability, and the Algorithmic Society.
• Murugesan, S. (2025). The Rise of Agentic AI: Implications, Concerns, and the Path Forward. IEEE Intell. Syst.,40(2), 8–14. 

Electronic Performance Monitoring (EPM) refers to the use of digital tools and data to monitor and evaluate employee behavior and performance (Tomczak et al. 2018). With the rise of digitalization and process mining, organizations are increasingly able to generate detailed insights into how employees work, how processes are executed, and where inefficiencies occur (Higgins et al. 2023). While such data-driven approaches can improve efficiency, transparency, and compliance, they also raise concerns regarding privacy, fairness, and trust (Rafiei and van der Aalst 2020). For organizations, the challenge lies in balancing the benefits of monitoring with ethical, legal, and cultural considerations (Sherif et al. 2021).

The aim of this seminar paper is to explore the intersection of Electronic Performance Monitoring and Process Mining. Students are expected to investigate how process mining techniques can be used to monitor performance, what potential benefits arise, and which risks and ethical dilemmas need to be considered.

Students should conduct a structured literature review and critically analyze examples from practice (e.g., case studies, company reports, or media coverage). The goal is to provide a well-founded overview of the topic and to identify potential future directions for research and practice.

Literatur

• Alder, G. S. (2001). Employee reactions to electronic performance monitoring: A consequence of organizational culture. Journal of High Technology Management Research, 12(2), 323–342.
• Ball, K. (2010). Workplace surveillance: An overview. Labor History, 51(1), 87–106.
• Ravid, D. M., Tomczak, D. L., White, J. C., & Behrend, T. S. (2020). EPM 20/20: A review, framework, and research agenda for electronic performance monitoring. Journal of Management, 46(1), 100–126.
• van der Aalst, W. (2016). Process Mining: Data Science in Action (2nd ed.). Springer.
• Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G. (2019). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 160(2), 377–392.
• Tomczak, D. L., Lanzo, L. A., Aguinis, H. (2018). Evidence-based recommendations for employee performance monitoring. Business Horizons, 61(2), 251–259.
• Higgins, L., Marshall, A., Crysel, K., Dencik, J. (2023). From efficiencies to transformation – tapping the full potential of process mining. SL 51(2), 31–36.
• Rafiei, Majid; van der Aalst, Wil M. P. (2020): Towards Quantifying Privacy in Process Mining. Available online at http://arxiv.org/pdf/2012.12031v1
• Sherif, Karma; Jewesimi, Omolola; El-Masri, Mazen (2021): Empowering employees: the other side of electronic performance monitoring. JICES, 19(2), 207–221.