A majority of researchers in the XAI community agree that understanding a machine learning model and its reasoning process is necessary to apply it in practical tasks, especially in domains with high stakes like medicine or credit rating. The terms interpretability and explainability are often used in this context. However, while both of these are related to understanding, there is disagreement about their definition and hence about how to measure a ‚good‘ or ‚sufficient‘ degree of interpretability or explainability. Comparing and systematizing these definitions and their relation to the concept of understanding could lead to insights about how understanding is achieved by different means of explanation and aid in a more unified and comprehensive research endeavour. 

Thus, as a part of this seminar thesis, a literature review is conducted to explore how researchers in the field of XAI define the terms interpretability and explainability and how the concept of understanding is related to different tasks via these definitions.

Literature

• Antoniadi, A. M., Du, Y., Guendouz, Y., Wei, L., Mazo, C., Becker, B. A., & Mooney, C. (2021). Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Applied Sciences, 11(11), 5088.

• Gilpin, L. H., Bau, D., Yuan, B. Z., Bajwa, A., Specter, M., & Kagal, L. (2018). Explaining explanations: An overview of interpretability of machine learning. In 2018 IEEE 5th International Conference on data science and advanced analytics (DSAA) (pp. 80-89). IEEE.

The XAI literature offers a multitude of properties and corresponding evaluation techniques to ensure that explanations achieve their goals with respect to understanding, quality, task performance, and trust creation. Task performance represents both model understanding and usability in practice. It is mostly evaluated on trivial decision problems where an unambiguous ground truth is available. Yet, in high-stakes domains like medicine, the correctness of a decision is not always as clearly defined as, for example, in the classification of animal pictures. Medical professionals work based on heuristics and can not always give precise explanations for their decisions. 

As a part of this seminar thesis, a literature review is conducted to explore how researchers in the field of XAI evaluate explanation quality in decision-making contexts where either theoretical knowledge to formulate ground truths is missing or no benchmark datasets offering ground truth explanations have, as of now, been curated. 

Literature

• Sendak, M., Elish, M. C., Gao, M., Futoma, J., Ratliff, W., Nichols, M., Bedoya, A., Balu, S. & O'Brien, C. (2020). "The human body is a black box": Supporting clinical decision-making with deep learning. In Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency (pp. 99-109). 

• London, A. J. (2019). Artificial intelligence and black‐box medical decisions: accuracy versus explainability. Hastings Center Report, 49(1), 15-21.

Flow, the experience of being fully focused on an activity and fluidity of action, is a desirable state as it is associated with improved individual performance and well-being. Recent literature suggests that electrocardiogram (ECG) parameters can be used to enable unobtrusive and continuous classification of flow states through machine learning (ML). On this basis, systems can be created for different scenarios that adapt their behavior based on the corresponding flow states. Therefore, in this seminar thesis, a literature review is conducted to investigate the state of the art of flow classifications based on ECG data using ML.

Literature

• De Manzano, Ö., Theorell, T., Harmat, L., & Ullén, F. (2010). The psychophysiology of flow during piano playing. Emotion, 10(3), 301.
• Peifer, C., & Engeser, S. (Eds.). (2021). Advances in flow research. Cham: Springer International Publishing.
• Rissler, R., Nadj, M., Li, M., Loewe, N., Knierim, M., & Maedche, A. (2023). To Be or Not to Be in Flow at Work: Physiological Classification of Flow Using Machine Learning. IEEE Transactions on Affective Computing, 14(1), 463-474. 

The assessment of cognitive load using psychophysiological data is important to ensure safety and enhance performance for employees across different industries. While various measurement systems exist, such as chest belts, smartwatches, and smartglasses, these often require external devices, and the application is limited in real-world settings. Camera-based systems offer a less invasive alternative for cognitive load assessment. Recent advances in machine learning (ML) enable the tracking of relevant parameters like gaze direction, heart rate, and body posture. A deeper understanding of the possibilities and limitations of camera-based systems helps to develop a minimally invasive and cost-effective solution. Therefore, in this seminar thesis, a literature review is conducted to explore the possibilities of using camera data and ML to measure cognitive load parameters.

Literature

• Ayres, P., Lee, J. Y., Paas, F., & Van Merrienboer, J. J. (2021). The validity of physiological measures to identify differences in intrinsic cognitive load. Frontiers in psychology, 12, 702538.
• McDuff, D. (2023). Camera measurement of physiological vital signs. ACM Computing Surveys, 55(9), 1-40.

User innovation refers to the process where end-users, rather than producers, develop or modify products or services to better meet their needs. This concept is driven by users’ unique insights, which may not be apparent to the companies or professionals who originally designed the product. Often, user innovation leads to new or improved features, products, or entirely new solutions, which may later be commercialized or adapted by companies. These mechanisms can also be found in the health sector. In digital health, where technologies like telemedicine, wearable devices, mobile health (mHealth), and health data platforms are transforming the healthcare landscape, user innovation can play a crucial role, in the form of patients as innovators, supporting communities, or personalized medicine. The goal of this seminar paper is to structure and synthesize the literature and identify gaps where knowledge on the role of users as innovators is still lacking.

Literature

• Amann, J., Zanini, C., & Rubinelli, S. (2016). What online user innovation communities can teach us about capturing the experiences of patients living with chronic health conditions. A scoping review. PloS one, 11(6), e0156175.
• Gambardella, A., Raasch, C., & von Hippel, E. (2017). The user innovation paradigm: impacts on markets and welfare. Management Science, 63(5), 1450-1468.
• Habicht, H., Oliveira, P., & Shcherbatiuk, V. (2013). User innovators: when patients set out to help themselves and end up helping many. Die Unternehmung, 66(3), 277-294.
• Rivard, L., Lehoux, P., & Alami, H. (2021). “It’s not just hacking for the sake of it”: a qualitative study of health innovators’ views on patient-driven open innovations, quality and safety. BMJ Quality & Safety, 30(9), 731-738.
• Stock, R. M., Oliveira, P., & Von Hippel, E. (2015). Impacts of hedonic and utilitarian user motives on the innovativeness of user‐developed solutions. Journal of Product Innovation Management, 32(3), 389-403.

Many companies have specific sustainability goals, often linked to the one defined by the United Nations. Clashes between clients and an organization's sustainability goals often arise when there are conflicting priorities or values regarding environmental, social, or economic practices. These conflicts can occur in various industries, especially when clients' expectations or business models do not align with an organization’s commitment to sustainability. For example, clients may prioritize short-term financial gains or cost-cutting measures, often focusing on delivering products or services as quickly and cheaply as possible. This can lead to the use of unsustainable materials, resource overuse, or environmentally harmful practices. The goal of this seminar paper is to conduct a literature review on clashes between companies sustainability goals and their clients’ interests together with potential mechanisms to resolve inherent tensions. In a second step, it should be analyzed what can be transferred to the IT companies in the context of Green IT.

Literature

• Acuti, D., Pizzetti, M., & Dolnicar, S. (2022). When sustainability backfires: A review on the unintended negative side‐effects of product and service sustainability on consumer behavior. Psychology & Marketing, 39(10), 1933-1945.
• Chen, J., & Liu, L. (2023). Is green customer integration always a facilitator for green product innovation?: a conflict-based view. European Journal of Innovation Management, 26(6), 1524-1546.
• Dedrick, J. (2010). Green IS: Concepts and issues for information systems research. Communications of the Association for Information Systems, 27(1), 11.

• Uddin, M., & Rahman, A. A. (2012). Energy efficiency and low carbon enabler green IT framework for data centers considering green metrics. Renewable and Sustainable Energy Reviews, 16(6), 4078-4094.

A Decentralized Autonomous Organization (DAO) is an organization that operates through rules encoded as computer programs on a blockchain. DAOs are designed to function without a centralized authority, relying instead on the collective decision-making of their members, who vote on governance proposals to steer the organization. The rules and governance are enforced automatically through smart contracts, which are self-executing agreements with the terms written into code. Hence, in addition to other attributes, unlike traditional organizations, which are managed by a central authority (like a CEO or board of directors), a DAO is managed collectively by its members. No single person or entity has control over the organization. The goal of this seminar paper is to develop a comprehensive understanding of what a DAO is based on a systematic literature review. In a second step, at least three concrete use scenarios should be described and analyzed according to self-developed criteria.

Literature

• Goldberg, M., & Schär, F. (2023). Metaverse governance: An empirical analysis of voting within Decentralized Autonomous Organizations. Journal of Business Research, 160, 113764.
• Qin, R., Ding, W., Li, J., Guan, S., Wang, G., Ren, Y., & Qu, Z. (2022). Web3-based decentralized autonomous organizations and operations: Architectures, models, and mechanisms. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 53(4), 2073-2082.
• Santana, C., & Albareda, L. (2022). Blockchain and the emergence of Decentralized Autonomous Organizations (DAOs): An integrative model and research agenda. Technological Forecasting and Social Change, 182, 121806.
• Wang, S., Ding, W., Li, J., Yuan, Y., Ouyang, L., & Wang, F. Y. (2019). Decentralized autonomous organizations: Concept, model, and applications. IEEE Transactions on Computational Social Systems, 6(5), 870-878.
• Zhao, X., Ai, P., Lai, F., Luo, X., & Benitez, J. (2022). Task management in decentralized autonomous organization. Journal of Operations Management, 68(6-7), 649-674.

Quiet quitting has emerged as a significant phenomenon in today’s work culture, reflecting growing discontent among employees in response to increasing work pressures, job stressors, and declining real wages (Markotoff, 2022). According to a recent study by Gallup (2022), 19% of the global workforce is now actively disengaged, signaling a potential crisis in workplace culture that could undermine organizational effectiveness. Although no uniform definition exists, "quiet quitting" can be understood as a workplace behavior where employees consciously reduce their engagement to the bare minimum required by their role (Scheyett, 2023). Often framed as a problematic trend (Mahand & Caldwell, 2023), quiet quitting is gaining attention as a reflection of changing employee values, particularly in response to modern work environments shaped by neoliberal transformations (e.g., Serenko, 2024). However, while quiet quitting has gained widespread attention through social media and the mainstream press – especially with viral trends such as 'bare minimum Mondays' (Kato, 2023), it is not an entirely new concept. Historically, practices like 'work to rule' (i.e., 'Dienst nach Vorschrift') were used by workers as a form of industrial action, though quiet quitting is more individualized and represents a silent, personal protest against unfavorable work conditions. In this vein, recent research has highlighted that quiet quitting is associated with both positive and negative outcomes for employees and organizations. On the employee side, many report an improved work-life balance and a stronger emphasis on mental health and personal relationships. However, organizations may experience reduced productivity, lower engagement, and decreased team cohesion (e.g., Scheyett, 2023; Serenko, 2024).

This seminar paper seeks to synthesize the antecedents and consequences of quiet quitting through a qualitative social network analysis (please see Stieglitz et al., 2018), drawing from discussions on social media platforms like Reddit. Hence, the aim is to understand how employees perceive quiet quitting, what drives their behavior (i.e., why do employees engage in quiet quitting), as well as what the intrapersonal consequences are. The Data from Reddit (posts on quiet quitting from r/antiwork) will be handed out to the students (i.e., there is no need to scrape the data yourself).

Literature

• GALLUP (2022). State of the Global Workplace: 2022 Report. https://www.gallup.com/workplace/349484/state-of-the-global-workplace-2022-report.aspx#ite-393218 (retrieved February 20^th, 2025).
• Kato, B. (2023, March 2). Viral Trends: I create ‘bare minimum Mondays’ – Gen Z’s latest low-effort work trend. New York Post. https://nypost.com/2023/03/02/i-created-bareminimum-mondays-gen-zs-latest-low-effort-work-trend/ (retrieved February 20^th, 2025).
• Mahand, T., & Caldwell, C. (2023). Quiet quitting–causes and opportunities. Business and Management Research, 12(1), 9-19.
• Makortoff, K. (2022, July 19). UK pay falls at fastest rate on record as inflation bites – as it happened. The Guardian. https://www.theguardian.com/business/live/2022/jul/19/inflation-jobs-ons-uk-paybusiness-live (retrieved February 20th, 2025).
• Scheyett, A. (2023). Quiet quitting. Social Work, 68(1), 5-7.
• Serenko, A. (2024). The human capital management perspective on quiet quitting: recommendations for employees, managers, and national policymakers. Journal of Knowledge Management, 28(1), 27-43.
• 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.

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., Kununu, 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 firms) will be handed out to the student (i.e., there is no need to scrape the data yourself).

Literature

• 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.

The increasing complexity of modern retail environments, driven by dynamic consumer preferences, omnichannel strategies, and real-time data processing, necessitates advanced decision-making systems. Multi-Agent Systems (MAS), which consist of autonomous agents capable of coordinating, learning and optimizing retail operations, have emerged as a promising solution. Despite their potential, MAS introduce significant governance challenges, particularly concerning control, transparency, and alignment with business objectives.

One of the key theoretical lenses for understanding these challenges is the Agent Theory, which addresses the relationship between a principal (e.g. a retailer) and an agent (e.g. an autonomous MAS component making pricing decisions). In traditional agent relationships, problems such as information asymmetry and goal misalignment arise when agents act in ways that do not fully align with the principal’s interests. When applied to MAS, these issues become even more complex, as autonomous agents operate based on machine learning algorithms, real-time data inputs, and potentially evolving decision-making strategies. Understanding how retailers can govern, monitor and align MAS with their strategic goals is thus a critical research question. 

This seminar paper aims to conduct a structured literature review to identify the fields of application of MAS in retail as well as governance mechanisms that retailers can employ to mitigate agent-related risks. By synthesizing insights from retail applications and governance mechanisms, this research will contribute to both academic discourse and derive practical implications.

Literature

• Lara, C. L., & Wassick, J. (2023). The future of supply chain - a perspective from the process and online retail industries. Computers & Chemical Engineering, 179, 108401. https://doi.org/10.1016/j.compchemeng.2023.108401
• Methenitis, G., Kaisers, M., & La Poutré, H. (2019). Degrees of rationality in Agent-Based retail markets. Computational Economics, 56(4), 953-973. https://doi.org/10.1007/s10614-019-09955-2
• Pavlou, N., Liang, N., & Xue, N. (2007). Understanding and mitigating uncertainty in online exchange Relationships: A Principal-Agent Perspective. MIS Quarterly, 31(1), 105. https://doi.org/10.2307/25148783
• Reim, W., Sjödin, D., & Parida, V. (2018). Mitigating adverse customer behaviour for product-service system provision: An agency theory perspective. Industrial Marketing Management, 74, 150–161. https://doi.org/10.1016/j.indmarman.2018.04.004

Wirkungen eines IT-Systems bezeichnen generell die erwartete Veränderung, die durch die Einführung eintritt. Hierbei ist es als schwierig einzuschätzen, diese Wirkungen Ex-Ante zu identifizieren und ihr Wirtschaftlichkeitspotenzial abzuschätzen. Um Wirkungen präzise zu identifizieren ist ein hohes Maß an Domänen- und Systemwissen notwendig. Sprachmodelle können sich hierbei vielleicht als hilfreich erweisen, dieses Wissen zu imitieren. Dadurch könnte es einfacher sein, konkrete Wirkungen zu identifizieren, gleichzeitig ist durch die hohe Gefahr von Halluzinationen es durchaus denkbar, dass falsche (z.B. nicht mögliche, oder nicht gewollte) Wirkungen identifiziert werden. Daher sollte untersucht werden, inwiefern Sprachmodelle hier eine Unterstützung bieten können, wie diese ausgestaltet werden kann und mit welchen Risiken sie verbunden ist. Ziel der Arbeit ist es zu analysieren inwiefern Sprachmodelle dazu geeignet sind die Identifizierung von Wirkungen zu vereinfachen und welche Schwierigkeiten und Probleme damit verbunden sind. Es soll kritisch evaluiert werden, bei welchen Tätigkeiten Sprachmodelle eine Unterstützung bieten können und wie diese ausgestaltet werden kann.

Literature

• Brynjolfsson, E., & Hitt, L. M. (2000). Beyond Computation: Information Technology, Organizational Transformation and Business Performance. Journal of Economic Perspectives, 14(4), 23–48. https://doi.org/10.1257/jep.14.4.23

• Hicks, M. T., Humphries, J., & Slater, J. (2024). ChatGPT is bullshit. Ethics and Information Technology, 26(2). https://doi.org/10.1007/s10676-024-09775-5

• Schütte, R., Seufert, S., & Wulfert, T. (2022). IT-Systeme wirtschaftlich verstehen und gestalten. Springer Fachmedien Wiesbaden GmbH.

• Ward, J., & Daniel, E. (2012). Benefits Management: How to Increase the Business Value of Your IT Projects. In Benefits Management (2nd ed.). Wiley.

• White, J., Fu, Q., Hays, S., Sandborn, M., Olea, C., Gilbert, H., Elsnashar, A., Spencer-Smith, J., & Schmidt, D. C. (2023). A Prompt Pattern Catalog to Enhance Prompt Engineering with ChatGPT. 1–19.

Prozesse beschreiben das „wie“ des Handelns eines Unternehmens, die Abfolge an Tätigkeiten - das, womit sich die Unternehmensakteure und deren Partner beschäftigen, um geschäftliche Mehrwerte zu schaffen. Werden die richtigen Aktivitäten in effizienter und effektiver Art und Weise miteinander kombiniert, kann eine hohe Prozessqualität erreicht werden. Ebenso ist die Untersuchung von Geschäftsprozessen ein zentraler Bestandteil der Wirtschaftsinformatik, die sich mit deren Analyse, Gestaltung, Optimierung und Digitalisierung befasst. Eine Voraussetzung für eine hohe Qualität von Geschäftsprozessen und ebenso für eine gezielte Verbesserung bestehender Geschäftsprozesse ist die Bewertung von Prozessen und die Identifizierung von Schwachstellen. Das primäre Ziel der Prozessbewertung liegt in der Einschätzung der tatsächlichen Leistungsfähigkeit eines Prozesses im Abgleich mit der gewünschten Leistungsfähigkeit. Eine systematische Prozessbewertung dient dementsprechend sowohl der Einordnung des Ist-Zustandes eines Prozesses als auch der Ableitung von Schwachpunkten und konkreter Handlungsbedarfe zur Prozessverbesserung. 

In dieser Seminararbeit sollen Ansätze und Methoden zur Bewertung der Qualität von (Geschäfts)-prozessen literaturbasiert erarbeitet und sinnvoll sortiert werden.

Literature

• Balasubramanian, S. and Gupta, M. (2005), "Structural metrics for goal based business process design and evaluation", Business Process Management Journal, Vol. 11 No. 6, pp. 680-694. https://doi.org/10.1108/14637150510630855

• Becker, T. (2018). Prozesse in Produktion und Supply Chain optimieren. Springer Verlag.

• Heinrich, R. (2014). Business Process Quality. Springer Verlag.

• Heinrich, R. & Paech, B. (2010). Defining the Quality of Business Processes. In G. Engels, D. Karagiannis, & H. C. Mayr (Hrsg.), Modellierung (S. 133–148).

• Kneuper, R. (2015) Messung und Bewertung von Prozessqualität – Ein Baustein der Governance. HMD 52, 301–311. doi.org/10.1365/s40702-014-0079-z

• Sternad, D., Mödritscher, G. (2018). Prozessqualität. In: Qualitatives Wachstum . Springer Gabler, Wiesbaden. https://doi.org/10.1007/978-3-658-18880-1_11

• Van Looy, A., Shafagatova, A. Business process performance measurement: a structured literature review of indicators, measures and metrics. SpringerPlus 5, 1797 (2016). doi.org/10.1186/s40064-016-3498-1

This seminar thesis aims to explore the various strategies that medium-sized cities adopt to implement Smart City initiatives. Smart City strategies generally refer to the integration of digital technologies and data-driven approaches to improve urban infrastructure, enhance sustainability, and increase the quality of life for residents. The thesis will examine how different cities conceptualize and implement their Smart City strategies, highlighting key success factors such as technological infrastructure, governance models, and citizen engagement. By analyzing these frameworks Smart City strategies in medium-sized cities, the study will offer insights into the commonalities and differences among them. Furthermore, the thesis provides practical recommendations for policymakers and urban planners to develop and refine Smart City strategies tailored to the specific needs and capacities of medium-sized urban areas.

Literature

• Angelidou, M. (2016). Four European smart city strategies. Int'l J. Soc. Sci. Stud., 4, 18.

• Clement, J., Ruysschaert, B., & Crutzen, N. (2023). Smart city strategies–A driver for the localization of the sustainable development goals?. Ecological Economics, 213, 107941.

• Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China. Information Sciences, 58(10), 1-18.

This seminar thesis aims to explore the various design approaches for fostering citizen participation in Smart Cities. Participation in Smart Cities generally refers to the inclusion of citizens in decision-making processes, service co-creation, and urban development through digital and non-digital means. The thesis will examine how different design approaches conceptualize and implement participatory mechanisms, highlighting key factors such as technological platforms, governance structures, and levels of citizen engagement. The research will also delve into established models that categorize participatory approaches and evaluate their effectiveness in achieving inclusive and sustainable urban development. By analyzing these models, the study will offer insights into the strengths and weaknesses of alternative participation strategies in Smart Cities. Furthermore, the thesis provides practical recommendations for city planners and policymakers to design and implement participatory frameworks that effectively integrate citizen input into Smart City initiatives.

Literature

• Levenda, A. M., Keough, N., Rock, M., & Miller, B. (2020). Rethinking public participation in the smart city. The Canadian geographer/Le géographe canadien, 64(3), 344-358.
• Tadili, J., & Fasly, H. (2019, October). Citizen participation in smart cities: A survey. In Proceedings of the 4th International Conference on Smart City Applications (pp. 1-6).
• Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China. Information Sciences, 58(10), 1-18.

This seminar thesis aims to explore the concept of Smart Cities as complex ecosystems, integrating technological, social, and economic components. Smart Cities as ecosystems generally refer to interconnected networks of stakeholders, including governments, businesses, citizens, and technology providers, working together to enhance urban living through digital innovation and data-driven solutions. The thesis will examine how existing research conceptualizes Smart Cities as dynamic ecosystems, highlighting key dimensions such as governance structures, technological infrastructure, and stakeholder collaboration. The research will also delve into established theoretical models that describe Smart City ecosystems and evaluate their effectiveness in fostering sustainable and adaptive urban development. By analyzing the current state of research, the study will offer insights into prevailing trends, gaps, and future directions in Smart City ecosystem studies. Furthermore, the thesis provides practical recommendations for researchers and policymakers to enhance the development and management of Smart Cities as interconnected and evolving systems.

Literature

• Appio, F. P., Lima, M., & Paroutis, S. (2019). Understanding Smart Cities: Innovation ecosystems, technological advancements, and societal challenges. Technological Forecasting and Social Change, 142, 1-14

• Rani, S., Bhambri, P., Kataria, A., & Khang, A. (2022). Smart city ecosystem: Concept, sustainability, design principles, and technologies. In AI-centric smart city ecosystems (pp. 1-20). CRC Press.

• Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China. Information Sciences, 58(10), 1-18.

This seminar thesis aims to explore the specific challenges that medium-sized German cities face in the implementation of Smart City initiatives. Smart City challenges generally refer to obstacles in the adoption and integration of digital technologies, governance structures, and stakeholder collaboration that hinder the successful transformation of urban areas. The thesis will examine how these challenges can be systematically categorized, highlighting key dimensions such as financial constraints, technological limitations, regulatory hurdles, and citizen engagement. The research will also delve into established frameworks for analyzing urban development challenges and apply root cause analysis to identify underlying factors contributing to these difficulties. By structuring and evaluating these challenges, the study will offer insights into common patterns and city-specific obstacles in medium-sized German municipalities. Furthermore, the thesis provides practical recommendations for policymakers, urban planners, and technology providers to develop targeted solutions that address the root causes of Smart City challenges in this urban context.

Literature

• Cui, L., Xie, G., Qu, Y., Gao, L., & Yang, Y. (2018). Security and privacy in smart cities: Challenges and opportunities. IEEE access, 6, 46134-46145.

• Law, K. H., & Lynch, J. P. (2019). Smart city: Technologies and challenges. It Professional, 21(6), 46-51.

• Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China. Information Sciences, 58(10), 1-18.

This seminar thesis aims to explore the various design approaches for digital participation platforms in Smart Cities. Digital participation platforms generally refer to online tools and systems that enable citizens to engage in decision-making processes, contribute to urban development, and interact with local governments in a structured and transparent manner. The thesis will examine how different design approaches conceptualize and implement participation mechanisms, highlighting key factors such as usability, accessibility, data security, and integration with existing municipal services. The research will also delve into established frameworks for evaluating participation platforms and assess their effectiveness in fostering citizen engagement and inclusivity. By analyzing these frameworks, the study will offer insights into the strengths and weaknesses of alternative digital participation platform designs. Furthermore, the thesis provides practical recommendations for city planners and platform developers to design and implement digital participation solutions that effectively enhance civic engagement and Smart City governance.

Literature

• Gil, O., Cortés-Cediel, M. E., & Cantador, I. (2019). Citizen participation and the rise of digital media platforms in smart governance and smart cities. International Journal of E-Planning Research (IJEPR), 8(1), 19-34.
• Levenda, A. M., Keough, N., Rock, M., & Miller, B. (2020). Rethinking public participation in the smart city. The Canadian geographer/Le géographe canadien, 64(3), 344-358.
• Yin, C., Xiong, Z., Chen, H., Wang, J., Cooper, D., & David, B. (2015). A literature survey on smart cities. Science China. Information Sciences, 58(10), 1-18.

Advances in artificial intelligence (AI) are transforming software engineering, with tools like GitHub Copilot demonstrating a substantial enhancement in productivity. Generative agents, such as Claude Code and Cursor, represent an evolution in AI's capabilities by enabling
(semi-)autonomous behaviors. These agents exceed the scope of conventional assistance with coding tasks, encompassing project planning, task delegation, and autonomous execution of programs. This evolution redefines the relationship between developers and IS, transitioning from a tool-based interaction to collaborative partnerships. Thus, it represents a paradigm shift towards agentic information systems (IS), in which agentic IS artifacts in the form of AI agents actively cooperate with humans regarding task completion.

This seminar topic precisely addresses this realm by examining the changing roles and relationships between human and IS in the domain of software engineering. Specifically, it aims to explore how agentic IS influences decision-making, collaboration, and task delegation. The objective of this topic is to conduct expert interviews with people leveraging agentic IS artifacts, specifically generative agents, for software engineering or related tasks and examine their perspective on these changes.

Literature

• Adam, M., Diebel, C., Goutier, M. & Benlian, A. (2024). Navigating autonomy and control in human-AI delegation: User responses to technology- versus user-invoked task allocation. Decision Support Systems, 180, 114193. https://doi.org/10.1016/j.dss.2024.114193

• Baird, A. and Maruping, L. M. (2021). The Next Generation of Research on IS Use: A Theoretical Framework of Delegation to and from Agentic IS Artifacts. MIS Quarterly 45(1). pp.315-341.

• 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

• Sauvola, J., Tarkoma, S., Klemettinen, M., Riekki, J. & Doermann, D. (2024). Future of software development with generative AI. Automated Software Engineering, 31(1). doi.org/10.1007/s10515-024-00426-z

• Suri, S., Das, S. N., Singi, K., Dey, K., Sharma, V. S. & Kaulgud, V. (2023). Software engineering using autonomous agents: Are we there yet? 2021 36th IEEE/ACM International Conference On Automated Software Engineering (ASE), 1855–1857. doi.org/10.1109/ase56229.2023.00174

• Vössing, M., Kühl, N., Lind, M., Satzger, G. (2022). Designing Transparency for Effective Human-AI Collaboration. Inf Syst Front 24, 877–895. doi:10.1007/s10796-022-10284-3

Organisations are increasingly adopting digital, platform-based business models that coordinate value transactions between complementary actors, such as producers and consumers. This externalisation of the value creation process necessitates the deliberate orchestration of providers to realise the ecosystem’s value proposition. In addition, innovation platforms play a crucial role in enabling collaborative value creation. These platforms provide technical and organisational foundations that allow third parties to develop complementary innovations. In such settings, governance must not only ensure stability and coordination but also foster generativity, openness, and innovation dynamics.

Since complementors are typically not exclusively tied to a single platform, platform owners must employ strategic connection and coordination mechanisms to shape and govern the ecosystem—without relying on direct command-and-control approaches. To this end, platform owners implement governance mechanisms that strategically integrate the resources of complementors. Inadequate governance, by contrast, can create structural voids in the ecosystem, thereby reducing value creation potential and weakening network effects. Particularly in innovation platforms, weak governance may hinder innovation uptake, slow down ecosystem evolution, or lead to fragmentation.

Despite the increasing importance of platform governance, scholarly discourse on this topic remains limited. This study addresses this gap by synthesising the current body of research through a systematic literature review and deriving a corresponding research agenda.

Research Questions:

• RQ1: What are the key topics in Information Systems (IS) research at the intersection of platforms, ecosystems, and governance?
• RQ2: Which aspects of platform governance—particularly in the context of innovation platforms—warrant further investigation?

Methodological Approach:
A systematic literature review of leading academic journals (Basket of Eight) and major conferences (ICIS, ECIS, PACIS, AMCIS, HICSS).

Objective:
The generativity of platforms—especially innovation platforms—is substantially shaped by governance mechanisms. To understand these dynamics, this study systematically synthesises the current research landscape, mapping the various streams of inter- and intra-organisational governance and positioning them within academic discourse.

Based on the analysed literature, a governance framework will be developed that delineates key concepts and their interrelations. Particular emphasis will be placed on identifying which governance mechanisms have been examined across different platform types and domains. The insights gained will serve as the foundation for formulating a future research agenda.

Literature

• 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 In Citavi anzeigen.
• 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 In Citavi anzeigen.
• 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 In Citavi anzeigen.
• 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 rasanten Fortschritte in der generativen künstlichen Intelligenz (GenAI) verändern nicht nur die Art und Weise, wie Inhalte erzeugt werden, sondern ermöglichen zunehmend autonome, zielgerichtete Entscheidungs- und Handlungsprozesse. Ein besonders vielversprechendes Anwendungsfeld für embodied GenAI – also KI, die in physische Systemen integriert ist – ist der Schutz der zivilen Bevölkerung, insbesondere in den Bereichen Wasserrettung, Katastrophenschutz und Brandschutz.

Die Überwachung von Gewässern stellt Rettungsdienste vor große Herausforderungen: Gefährliche Strömungen, plötzlich auftretende Notfälle und unerlaubte Aktivitäten erfordern ein hohes Maß an Aufmerksamkeit und eine schnelle Reaktionsfähigkeit. Ähnliche Herausforderungen existieren im Katastrophenschutz, etwa bei Hochwasser, Erdbeben oder bei Industrieunfällen, sowie im Brandschutz, wo rasche Lageerfassung und gezielte Intervention entscheidend sind. Der Einsatz autonomer KI-gesteuerter Systeme wie schwimmende oder fliegende Drohnen sowie bodengebundene Roboter könnte die Arbeit der Rettungs- und Einsatzkräfte in all diesen Bereichen signifikant unterstützen. Durch die Kombination von Sensordatenerfassung, Echtzeit-Analyse und intelligentem Verhalten könnten solche Systeme Gefahrensituationen frühzeitig erkennen, Personen in Not lokalisieren und erforderliche Rettungsmaßnahmen schnell und effizient initiieren. Dies ist besonders relevant, da viele Rettungs- und Katastrophenschutzorganisationen auf ehrenamtliches Personal angewiesen und chronisch unterbesetzt sind.

Zielsetzung

Die Seminararbeit soll eine systematische Untersuchung bestehender und prototypischer (1) KI-Systeme sowie (2) Robotik-Anwendungen in den Bereichen Wasserrettung, Katastrophenschutz und Brandschutz durchführen. Neben einer wissenschaftlichen Literaturrecherche sollen praxisnahe Quellen von Rettungsverbänden, Feuerwehren und Katastrophenschutzbehörden einbezogen werden, um einen umfassenden Überblick über den aktuellen Stand sowie zukünftige Potenziale zu erhalten.

Forschungsfragen

• RQ1: Wie kann künstliche Intelligenz (AI) in den Bereichen Wasserrettung, Katastrophenschutz und Brandschutz eingesetzt werden? Welche Anwendungen existieren bereits?
• RQ2: Wie wird Robotik derzeit in diesen Bereichen genutzt, und welche technologischen Entwicklungen zeichnen sich ab?
• RQ3: Welche Potenziale bieten autonome Roboter mit embodied GenAI für zukünftige Schutzmaßnahmen der zivilen Bevölkerung? Wie können diese Systeme effektiv mit menschlichen Einsatzkräften zusammenarbeiten, welche neuen Einsatzszenarien ergeben sich, und wo liegen potenzielle Grenzen und Risiken?

Die Arbeit verfolgt einen interdisziplinären Ansatz, der technologische, organisatorische und ethische Perspektiven berücksichtigt. Die Methodik der systematischen Literaturrecherche nach Webster und Watson (2002) sowie vom Brocke et al. (2009) wird hierfür angewendet.

Literature

• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1  

• Baywatch 2.0: Rescuing drowning persons with an underwater robotic lifeguard. In OCEANS 2022, Hampton Roads (pp. 1-5). IEEE.nformation Technology, 102(24).

• Duan, J., Yu, S., Tan, H. L., Zhu, H., & Tan, C. (2022). A Survey of Embodied AI: From Simulators to Research Tasks. IEEE Transactions on Emerging Topics in Computational Intelligence, 6(2), 230–244. https://doi.org/10.1109/TETCI.2022.3141105

• Feuerriegel S, Hartmann J, Janiesch C, Zschech P (2024). Generative AI. Business & Information Systems Engineering 66(1):111–126. doi:10.1007/s12599-023-00834-7

• Hemalatha, S., Adavala, D. K. M., Shekhar, S. C., Kumar, P. S., Venkataramanan, A., & Malleswari, D. N. (2024). Proposal of enhancing water safety – An autonomous robot for drowning prevention. Journal of Theoretical and Applied Information Technology, 102(24)Paolo, G., Gonzalez-Billandon, J., & Kégl, B. (2024). Position: A Call for Embodied AI. In Forty-first International Conference on Machine Learning. (Vol. 235, pp. 39493–39508).

• Pfeifer, R., & Iida, F. (2004). Embodied Artificial Intelligence: Trends and Challenges. In D. Hutchison, T. Kanade, J. Kittler, J. M. Kleinberg, F. Mattern, J. C. Mitchell, M. Naor, O. Nierstrasz, C. Pandu Rangan, B. Steffen, M. Sudan, D. Terzopoulos, D. Tygar, M. Y. Vardi, G. Weikum, F. Iida, R. Pfeifer, L. Steels, & Y. Kuniyoshi (Eds.), Lecture Notes in Computer Science. Embodied Artificial Intelligence (Vol. 3139, pp. 1–26). Springer Berlin Heidelberg. doi.org/10.1007/978-3-540-27833-7_1

• Shatnawi, M., Albreiki, F., Alkhoori, A., Alhebshi, M., & Shatnawi, A. (2024). Advances and Challenges in Automated Drowning Detection and Prevention Systems. Information, 15(11), 721.

• vom Brocke, J., Simons, A., Niehaves, B., Riemer, K., Plattfaut, R., & Cleven, A. (2009). Reconstructing The Giant: On The Importance Of Rigour In Documenting The Literature Search Process. ECIS 2009 Proceedings.

• 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.

Praxisbeispiele:

• Bundesamt für Bevölkerungsschutz und Katastrophenhilfe (BBK). (2023).Drohnen im Bevölkerungsschutz. Bevölkerungsschutz-Magazin, 04/2023. Abgerufen von https://www.bbk.bund.de/SharedDocs/Downloads/DE/Mediathek/Publikationen/BSMAG/bsmag-23-04.pdf (Zugriff am 24.03.2025).

• Deutsche Lebens-Rettungs-Gesellschaft e. V. (DLRG). Drohnen im Wasserrettungsdienst. Abgerufen von https://www.dlrg.de/fuer-mitglieder/drohnen/ (Zugriff am 21.03.2025).

• Deutsche Lebens-Rettungs-Gesellschaft e. V. (DLRG), Ortsgruppe Heppenheim. Der Tauchroboter – Unterstützung im Einsatz. Abgerufen von https://heppenheim.dlrg.de/einsatz/der-tauchroboter/ (Zugriff am 21.03.2025)

• Norddeutscher Rundfunk (NDR). Ein Roboter als treuer Freund der Feuerwehr.Schleswig-Holstein Magazin, 05.06.2024. Abgerufen von https://www.ardmediathek.de/video/schleswig-holstein-magazin/ein-roboter-als-treuer-freund-der-feuerwehr/ndr/Y3JpZDovL25kci5kZS9kMzc2NmYyMS0yMTYyLTQyNzQtYmU5Ni04YjI2MjQ2MjQxNDk (Zugriff am 24.03.2025).

• Royal Life Saving Society UK (RLSS UK). Assisted Lifeguard Technology. Abgerufen von https://www.rlss.org.uk/assisted-lifeguard-technology (Zugriff am 21.03.2025).

• Surf Life Saving Western Australia (SLSWA). Drone Patrol – Enhancing Coastal Safety. Abgerufen von https://www.mybeach.com.au/coastal-safety/lifesaving-services/slswa-drone-patrol/ (Zugriff am 21.03.2025).

• von Hallern, H. (2025). KI im Rettungseinsatz: Mit autonomen Robotern gegen Waldbrände.NDR. Abgerufen von https://www.ndr.de/nachrichten/schleswig-holstein/KI-im-Einsatz-Mit-autonomen-Robotern-gegen-Waldbraende,roboter810.html (Zugriff am 24.03.2025).

The advancements of generative AI (GenAI) technologies has opened up unprecedented opportunities for innovation across various sectors, including healthcare, education, environmental protection, and social welfare. These technologies have the potential to revolutionize how we address complex societal challenges by generating novel solutions, enhancing decision-making processes, and automating tasks to improve efficiency and accessibility. However, harnessing the power of GenAI for the greater good requires careful consideration of ethical implications, inclusivity, and the potential for unintended consequences. The ongoing discourse on GenAI seems to praise the promises of AI (i.e., utopian visions of a world free of barriers) and the dangers (i.e., dystopian visions of machines taking over the world). The objective of this topic is to offer a careful examination of the challenges faced in managing this powerful set of technologies for individuals, organizations, and society to harness GenAI for greater good. Thus, the work should discuss on GenAI perspectives from organizational and societal levels. Therefore, this research topic will be explored empirically using a qualitative approach and interviews will be conducted with interview partners who have gained experience with GenAI in an organizational or societal context.

Literature

• Banh, L. & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets 33(63). DOI: 10.1007/s12525-023-00680-1 

• Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. doi:10.1016/j.ijinfomgt.2023.102642

• Jarvenpaa, S. and Klein, S. (2024). New Frontiers in New Frontiers in Information Systems Theorizing: Human-gAI Collaboration. Journal of the Association for Information Systems, 25(1), 110-121. DOI: 10.17705/1jais.00868

• Sabherwal, R. and Grover, V. (2024). The Societal Impacts of Generative Artificial Intelligence: A Balanced Perspective. Journal of the Association for Information Systems, 25(1), 13-22. DOI: 10.17705/1jais.00860

• Strobel, G.; Banh, L.; Möller, F.; Schoormann, T. (2024). Exploring Generative Artificial Intelligence: A Taxonomy and Types. In: Proceedings of the 57th Hawaii International Conference on System Sciences (HICSS). Hawaii, USA

The variability of platform governance strategies and the degree of platform openness play a crucial role in shaping data-sharing practices, influencing both the quantity and quality of shared data. Governance mechanisms such as resourcing and securing, as outlined by Ghazawneh and Henfridsson (2013), determine how data flows within and beyond platform boundaries. When governance emphasizes strong resourcing—such as providing well-documented APIs, incentivizing third-party contributions, and ensuring reliable infrastructure—positive network externalities emerge, encouraging increased data sharing among users and developers (Tiwana, 2015; Tiwana, 2013; Karhu et al., 2023). However, overly restrictive governance and excessive security controls can create negative network externalities by limiting data access, reducing interoperability, and discouraging participation (Wareham, et al., 2014; Karhu et al., 2023). The openness of a platform further shapes these dynamics; while open platforms facilitate broader data exchange and innovation, they also introduce risks related to data security, quality control, and competitive misuse (Boudreau, 2010; Ofe & De Reuver, 2024).

The impact of governance variability on data-sharing practices also depends on how platforms balance openness with control. A more open, decentralized governance model, characterized by accessible data-sharing mechanisms and transparent policies, fosters network effects by enabling external contributors to enhance platform value through innovation and services, but risks a more chaotic environment (Eaton et al., 2015; Hanseth & Ciborra, 2007). Conversely, excessively closed, centralized platforms risk stifling innovation by restricting data availability, reducing the likelihood of network effects taking hold, but allows for greater control. 

This seminar topic thereby asks: How do health data platforms navigate the tension of securing its data while opening up? How do they increase data-sharing practices while ensuring security, fostering trust, and sustaining long-term platform viability?

Literature

• Boudreau, K. J. (2010). Open platform strategies and innovation: Granting access vs. devolving control. Management Science, 56(10), 1849–1872.

• Eaton, B., Elaluf-Calderwood, S., Sørensen, C., & Yoo, Y. (2015). Distributed tuning of boundary resources: The case of Apple's iOS service system. MIS Quarterly, 39(1), 217–243.

• Ghazawneh, A., & Henfridsson, O. (2013). Balancing platform control and external contribution in third‐party development: the boundary resources model. Information systems journal, 23(2), 173-192.

• Hanseth, O., & Ciborra, C. (Eds.). (2007). Risk, complexity and ICT. Edward Elgar Publishing.

• Karhu, K., Heiskala, M., Ritala, P., & Thomas, L. D. (2024). Positive, negative, and amplified network externalities in platform markets. Academy of Management Perspectives, 38(3), 349-367.

• Ofe, H., & de Reuver, M. (2024). Rethinking Openness in Data Platforms: The Impact of Data Artifact Characteristics on Platform Openness: Consequences, Scope and Mechanisms. Business & Information Systems Engineering, 1-12.

• Tiwana, A. (2013). Platform ecosystems: Aligning architecture, governance, and strategy. Newnes.

• Tiwana, A. (2015). Evolutionary competition in platform ecosystems. Information Systems Research, 26(2), 266–281.

• Wareham, J., Fox, P. B., & Giner, J. L. (2014). Technology ecosystem governance. MIS Quarterly, 38(2), 455–475.

Other potentially helpful literature:

• Levina, Olga; Mattern, Saskia; and Kiefer, Felix, "Extending Digital Platform Governance with Legal Context" (2019). AMCIS 2019 Proceedings. 4.

Fostering incentivization for data sharing in health data platforms requires a careful balance between platform design, governance structures, and regulatory compliance. A well-designed health data platform should incorporate mechanisms that enhance trust, ensure security, and provide tangible benefits for data contributors. Incentive structures can take various forms, for example financial incentives, such as direct compensation, reduced service fees, or data-driven value-sharing models, which have been shown to encourage participation among healthcare providers and patients, though monetary rewards alone seem to be insufficient (Mettler & Winter, 2016). Alternatively, data cooperatives allow participants to retain ownership over their data while benefiting from aggregated insights, fostering a sense of shared value and mutual benefit (Sharon, 2020). Moreover, the architecture of the platform—such as its openness, interoperability, and usability—plays a crucial role in encouraging data sharing. Platforms that implement modular design principles and standardized data formats lower entry barriers for healthcare providers and researchers, fostering positive network externalities and enhancing participation (Tiwana, 2015; Tiwana, 2013). Additionally, secure and privacy-preserving technologies, such as federated learning and differential privacy, can mitigate data-sharing risks while maintaining regulatory compliance (Ransbotham et al., 2016).

The European Health Data Space (EHDS) will play a transformative role in incentivizing data sharing by setting a standardized legal and technical framework across European Union member states by enhancing data accessibility while ensuring robust data protection and patient control, which can significantly improve trust in health data platforms (European Commission, 2022). By mandating interoperability and facilitating cross-border data exchange, the EHDS creates an environment where data-sharing incentives extend beyond individual organizations to the broader healthcare ecosystem. However, successful implementation will depend on aligning platform design with regulatory requirements, ensuring that health data platforms integrate mechanisms for patient consent management, compliance automation, and ethical data use (Henfridsson et al., 2014). Platforms that effectively balance these factors will not only meet regulatory standards but also drive sustained engagement through trust and value-driven data-sharing incentives. 

This seminar thereby asks: How can health (data) platforms incentivize data sharing? How do they identify, establish and frame the means of incentivizing data-sharing practices? 

Furthermore, this topic will focus on whether the identified approaches would be practical within the new EHDS regulatory framework. 

Literature

• European Commission. (2022). European Health Data Space: Regulation proposal. Retrieved from https://health.ec.europa.eu

• Henfridsson, O., Mathiassen, L., & Svahn, F. (2014). Managing technological change in the digital age: the role of architectural frames. Journal of Information Technology, 29(1), 27-43.

• Mettler, T., & Winter, R. (2016). Are business users social? A design experiment exploring information sharing in enterprise social systems. Journal of Management Information Systems, 33(1), 268–299.

• Ransbotham, S., Kiron, D., & Prentice, P. K. (2016). Minding the analytics gap. MIT Sloan Management Review, 57(3), 63–68.

• Sharon, T. (2020). Blind-sided by privacy? Digital contact tracing, the Apple/Google API and big tech’s newfound role as global health policy makers. Ethics and Information Technology, 23(1), 45–57.

• Tiwana, A. (2013). Platform ecosystems: Aligning architecture, governance, and strategy. Newnes.

• Tiwana, A. (2015). Evolution of platform-based ecosystems. Information Systems Research, 26(2), 266–281.

Other potential helpful literature:

• de Reuver, Mark; Sørensen, Carsten; and Basole, Rahul C. (2018) "The Digital Platform: A Research Agenda," Journal of Information Technology: Vol. 33: Iss. 2, Article 3.

• Lessard, Lysanne and de Reuver, Mark, "Describing Health Service Platform Architectures: A Guiding Framework" (2019). AMCIS 2019 Proceedings. 14.

Founders and entrepreneurial ventures are increasingly engaged in addressing ecological challenges through innovation. Here, digital products and services are increasingly considered to bear potential to leverage positive change (Gartenberg, 2022; Haldar, 2019; Kotlarsky et al., 2023). These ventures and products are new, and entrepreneurs need to legitimize their activities to make them understandable and justify their actions (Snihur et al., 2022; Lounsbury, 2019, Garud et al. 2013). When ventures operate in a new market or ‘field’, they turn to strategic framing to woo audiences such as customers and investors (Snihur et al. 2022). Strategic framing produces legitimacy through language (e.g., written or spoken speech) and emphasis (e.g., highlights). When attempting to create a field as regenerative, such as with “regenerative finance”, ventures need to set boundaries around that field and produce collective identity, i.e., contrast old from new ways of doing things, foster identity, and motivate audiences to join (Benford & Snow 2000; Silver, 1997). Emotional framings, such as ‘regenerative’ framing, can lead to substantial changes in field perception of clients and investors (Taeuscher & Rothe 2024, Weber et al. 2009). Within this seminar, we therefore explore how ventures framed the field of “regenerative finance” and compare it to their attempts to create audience support. For this, the student will conduct a thematic cluster analysis on press releases, websites, and reports and compare it to accumulated investments and attention.

RQ: How does ‘regenerative’ framing by entrepreneurial ventures influence support of clients and investors in Finance?

Literature

• Benford, R. D., & Snow, D. A. (2000). Framing Processes and Social Movements: An Overview and Assessment. Annual Review of Sociology, 26(1), 611–639. doi.org/10.1146/annurev.soc.26.1.611
• Gartenberg, C. (2022). Purpose-Driven Companies and Sustainability. In G. George, M. R. Haas, H. Joshi, A. M. McGahan, & P. Tracey (Eds.), Handbook on the Business of Sustainability. Edward Elgar Publishing. doi.org/10.4337/9781839105340.00009
• Garud, R., Lant, T. K., & Schildt, H. A. (2019). Generative imitation, strategic distancing and optimal distinctiveness during the growth, decline and stabilization of Silicon Alley. Innovation, 21(1), 187–213. doi.org/10.1080/14479338.2018.1465822
• Haldar, S. (2019). Towards a conceptual understanding of sustainability-driven entrepreneurship. In Corporate Social Responsibility and Environmental Management (Vol. 26, Issue 6, pp. 1157–1170). doi.org/10.1002/csr.1763
• 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. doi.org/10.17705/1jais.00825
• Lounsbury, M. (with Glynn, M. A.). (2019). Cultural Entrepreneurship: A New Agenda for the Study of Entrepreneurial Processes and Possibilities (1st ed). Cambridge University Press.
• Silver, I. (1997). Constructing “Social Change” through Philanthropy: Boundary Framing and the Articulation of Vocabularies of Motives for Social Movement Participation*. Sociological Inquiry, 67(4), 488–503. doi.org/10.1111/j.1475-682X.1997.tb00449.x
• Snihur, Y., Thomas, L. D. W., Garud, R., & Phillips, N. (2022). Entrepreneurial Framing: A Literature Review and Future Research Directions. Entrepreneurship Theory and Practice, 46(3), 578–606. doi.org/10.1177/10422587211000336
• Taeuscher, K., & Rothe, H. (2024). Entrepreneurial framing: How category dynamics shape the effectiveness of linguistic frames. Strategic Management Journal, 45(2), 362-395.
• Weber, K., Rao, H., & Thomas, L. G. (2009). From streets to suites: How the anti-biotech movement affected German pharmaceutical firms. American Sociological Review, 74(1), 106–127.

Founders and entrepreneurial ventures are increasingly engaged in addressing ecological challenges through innovation. Here, digital products and services are increasingly considered to bear potential to leverage positive change (Gartenberg, 2022; Haldar, 2019; Kotlarsky et al., 2023). These ventures and products are new, and entrepreneurs need to legitimize their activities to make them understandable and justify their actions (Snihur et al., 2022; Lounsbury, 2019, Garud et al. 2013). When ventures operate in a new market or ‘field’, they turn to strategic framing to woo audiences such as customers and investors (Snihur et al. 2022). Strategic framing produces legitimacy through language (e.g., written or spoken speech) and emphasis (e.g., highlights). When attempting to create a field as regenerative, such as with “clean tech” or “regenerative finance”, ventures need to set boundaries around that field and produce collective identity, i.e., contrast old from new ways of doing things, foster identity, and motivate audiences to join (Benford & Snow 2000; Silver, 1997). 

Emotional framings, such as ‘regenerative’ or ‘clean’ framing, can lead to substantial changes in field perception of clients and investors (Taeuscher & Rothe 2024, Weber et al. 2009). Within this seminar, we therefore explore how ventures framed the field of “clean tech” and compare it to their attempts to create audience support. For this, the student will conduct a thematic cluster analysis on press releases, websites, and reports and compare it to accumulated investments and attention, in a self-selected subdomain of “clean tech” (e.g., clean tech and AI).

RQ: How does ‘clean’ framing by entrepreneurial ventures influence support of clients and investors in Tech?

Literature

• Benford, R. D., & Snow, D. A. (2000). Framing Processes and Social Movements: An Overview and Assessment. Annual Review of Sociology, 26(1), 611–639. doi.org/10.1146/annurev.soc.26.1.611
• Gartenberg, C. (2022). Purpose-Driven Companies and Sustainability. In G. George, M. R. Haas, H. Joshi, A. M. McGahan, & P. Tracey (Eds.), Handbook on the Business of Sustainability. Edward Elgar Publishing. doi.org/10.4337/9781839105340.00009
• Garud, R., Lant, T. K., & Schildt, H. A. (2019). Generative imitation, strategic distancing and optimal distinctiveness during the growth, decline and stabilization of Silicon Alley. Innovation, 21(1), 187–213. doi.org/10.1080/14479338.2018.1465822
• Haldar, S. (2019). Towards a conceptual understanding of sustainability-driven entrepreneurship. In Corporate Social Responsibility and Environmental Management (Vol. 26, Issue 6, pp. 1157–1170). doi.org/10.1002/csr.1763
• 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. doi.org/10.17705/1jais.00825
• Lounsbury, M. (with Glynn, M. A.). (2019). Cultural Entrepreneurship: A New Agenda for the Study of Entrepreneurial Processes and Possibilities (1st ed). Cambridge University Press.
• Silver, I. (1997). Constructing “Social Change” through Philanthropy: Boundary Framing and the Articulation of Vocabularies of Motives for Social Movement Participation*. Sociological Inquiry, 67(4), 488–503. doi.org/10.1111/j.1475-682X.1997.tb00449.x
• Snihur, Y., Thomas, L. D. W., Garud, R., & Phillips, N. (2022). Entrepreneurial Framing: A Literature Review and Future Research Directions. Entrepreneurship Theory and Practice, 46(3), 578–606. doi.org/10.1177/10422587211000336
• Taeuscher, K., & Rothe, H. (2024). Entrepreneurial framing: How category dynamics shape the effectiveness of linguistic frames. Strategic Management Journal, 45(2), 362-395.
• Weber, K., Rao, H., & Thomas, L. G. (2009). From streets to suites: How the anti-biotech movement affected German pharmaceutical firms. American Sociological Review, 74(1), 106–127.

Climate tech, tokenization of biodiversity assets, artificial intelligence for sustainability. Digital technologies are used as vehicles and tools to tackle environmental problems (Bermeo-Almeida et al., 2018; Oberhauser, 2019; Schletz et al., 2023; Schoormann et al., 2023). What is driving entrepreneurs to engage in environmental protection or in finding solutions for environmental problems (Melville, 2010; Saldanha et al., 2022)? The search for purposeful work or a felt closeness to nature can be reasons for such engagement (Gregori et al., 2021; Intergovernmental Science-Policy Platform On Biodiversity And Ecosystem Services, 2022; Rastogi & Sharma, 2018).

• Which role and function do values play in the entrepreneurs’ mission? How is nature and its value considered in the entrepreneurs’ values and in which way does this influence the entrepreneurial endeavor? (literature review, application of results on webpage contents (several cases) including critical assessment of resulting analysis)

Literature

• Bermeo-Almeida, O., Cardenas-Rodriguez, M., Samaniego-Cobo, T., Ferruzola-Gómez, E., Cabezas-Cabezas, R., & Bazán-Vera, W. (2018). Blockchain in agriculture: A systematic literature review. In R. Valencia-García, G. Alcaraz-Mármol, J. Del Cioppo-Morstadt, N. Vera-Lucio, & M. Bucaram-Leverone (Eds.), Technologies and innovation (pp. 44–56). Springer International Publishing.

• Gregori, P., Holzmann, P., & Wdowiak, M. A. (2021). For the sake of nature: Identity work and meaningful experiences in environmental entrepreneurship. Journal of Business Research, 122, 488–501. doi.org/10.1016/j.jbusres.2020.09.032

• Intergovernmental Science-Policy Platform On Biodiversity And Ecosystem Services. (2022). Summary for policymakers of the methodological assessment of the diverse values and valuation of nature of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES) (Version 1.2). Zenodo. doi.org/10.5281/ZENODO.6522392

• Melville, N. P. (2010). Information Systems Innovation for Environmental Sustainability. MIS Quarterly, 34(1), 1–21.

• Oberhauser, D. (2019). Blockchain for Environmental Governance: Can Smart Contracts Reinforce Payments for Ecosystem Services in Namibia? Frontiers in Blockchain, 2, 21. doi.org/10.3389/fbloc.2019.00021

• Rastogi, P., & Sharma, R. (2018). Ecopreneurship for Sustainable Development. In J. Marques (Ed.), Handbook of Engaged Sustainability (pp. 991–1016). Springer International Publishing. doi.org/10.1007/978-3-319-71312-0_46

• Saldanha, T., Mithas, S., Khuntia, J., Whitaker, J., & Melville, N. (2022). How Green Information Technology Standards and Strategies Influence Performance: Role of Environment, Cost and Dual Focus. MIS Quarterly, 46(4), 2367–2386.

• Schletz, M., Constant, A., Hsu, A., Schillebeeckx, S., Beck, R., & Wainstein, M. (2023). Blockchain and regenerative finance: Charting a path toward regeneration. Frontiers in Blockchain, 6, 1165133. doi.org/10.3389/fbloc.2023.1165133

• Schoormann, T., Strobel, G., Möller, F., Petrik, D., & Zschech, P. (2023). Artificial Intelligence for Sustainability—A Systematic Review of Information Systems Literature. CAIS, 52, 199–237. doi.org/10.17705/1CAIS.05209

Despite all the potential advantages of digital transformation for organizations, industries and society as a whole like combating societal grand challenges and promoting the common good (Pappas et al., 2023), and despite all attention from academia and practice (Chen & King, 2022), digital transformation often fails (Wade & Shan, 2020). As digital transformation goes far beyond previous IT-enabled organizational transformation in terms of potential impact, dynamics, and scope (Carroll et al., 2023; Vial, 2019), managing digital transformation is a complex issue that affects the transformation of numerous levels of an organization and affects organizations as a whole. One prime challenge is to effectively respond to digital disruption that is characterized by changing competitive landscapes, market dynamics, and changing customer expectations and behavior (Vial, 2019; Kraus et al., 2022).

Dynamic capabilities refer to a firm's ability to integrate, build, and reconfigure internal and external resources to address rapidly changing environments (Steininger et al., 2022; Teece et al., 1997). The concept, emphasizes the importance of not just having resources (like financial capital, human skills, and technology) but also the ability to adapt and innovate in response to market shifts and disruptions. Managing digital transformation thus involves understanding how organizations can adapt, integrate, and reconfigure internal and external resources to proactively address digital disruption fueled by the increasing use of digital technologies across industries (Vial, 2019; Kraus et al., 2022). By successfully building, developing and leveraging sensing capabilities, organizations can respond to disruptive changes early and successfully compete in an increasingly digital world.

As part of this seminar paper, the role of dynamic capabilities in responding to digital disruption will be analyzed and discussed. To this end, characteristics and dimensions of digital disruption will be identified, as well as dynamic capabilities needed to respond to digital disruption by conducting a systematic analysis of the literature. Based on this, implications for management and further research will be discussed.

Literature

• 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.

• Chen, S., & King, J. L. (2022). Policy and imprecise concepts: the case of digital transformation. Journal of the Association for Information Systems, 23(2), 401-407. 

• 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.

• Pappas, I. O., Mikalef, P., Dwivedi, Y. K., Jaccheri, L., & Krogstie, J. (2023). Responsible digital transformation for a sustainable society. Information Systems Frontiers, 25(3), 945-953. 

• Steininger, D. M., Mikalef, P., Pateli, A., & Ortiz-de-Guinea, A. (2022). Dynamic Capabilities in Information Systems Research: A Critical Review, Synthesis of Current Knowledge, and Recommendations for Future Research. Journal of the Association for Information Systems, 23(2), 447.

• Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509-533.

• 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).

Having understood the significant effects of artificial intelligence (AI) on operational efficiency (Cui et al., 2024), organizations strategically focus on deploying and providing artificial intelligence in their processes and structures. Organizational members across departments now routinely and increasingly interact with conversational AI to reduce highly repetitive tasks and focus more on higher-level cognitive tasks at work, with 88% of AI users coming from non-technical professions (De Smet et al., 2023). AI interactions are likely to become even more common as generative AI is increasingly used in roles and functions previously reserved for humans, such as HR, IT, finance, or customer service and support (Tey et al., 2024). Estimations of the World Economic Forum indicate that until 2020, AI-driven automation will autonomously take over one-third of all work tasks (Di Battista et al., 2025). 

The use of GenAI brings implications for the working environment that are important today and in the future in order to generate hoped-for efficiencies: the way of working through the use of AI in business processes is changing and interpersonal collaboration in teams is being influenced as AI is used alongside human colleagues for monitoring, coordination and operational work. With AI being increasingly embedded in collaborative processes, this technology challenges the understanding of the technology itself (Larson & DeChurch, 2020), traditional notions of teamwork (Richter & Schwabe, 2025), and intragroup processes (Zercher et al., 2023).

Literature

• Berente, N., Gu, B., Recker, J., & Santhanam, R. (2021). Managing artificial intelligence. MIS quarterly, 45(3).

• Candelon, F., Krayer, L., Rajendran, S., & Martinez, D. Z. (2023). How people Can create–and destroy–value with generative AI. BCG Global, 21.

• Cui, et al. (2024). The effects of generative ai on high skilled work: Evidence from three field experiments with software developers. Available at SSRN 4945566.

• Mayer, H., Yee, L., Chui, M. & Roberts, R. (2025), Superagency in the workplace: Empowering people to unlock AI’s full potential. McKinsey & Company 

• Peifer, Y., Jeske, T., & Hille, S. (2022). Artificial intelligence and its impact on leaders and leadership. Procedia computer science, 200, 1024-1030.

• Richter, A., & Schwabe, G. (2025). “There is No ‘AI’in ‘TEAM’! Or is there?”–Towards meaningful human-AI collaboration. Australasian Journal of Information Systems, 29.

• Toner-Rodgers, A. (2024). Artificial Intelligence, Scientific Discovery, and Product Innovation.

• Watson, G. J., Desouza, K. C., Ribiere, V. M., & Lindič, J. (2021). Will AI ever sit at the C-suite table? The future of senior leadership. Business Horizons, 64(4), 465-474.

Artificial Intelligence (AI) has undergone significant advancements in the recent years, with one of the most recent developments being "Agentic AI"—AI systems capable of autonomous decision-making and proactive execution of complex tasks without direct human intervention (Acharya et al., 2025). These systems offer substantial potential for optimizing automation, enhancing decision-making processes, and improving operational efficiency across various domains (Parab 2024). In particular, Agentic AI is expected to fundamentally transform business processes by enabling end-to-end automation, real-time process adaptation, and intelligent exception handling (Samdani et al., 2023). However, they also raise critical concerns regarding ethical considerations, security implications, accountability, and regulatory compliance (Mukherjee & Chang, 2025). As Agentic AI continues to evolve, a systematic examination of the current research landscape is essential to clarify both its potential benefits and associated risks.

Despite the increasing academic interest in Agentic AI, there remains a lack of consensus regarding its precise definition, classification, and differentiation from other AI paradigms. The rapid pace of development in this field complicates the identification of key trends, methodological approaches, and theoretical underpinnings. Furthermore, the challenges associated with the reliability, interpretability, and unintended consequences of autonomous AI systems necessitate a structured review of existing literature. A systematic literature review is necessary to synthesize current knowledge, identify research gaps and provide a basis for future advances in the field.

In this seminar paper, you will conduct a structured literature review to examine the current state of research on Agentic AI. Your study will aim to systematically identify key technological advancements, conceptual frameworks, and prevailing challenges within this research domain. Where applicable, your review may place a specific focus on the impact of Agentic AI on business process automation, orchestration, and governance. Additionally, depending on the scope of your analysis, qualitative insights may be incorporated by evaluating case studies or industry applications to explore real-world deployments of Agentic AI. Where feasible, engagement with industry stakeholders or an assessment of practical implementations could further enrich the findings by contextualizing theoretical insights with empirical evidence.

Literature

• Acharya, D. B., Kuppan, K., & Divya, B. (2025). Agentic AI: Autonomous Intelligence for Complex Goals–A Comprehensive Survey. IEEE Access.
• Mukherjee, A., Chang, H. H. (2025). Agentic AI: Autonomy, Accountability, and the Algorithmic Society. arxiv.org/pdf/2502.00289v3.
• Parab, G. U. (2024). Agentic AI in Data Analytics: Transforming Autonomous Insights and Decision-Making. International Journal of Scientific Research in Computer Science Engineering and Information Technology, 10(6), 1752-1759.

• Samdani, G., Paul, K., & Saldanha, F. (2023). Agentic AI in the Age of Hyper-Automation. World Journal of Advanced Engineering Technology and Sciences, 8(1), 416-427.