Artificial intelligence (AI) in medicine shows potential for various clinical tasks such as imaging, diagnosis, prognosis and treatment planning. Because one treatment may show different effects when applied to different patients, predicting individual treatment effects (ITE) could aid doctors in selecting the right treatment for each patient. This ITE describes the difference between the expected outcome if a patient receives treatment, and the expected outcome if no treatment is prescribed. However, using AI-based ITE predictions in practice requires that doctors understand the system’s reasoning and are able to detect potential errors. This is not the case for many modern, highly performant AI systems, so-called black-box models. Many established methods for explaining AI outputs to promote understanding of black-box AI models suffer from the “consumer-creator gap” (Ehsan et al., 2024), as they were not designed with doctors’ explanation needs in mind. Hence, other approaches to making AI-based predictions of ITE understandable are necessary.

In a previous study, we collected visualizations of medical ITE predictions and conducted interviews with doctors to assess their explanatory potential. Therefore, a qualitative analysis of these interviews based on Grounded Theory Methodology (Wiesche et al., 2017) will be conducted in this thesis. In addition to the interviews with doctors from the domains of oncology, gastroenterology, and rheumatology, interviews with experts in medical didactics will be analyzed in the thesis.

• Ehsan, U., Passi, S., Liao, Q. V., Chan, L., Lee, I.‑H., Muller, M. & Riedl, M. O. (2024). The Who in XAI: How AI Background Shapes Perceptions of AI Explanations. In Proceedings of the CHI Conference on Human Factors in Computing Systems (S. 1–32). ACM. 10.1145/3613904.3642474

• Wiesche, M., Jurisch, M. C., Yetton, P. W., & Krcmar, H. (2017). Grounded Theory Methodology in Information Systems Research1. MIS quarterly, 41(3), 685-701.

Flow describes a state of complete absorption in a task, which can be experienced across various activities such as learning, music, or gaming. This state can be captured through questionnaires or physiological measurements such as heart rate or brain activity. Because flow is related to increased performance and well-being, it is of high interest to a range of stakeholders in the gaming industry. Such as trainers and players aiming to improve individual and team performances, or game designers seeking to increase engagement. A system capable of detecting flow in e-sports players and supporting them in entering or remaining in it could therefore benefit all of these stakeholders. Yet no such flow-supportive system exists in the context of competitive e-sports.
Thus, the aim of this thesis is to develop an artefact for a physiological flow system in a competitive esports environment. Students identify requirements through a systematic literature review and interviews with representatives from different stakeholder groups, and translate their findings to corresponding design guidelines. Finally, the resulting guidelines are evaluated in a second round of interviews.

Over the past years, remote photoplethysmography (rPPG) algorithms have evolved into powerful tools capable of extracting cardiac features from standard webcams. Among these, heat rate and, in particular, heart rate variability are of importance for researchers interested in classifying mental states (e.g., stress or flow). For new technologies like rPPG to be integrated into research projects, the valid and reliable measurement of these parameters is decisive. While the first commercial providers already offer rPPG solutions with medical-grade certification for remote heart rate estimation, the measurement of HRV remains more challenging, and transparent information on its accuracy is often unavailable. An independent evaluation against an established reference standard is therefore needed.

Thus, the aim of this thesis is to derive cardiac features from a commercial rPPG algorithm (students get access to the license once the thesis is registered) and evaluate them against a synchronized ECG gold standard.

Generative KI-Systeme wie GitHub Copilot oder ChatGPT haben in den letzten Jahren zunehmend Einzug in die professionelle Softwareentwicklung gefunden und sind dabei, Prozesse grundlegend zu verändern. Entwickler:innen nutzen diese Tools heute für eine Vielzahl von Aufgaben – von der explorativen Ideenfindung und kollaborativen Problemlösung über die routinemäßige Code-Generierung bis hin zur weitgehenden Delegation komplexer Teilaufgaben. Erste Studien zeigen, dass der Mehrwert generativer KI nicht allein durch ihre Einführung entsteht, sondern davon abhängt, wie ihre Handlungspotenziale (sogenannte Affordanzen) im konkreten Arbeitskontext tatsächlich genutzt werden.
Die technologische Entwicklung steht jedoch nicht still. Mit dem Aufkommen agentischer KI-Systeme – also KI-Artefakten, die eigenständig Teilaufgaben planen, Werkzeuge aufrufen und Arbeitsschritte ohne kontinuierliche menschliche Steuerung ausführen können – zeichnet sich ein weiterer Paradigmenwechsel ab. Erste Tools dieser Art, etwa KI-basierte Coding Agents oder autonome Test- und Deployment-Pipelines (bspw. Claude Code, Cursor, Codex, Warp), halten bereits Einzug in Entwicklungsumgebungen sowie in anderen Bereichen. Wie Praktiker:innen diese erweiterte Autonomie wahrnehmen, welche neuen Nutzungsmuster entstehen und inwieweit sich Fragen rund um Kontrolle, Vertrauen und Abhängigkeit verschieben, ist bislang kaum empirisch untersucht.
Ziel dieser Abschlussarbeit ist es daher, qualitative, halbstrukturierte Interviews mit Arbeitnehmer:innen, bspw. aus Softwareentwicklungs-nahen Tätigkeiten (engl. Information Systems Development), zu erheben und anschließend zu analysieren, wie genau generative und agentische KI-Tools heute im Arbeitsalltag genutzt werden. Dazu können auf Basis der Erkenntnisse Nutzungstypen, Affordanzen oder Spannungsfelder identifiziert werden, welche die Basis für nachfolgende Forschung für eine verantwortungsvolle Entwicklung und Einführung von GenAI/Agentic AI in Unternehmen darstellt (Ziel wird je nach Abschluss gemeinsam erarbeitet). Die Arbeit umfasst die eigenständige Durchführung und Auswertung von mind. 10 Interviews sowie eine strukturierte qualitative Analyse der erhobenen Daten mittels der Grounded Theory Methodik.

Empfohlene Literatur
Affordance Actualization & GenAI

• Strong, D., Volkoff, O., Johnson, S., Pelletier, L., Tulu, B., Bar-On, I., Trudel, J., & Garber, L. (2014). A Theory of Organization-EHR Affordance Actualization. Journal of the Association for Information Systems, 15(2), 53–85. https://doi.org/10.17705/1jais.00353
• Volkoff, O., & Strong, D. M. (2013). Critical Realism and Affordances: Theorizing IT-Associated Organizational Change Processes1. MIS Quarterly, 37(3), 819–834.
  https://doi.org/10.25300/misq/2013/37.3.07
• Retkowsky, J., Hafermalz, E., & Huysman, M. (2024). Managing a ChatGPT-empowered workforce: Understanding its affordances and side effects. Business Horizons, 67(5), 511–523. https://doi.org/10.1016/j.bushor.2024.04.009
• Leidner, D. E., Gonzalez, E., & Koch, H. (2018). An affordance perspective of enterprise social media and organizational socialization. The Journal of Strategic Information Systems, 27(2), 117–138. https://doi.org/10.1016/j.jsis.2018.03.003

Von GenAI zu Agentic IS

• Baird, A., & 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), 315–341. https://doi.org/10.25300/misq/2021/15882
• Banh, L., & Strobel, G. (2023). Generative Artificial Intelligence. Electronic Markets, 33(1). https://doi.org/10.1007/s12525-023-00680-1 
• Holldack, F., Banh, L., & Strobel, G. (2026). Agentic information systems. Electronic Markets, 36(1). https://doi.org/10.1007/s12525-025-00861-0
• Strunk, J. A., Banh, L., Nissen, A., Strobel, G., Smolnik, S. (2024). To Delegate or Not to Delegate? Factors Influencing Human-Agentic IS Interaction. ICIS 2024 Proceedings.
• Krakowski, S. (2025). Human-AI agency in the age of generative AI. Information and Organization, 35(1), 100560. https://doi.org/10.1016/j.infoandorg.2025.100560
• Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation–Augmentation Paradox. Academy of Management Review, 46(1), 192–210. https://doi.org/10.5465/amr.2018.0072
• 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, 33(2), 678–696. https://doi.org/10.1287/isre.2021.1079
• Raisch, S., & Fomina, K. (2025). Combining Human and Artificial Intelligence: Hybrid Problem-Solving in Organizations. Academy of Management Review, 50(2), 441–464. https://doi.org/10.5465/amr.2021.0421 

Methodik

• Myers, M. D., & Newman, M. (2007). The qualitative interview in IS research: Examining the craft. Information and Organization, 17(1), 2–26.
  https://doi.org/10.1016/j.infoandorg.2006.11.001
• Gioia, D. A., Corley, K. G., & Hamilton, A. L. (2012). Seeking Qualitative Rigor in Inductive Research. Organizational Research Methods, 16(1), 15–31. https://doi.org/10.1177/1094428112452151 

Treatment Effect Prediction (TEP) emphasizes that merely predicting the outcome of a treatment decision instead of both scenarios – under treatment and under control – is a critical flaw in many machine learning (ML) systems. Although true individual treatment effects (ITE) cannot be directly evaluated, as only one decision outcome can be observed for each patient, TEP has sought to develop techniques that allow for such predictions and their evaluation, e.g., through aggregated benefits of medical outcomes when following model recommendations (Pan et al., 2024). While explainable artificial intelligence (XAI) methods are being used to interpret these models, XAI sees little use when it comes to improving them. For an image classification model, e.g., Ribeiro et al. (2016) showed that Local Interpretable Model-Agnostic Explanations (LIME) were suitable to identify spurious correlations in their training data. The explanation showed, that background information, snow in this case, had been the classifier’s focus instead of the animals shown on the images. To the best of our knowledge, no comparable efforts related to TEP have been made so far. Another possible approach to model improvement could be to remove less important features from the training data (Nauta et al., 2023). On top of attempting to improve older and more recent TEP-based ML models, applying XAI may also help us to understand differences between the different models’ internal reasoning.

In this thesis project, the student will develop three TEP-based ML models (Pan et al., 2024) for cancer patients using data from the Surveillance, Epidemiology, and End Results (SEER) database. The models should include the established causal forest (Athey & Wager, 2019) and the more recent SNB model (Pan et al., 2024). First, a scoping review on ML models for TEP and their evaluation will be conducted. After the implementation and training of the models, the student will apply three different XAI methods to generate explanations for each model and analyze these to find potential for adjustments to the training pipeline. These XAI methods should include Shapley Additive Explanations (SHAP) (Lundberg & Lee, 2017) and Diverse Counterfactual Explanations (DiCE) (Mothilal et al., 2020). Interviews with oncologists will be conducted to find flaws of the models through their explanations and improve upon the model by accounting for their input. The interviews will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

• Ribeiro, M. T., Singh, S., & Guestrin, C. (2016). " Why should i trust you?" Explaining the predictions of any classifier. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 1135-1144).

• Pan, H., Wang, J., Shi, W., Xu, Z., & Zhu, E. (2024). Quantified treatment effect at the individual level is more indicative for personalized radical prostatectomy recommendation: implications for prostate cancer treatment using deep learning. Journal of Cancer Research and Clinical Oncology, 150(2), 67.

• Athey, S., & Wager, S. (2019). Estimating treatment effects with causal forests: An application. Observational studies, 5(2), 37-51.

• Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. Advances in neural information processing systems, 30.

• Mothilal, R. K., Sharma, A., & Tan, C. (2020). Explaining machine learning classifiers through diverse counterfactual explanations. In Proceedings of the 2020 conference on fairness, accountability, and transparency (pp. 607-617).

• Nauta, M., & Seifert, C. (2023). The co-12 recipe for evaluating interpretable part-prototype image classifiers. In World conference on explainable artificial intelligence (pp. 397-420). Cham: Springer Nature Switzerland.

In a fast-paced context like stock trading, decisions are made with distinct considerations. Anomaly detection systems based on machine learning (ML) may provide indications for unusual market behavior, but interpreting how to translate a known anomaly into action is a different story. To achieve this, practitioners put high value on real-world context. News headlines, company statements, and political discussions can be quintessential and insightful when trying to interpret a detected anomaly. Sentiment analysis, among other text analysis techniques, shows promise in this regard (Sufi & Alsulami, 2021; Cruz et al., 2023). A trustworthy sentiment analysis component in an anomaly detection system could help stock traders in filtering information, focusing on the most relevant news pieces, and deciding on a reasonable course of action. Trustworthiness in this context means, that human oversight is ensured through increased transparency, and that users understand the system enough to identify when it is mistaken or its outputs should not be acted on. 

In a previous project, a set of design principles for a stock market anomaly detection system has been derived, involving the demand for news integration. Based on this design knowledge, a systematic literature review (SLR) on sentiment analysis, and a preliminary round of interviews for further requirements elicitation the student will develop an anomaly detection system for stock market data that integrates news sentiment analysis in real time. This system will be evaluated with additional experts in think-aloud sessions. The interviews and think-alouds will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

• Sufi, F. K., & Alsulami, M. (2021). Automated multidimensional analysis of global events with entity detection, sentiment analysis and anomaly detection. IEEE Access, 9, 152449-152460.

• Cruz, R., Kinyua, J., & Mutigwe, C. (2023). Analysis of Social Media Impact on Stock Price Movements Using Machine Learning Anomaly Detection. Intelligent Automation & Soft Computing, 36(3).

One promise of artificial intelligence (AI) in medicine is to enable learning from the vast amounts of observational data collected at diverse medical institutions. With all the benefits for knowledge generation and more refined decision making this may bring, it leaves a problem of scientific rigor. To a large degree, medical decision making is bounded by guidelines based on evidence from randomized clinical trials (RCT). However, this trial data might not cover each and every combination of patient characteristics, treatment options, and individual circumstance. Moreover, updating guidelines takes time and the wealth of new literature being published is prone to overwhelming practitioners. A solution to these problems could be found in retrieval augmented generation (RAG) for medical studies. Combing such systems with a clinical decision support system (CDSS) would lead to enhanced explainability, and an improved capacity to assess the quality of AI advice. 

In this thesis project, the student will develop and implement an RAG system for medical treatment decisions in the domain of gastroenterology in three phases. First, a scoping review of RAG in medicine will be conducted alongside a series of interviews with RAG experts and gastroenterologists for requirements elicitation. Then, the system will be implemented for studies on chronic inflammatory bowel disease. Finally, the student conducts an interview study with gastroenterologists to evaluate the system. The interviews will be recorded, transcribed, and analyzed (e.g., via tools like MAXQDA).

The digital health market is increasingly moving from a niche market to a mainstream market, and is expected to grow at an annual growth rate of 5.42% to reach a projected market volume of USD 219.60 billion by 2030 (Statista Market Insights, 2025). Health monitoring is an important sub-segment within the digital health market. 

Recent advances in artificial intelligence have significantly improved the accuracy of remote photoplethysmography (rPPG) algorithms. Using these algorithms, heart rate and other vital signs can be measured using a standard RGB camera, enabling completely contactless health monitoring. While heart rate can already be assessed with relatively high accuracy, the reliable extraction of heart rate variability, an important indicator of mental states, remains an ongoing challenge. 

Therefore, the aim of this thesis is to develop and validate an AI-based rPPG algorithm for heart rate variability extraction. An open access dataset (e.g., DEAP, MAHNOB-HCI) will be used to train and develop the algorithm. In addition, a small data sample will be collected using a reference measurement device (e.g., ECG chest strap) for validation purposes. The resulting data will then be compared and evaluated using selected performance indicators (e. g. mean absolute error, Pearson correlation coefficient).

• Statista Market Insights (2025). Digital Health. Statista. www.statista.com/outlook/hmo/digital-health/worldwide Retrieved 08.03.2025

Over the past years, consumer wearables have evolved from lifestyle gadgets into advanced sensing platforms capable of continuous physiological monitoring. Ecosystems like Garmin and Apple Health provide access to health metrics (e.g., sleep score) and raw data (e.g., heart rate) for research institutions. This is of particular importance for researchers interested in predicting diseases (e.g., hypertension) or classifying mental states (e.g., stress or flow), based on physiological data. Among these, flow presents a state of deep task engagement and optimal experience, and can be assessed with physiological indicators, such as heart rate variability (HRV) or respiration rate.

Therefore, the aim of this thesis is to develop a Garmin application that records physiological data and correlates it with user-reported flow experiences. Students will receive access to the Garmin Developer Portal and will implement a mobile application capable of collecting HRV-related metrics and questionnaire-based self-reports. The application will then be evaluated in a small pilot study to explore relationships between health metrics and flow data.

• Henriksen, A., Haugen Mikalsen, M., Woldaregay, A. Z., Muzny, M., Hartvigsen, G., Hopstock, L. A., & Grimsgaard, S. (2018). Using fitness trackers and smartwatches to measure physical activity in research: analysis of consumer wrist-worn wearables. Journal of medical Internet research, 20(3), e110.

Generative artificial intelligence (GenAI) leverages deep generative models to produce novel content across domains such as text, images, video, and code based on simple user prompts (Banh & Strobel, 2023). Unlike traditional AI systems focused on prediction and pattern recognition, GenAI can understand context, learn from examples, and generate new outputs across multiple domains (Wessel et al., 2025).

The emergence of GenAI represents a disruptive technological shift for digital platforms, fundamentally reshaping how platforms operate and create value. By enabling the autonomous generation of novel outcomes, GenAI has far-reaching implications for platform architecture, value creation, governance, and stakeholder interactions (Wessel et al., 2025). In particular, GenAI platforms reshape value creation through intelligent automation, democratization of participation, hyper-personalization, and collaborative human–AI innovation, thereby expanding platform scope while increasing complexity.

Platform owners establish digital platform ecosystems by orchestrating participants’ activities to enhance the ecosystem’s value proposition (Kindermann et al., 2022). This orchestration is enacted through platform governance mechanisms, defined as the activities through which platform owners shape ecosystem functioning (Chen et al., 2022; Rietveld & Schilling, 2021). In contrast to command-and-control approaches, platform governance relies on connect-and-coordinate mechanisms to influence largely autonomous participants (Tilson et al., 2010).

In the context of GenAI platforms, boundary resources and incentive structures must be reconfigured to accommodate both human developers and agentic complementors. This includes agent-oriented interfaces, protocols for inter-agent communication, and APIs that expose generative capabilities, as well as novel incentive and revenue-sharing mechanisms for autonomous agents (Mayer et al., 2025).

Beyond traditional governance challenges, GenAI platform owners must address risks that are specific to generative AI systems, including hallucinations, jailbreaking, data training and validation issues, and the handling of sensitive information. Addressing these risks necessitates novel forms of governance mechanisms to effectively orchestrate the surrounding ecosystem (Hein et al., 2020; Taeihagh, 2025).

While GenAI platforms democratize value creation and amplify network effects, they also introduce governance challenges that are increasingly salient for regulatory authorities. Hyper-personalization, for instance, increases user engagement and lock-in but raises concerns related to privacy, data use, filter bubbles, and manipulation, thereby requiring governance mechanisms that balance personalization with user protection (Feuerriegel et al., 2024; Wessel et al., 2025). Moreover, GenAI enables forms of collaborative innovation in which autonomous agents participate as ecosystem actors, challenging governance mechanisms originally designed for human complementors (Croitor et al., 2022; He et al., 2025; Wessel et al., 2025).

Research Question: What governance mechanisms are designed and implemented by owners of GenAI platforms to orchestrate value creation within their ecosystems?

Goal: Against this background, this study aims to develop a taxonomy that systematically classifies GenAI platforms based on their governance mechanisms. The taxonomy will be developed following the methodological approach proposed by Nickerson et al. (2013), as extended by Kundisch et al. (2022), and includes at least one conceptual-to-empirical and one empirical-to-conceptual iteration. To this end, a literature corpus is constructed and analyzed through a systematic literature review (Bandara et al., 2015; vom Brocke et al., 2009; Webster & Watson, 2002). In addition, GenAI platforms from multiple organizations (e.g., OpenAI, Alphabet, Microsoft) are examined to inform the taxonomy’s development and to demonstrate its applicability. The taxonomy will also be evaluated through expert interviews.

For the identification of governance mechanisms, an overview of existing mechanisms provided by the chair serves as the initial foundation. Building on this foundation, GenAI-specific governance mechanisms are identified and integrated, with particular emphasis on mechanisms that address the distinct risks associated with generative AI systems.

Please note that, due to its scope, this thesis is intended exclusively for master’s students. The thesis must be written in English.

Interested in Writing Your Master’s Thesis on GenAI Platforms?
Students are invited to submit an extended proposal that details the systematic literature review (search terms, data sources, and initial hits) and proposes initial empirical cases (i.e., GenAI Platforms).

References:

Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., & Beekhuyzen, J. (2015). Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. Communications of the Association for Information Systems, 37. doi.org/10.17705/1CAIS.03708

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 1–17. doi.org/10.1007/s12525-023-00680-1

Chen, L., Yi, J., Li, S., & Tong, T. W. (2022). Platform Governance Design in Platform Ecosystems: Implications for Complementors’ Multihoming Decision. Journal of Management, 48(3), 630–656.

Croitor, E., Werner, D., Adam, M., & Benlian, A. (2022). Opposing effects of input control and clan control for sellers on e-marketplace platforms. Electronic Markets, 32(1), 201–216.

He, Q., Hong, Y., & Raghu, T. S. (2025). Platform Governance with Algorithm-Based Content Moderation: An Empirical Study on Reddit. Information Systems Research, 36(2), 1078–1095.

Hein, A., Schreieck, M., Riasanow, T., Setzke, D. S., Wiesche, M., Böhm, M., & Krcmar, H. (2020). Digital platform ecosystems. Electronic Markets, 30(1), 87–98. doi.org/10.1007/s12525-019-00377-4

Kindermann, B., Salge, T. O., Wentzel, D., Flatten, T. C., & Antons, D. (2022). Dynamic capabilities for orchestrating digital innovation ecosystems: Conceptual integration and research opportunities. Information and Organization, 32(3).

Kundisch, D., Muntermann, J., Oberländer, A. M., Rau, D., Röglinger, M., Schoormann, T., & Szopinski, D. (2022). An Update for Taxonomy Designers. Business & Information Systems Engineering, 64(4), 421–439. doi.org/10.1007/s12599-021-00723-x

Mayer, A. S., Kostis, A., Strich, F., & Holmström, J. (2025). Shifting Dynamics: How Generative AI as a Boundary Resource Reshapes Digital Platform Governance. Journal of Management Information Systems, 42(2), 400–430.

Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336–359. doi.org/10.1057/ejis.2012.26

Rietveld, J., & Schilling, M. A. (2021). Platform Competition: A Systematic and Interdisciplinary Review of the Literature. Journal of Management, 47(6), 1528–1563.

Taeihagh, A. (2025). Governance of Generative AI. Policy and Society, 44(1), 1–22. doi.org/10.1093/polsoc/puaf001

Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Research Commentary—Digital Infrastructures: The Missing IS Research Agenda. Information Systems Research, 21, 748–759.

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. In 17th European Conference on Information Systems (ECIS 2009), Verona, Italy. aisel.aisnet.org/ecis2009/161

Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii. www.jstor.org/stable/4132319

Wessel, M., Adam, M., Benlian, A., Majchrzak, A., & Thies, F. (2025). Generative AI and its Transformative Value for Digital Platforms. Journal of Management Information Systems, 42(2), 346–369.

Generative artificial intelligence (GenAI) leverages deep generative models to produce novel content across domains such as text, images, video, and code based on simple user prompts (Banh & Strobel, 2023). Unlike traditional AI systems focused on prediction and pattern recognition, GenAI can understand context, learn from examples, and generate new outputs across multiple domains (Wessel et al., 2025).

The emergence of GenAI represents a disruptive technological shift for digital platforms, fundamentally reshaping how platforms operate and create value. By enabling the autonomous generation of novel outcomes, GenAI has far-reaching implications for platform architecture, value creation, governance, and stakeholder interactions (Wessel et al., 2025). In particular, GenAI platforms reshape value creation through intelligent automation, democratization of participation, hyper-personalization, and collaborative human–AI innovation, thereby expanding platform scope while increasing complexity.

Platform owners establish digital platform ecosystems by orchestrating participants’ activities to enhance the ecosystem’s value proposition (Kindermann et al., 2022). This orchestration is enacted through platform governance mechanisms, defined as the activities through which platform owners shape ecosystem functioning (Chen et al., 2022; Rietveld & Schilling, 2021). In contrast to command-and-control approaches, platform governance relies on connect-and-coordinate mechanisms to influence largely autonomous participants (Tilson et al., 2010).

In the context of GenAI platforms, boundary resources and incentive structures must be reconfigured to accommodate both human developers and agentic complementors. This includes agent-oriented interfaces, protocols for inter-agent communication, and APIs that expose generative capabilities, as well as novel incentive and revenue-sharing mechanisms for autonomous agents (Mayer et al., 2025).

Beyond traditional governance challenges, GenAI platform owners must address risks that are specific to generative AI systems, including hallucinations, jailbreaking, data training and validation issues, and the handling of sensitive information. Addressing these risks necessitates novel forms of governance mechanisms to effectively orchestrate the surrounding ecosystem (Hein et al., 2020; Taeihagh, 2025).

While GenAI platforms democratize value creation and amplify network effects, they also introduce governance challenges that are increasingly salient for regulatory authorities. Hyper-personalization, for instance, increases user engagement and lock-in but raises concerns related to privacy, data use, filter bubbles, and manipulation, thereby requiring governance mechanisms that balance personalization with user protection (Feuerriegel et al., 2024; Wessel et al., 2025). Moreover, GenAI enables forms of collaborative innovation in which autonomous agents participate as ecosystem actors, challenging governance mechanisms originally designed for human complementors (Croitor et al., 2022; He et al., 2025; Wessel et al., 2025).

Research Question: What governance mechanisms are designed and implemented by owners of GenAI platforms to orchestrate value creation within their ecosystems?

Goal: Against this background, this study aims to develop a taxonomy that systematically classifies GenAI platforms based on their governance mechanisms. The taxonomy will be developed following the methodological approach proposed by Nickerson et al. (2013), as extended by Kundisch et al. (2022), and includes at least one conceptual-to-empirical and one empirical-to-conceptual iteration. To this end, a literature corpus is constructed and analyzed through a systematic literature review (Bandara et al., 2015; vom Brocke et al., 2009; Webster & Watson, 2002). In addition, GenAI platforms from multiple organizations (e.g., OpenAI, Alphabet, Microsoft) are examined to inform the taxonomy’s development and to demonstrate its applicability. The taxonomy will also be evaluated through expert interviews.

For the identification of governance mechanisms, an overview of existing mechanisms provided by the chair serves as the initial foundation. Building on this foundation, GenAI-specific governance mechanisms are identified and integrated, with particular emphasis on mechanisms that address the distinct risks associated with generative AI systems.

Please note that, due to its scope, this thesis is intended exclusively for master’s students. The thesis must be written in English.

Interested in Writing Your Master’s Thesis on GenAI Platforms?
Students are invited to submit an extended proposal that details the systematic literature review (search terms, data sources, and initial hits) and proposes initial empirical cases (i.e., GenAI Platforms).

References:

Bandara, W., Furtmueller, E., Gorbacheva, E., Miskon, S., & Beekhuyzen, J. (2015). Achieving Rigor in Literature Reviews: Insights from Qualitative Data Analysis and Tool-Support. Communications of the Association for Information Systems, 37. doi.org/10.17705/1CAIS.03708

Banh, L., & Strobel, G. (2023). Generative artificial intelligence. Electronic Markets, 33(1), 1–17. doi.org/10.1007/s12525-023-00680-1

Chen, L., Yi, J., Li, S., & Tong, T. W. (2022). Platform Governance Design in Platform Ecosystems: Implications for Complementors’ Multihoming Decision. Journal of Management, 48(3), 630–656.

Croitor, E., Werner, D., Adam, M., & Benlian, A. (2022). Opposing effects of input control and clan control for sellers on e-marketplace platforms. Electronic Markets, 32(1), 201–216.

He, Q., Hong, Y., & Raghu, T. S. (2025). Platform Governance with Algorithm-Based Content Moderation: An Empirical Study on Reddit. Information Systems Research, 36(2), 1078–1095.

Hein, A., Schreieck, M., Riasanow, T., Setzke, D. S., Wiesche, M., Böhm, M., & Krcmar, H. (2020). Digital platform ecosystems. Electronic Markets, 30(1), 87–98. doi.org/10.1007/s12525-019-00377-4

Kindermann, B., Salge, T. O., Wentzel, D., Flatten, T. C., & Antons, D. (2022). Dynamic capabilities for orchestrating digital innovation ecosystems: Conceptual integration and research opportunities. Information and Organization, 32(3).

Kundisch, D., Muntermann, J., Oberländer, A. M., Rau, D., Röglinger, M., Schoormann, T., & Szopinski, D. (2022). An Update for Taxonomy Designers. Business & Information Systems Engineering, 64(4), 421–439. doi.org/10.1007/s12599-021-00723-x

Mayer, A. S., Kostis, A., Strich, F., & Holmström, J. (2025). Shifting Dynamics: How Generative AI as a Boundary Resource Reshapes Digital Platform Governance. Journal of Management Information Systems, 42(2), 400–430.

Nickerson, R. C., Varshney, U., & Muntermann, J. (2013). A method for taxonomy development and its application in information systems. European Journal of Information Systems, 22(3), 336–359. doi.org/10.1057/ejis.2012.26

Rietveld, J., & Schilling, M. A. (2021). Platform Competition: A Systematic and Interdisciplinary Review of the Literature. Journal of Management, 47(6), 1528–1563.

Taeihagh, A. (2025). Governance of Generative AI. Policy and Society, 44(1), 1–22. doi.org/10.1093/polsoc/puaf001

Tilson, D., Lyytinen, K., & Sørensen, C. (2010). Research Commentary—Digital Infrastructures: The Missing IS Research Agenda. Information Systems Research, 21, 748–759.

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. In 17th European Conference on Information Systems (ECIS 2009), Verona, Italy. aisel.aisnet.org/ecis2009/161

Webster, J., & Watson, R. T. (2002). Analyzing the Past to Prepare for the Future: Writing a Literature Review. MIS Quarterly, 26(2), xiii–xxiii. www.jstor.org/stable/4132319

Wessel, M., Adam, M., Benlian, A., Majchrzak, A., & Thies, F. (2025). Generative AI and its Transformative Value for Digital Platforms. Journal of Management Information Systems, 42(2), 346–369.

Background and Motivation

Smart City studies and rankings commonly rely on composite indices that aggregate multiple indicators into overall scores. Central to these indices are scoring rules and weighting schemes, which determine how individual indicators contribute to final results. While such approaches enable comparability and simplification, they are inherently based on a variety of methodological and normative assumptions.

These assumptions - such as the relative importance of dimensions, the linearity of aggregation, or the compensability between indicators - are often implicit and rarely made transparent. As a result, Smart City rankings may produce results that are difficult to interpret, sensitive to design choices, and perceived as unfair or arbitrary by participating cities.

In addition to methodological concerns, there is increasing criticism from municipalities regarding how rankings reflect their efforts and contextual conditions. Cities may question whether scoring and weighting logics adequately capture their strategic priorities, structural constraints, or development paths. Despite this, the empirical perspective of cities on scoring fairness and transparency remains largely unexplored.

Against this background, a systematic analysis of scoring and weighting logics - combined with an empirical assessment of how these logics are perceived by cities - represents an important contribution to the methodological robustness and legitimacy of Smart City assessments.

Research Objectives

The objective of this master thesis is to analyze how different scoring and weighting logics influence the outcomes of Smart City studies and how these logics are perceived by municipalities.

The thesis aims to:

• Analyze and compare scoring and weighting models used in existing Smart City studies and rankings;

• Identify and explicate underlying methodological and normative assumptions embedded in these models;

• Develop alternative weighting scenarios and aggregation logics;

• Conduct sensitivity analyses to assess how changes in weighting and scoring affect ranking outcomes;

• Empirically explore how municipalities perceive scoring fairness, transparency, and validity, and identify common points of criticism;

• Derive implications for the design of transparent, robust, and fair scoring systems in Smart City studies

Methodology

The thesis will follow a mixed-methods research approach, combining quantitative and qualitative elements:

• A structured literature review on composite indicators, scoring models, and weighting techniques in Smart City and related fields;

• Quantitative analysis of existing scoring models, including the development of alternative weighting scenarios;

• Sensitivity and scenario analyses to assess the robustness of ranking outcomes;

• Empirical data collection through qualitative interviews or surveys with municipal representatives to capture perceptions of fairness, transparency, and critique of scoring approaches;

• Integration of quantitative and qualitative findings to inform design recommendations

The methodological design will be tailored to the scope and requirements of a master thesis

Expected Contribution

This master thesis will contribute to Smart City research by providing a systematic and empirically informed analysis of scoring and weighting logics. By making underlying assumptions explicit and incorporating the perspectives of municipalities, the thesis will support the development of more transparent, robust, and legitimate assessment and ranking approaches in Smart City studies.

Interested students are invited to send an e-mail to: tim.bree (at) uni-due.de

Background and Motivation

Smart Cities are commonly described as socio-technical systems in which technological infrastructures, organizational arrangements, governance structures, and societal goals interact. To analyze, compare, and assess Smart City initiatives, numerous reference models and frameworks have been proposed in both academic research and applied practice. These models differ substantially in their scope, structure, and underlying assumptions.

While some Smart City models emphasize technological layers and digital infrastructures, others focus on governance, sustainability, or societal value creation. As a result, existing models often lack conceptual consistency, clear separation of concerns, or explicit design principles. Moreover, many models conflate the architecture of a reference model (i.e., its structural logic and dimensions) with concrete indicators or evaluation criteria.

There is therefore a need to systematically examine how a Smart City reference model should be architected in order to integrate technological, organizational, and societal dimensions in a coherent and extensible way. Rather than proposing yet another complete model, a focus on reference model architecture allows for a more fundamental and transferable contribution.

Research Objectives

The objective of this master thesis is to conceptualize the architecture of a holistic Smart City reference model. The focus lies on defining how such a model should be structured, rather than on populating it with concrete indicators or metrics.

The thesis aims to:

• Review and compare existing Smart City reference models and frameworks from academic literature and practice;

• Identify core dimensions, layers, and viewpoints commonly used to describe Smart Cities;

• Analyze how technological, organizational, governance-related, and societal aspects are represented and related in existing models;

• Derive design principles for a Smart City reference model architecture (e.g., modularity, extensibility, separation of concerns);

• Propose a conceptual architecture that specifies how dimensions, layers, and relationships in a Smart City reference model should be structured;

• Discuss how such an architecture can serve as a foundation for measurement, comparison, and evaluation approaches

Methodology

The thesis will follow a conceptual and theory-driven research approach, including:

• A structured literature review of Smart City models, reference architectures, and related frameworks;

• Conceptual analysis of model structures, dimensions, and underlying assumptions;

• Synthesis of findings into a coherent reference model architecture;

• Conceptual validation through comparison with existing models and use cases.

The methodological scope and depth will be aligned with the requirements of a master thesis.

Expected Contribution

This master thesis will contribute to Smart City research by providing a theoretically grounded and systematically developed reference model architecture. By clarifying how Smart City models should be structured at an architectural level, the thesis will support future work on indicator development, benchmarking, governance analysis, and comparative Smart City studies.

Interested students are invited to send an e-mail to: tim.bree (at) uni-due.de

Background and Motivation

Benchmarking and ranking studies are widely used to assess and compare Smart City development across municipalities. They aim to provide transparency, identify best practices, and support strategic decision-making. However, existing Smart City benchmarking approaches often rely on standardized indicator sets and uniform scoring mechanisms that insufficiently account for the heterogeneous conditions under which cities operate.

Cities differ substantially with regard to size, administrative capacities, financial resources, socio-economic structures, and geographic or institutional contexts. Ignoring these contextual factors can lead to distorted comparisons, misleading rankings, and dysfunctional incentives for municipalities. As a result, Smart City rankings are frequently criticized for lacking fairness, transparency, and analytical validity.

In response to these limitations, there is a growing need for benchmarking approaches that explicitly incorporate contextual factors and enable meaningful comparison between cities with similar structural conditions. Developing such context-sensitive benchmarking frameworks represents a central methodological challenge in Smart City research and comparative urban studies.

Research Objectives

The objective of this master thesis is to develop a context-sensitive benchmarking framework for Smart Cities that enables fair and analytically sound comparison across heterogeneous municipal contexts.

The thesis aims to:

• Analyze and critically review existing Smart City benchmarking and ranking approaches in academic research and applied studies;

• Identify key methodological weaknesses, with a particular focus on the treatment of contextual factors;

• Conceptualize methods for incorporating context into benchmarking, such as comparison groups, normalization techniques, or multi-dimensional assessment models;

• Develop a coherent benchmarking framework that balances comparability, transparency, and contextual sensitivity;

• Discuss the implications of the proposed framework for Smart City assessment and comparative urban analysis.

Methodology

The thesis will follow a conceptually and methodologically driven research approach, potentially combining:

• A structured literature review on benchmarking methodologies, ranking systems, and Smart City assessment frameworks;

• Comparative analysis of existing Smart City indices and ranking models;

• Conceptual modeling of alternative benchmarking logics (e.g., clustering, peer-group comparison, normalization);

• Optional exploratory data analysis to illustrate or assess selected design choices, depending on data availability.

The exact methodological focus will be defined in coordination with the supervisor and aligned with the scope of a master thesis

Expected Contribution

This master thesis will contribute to the methodological advancement of Smart City benchmarking by proposing a context-sensitive assessment framework that addresses key limitations of existing approaches. The results will provide transferable insights for the design of fair, transparent, and learning-oriented benchmarking systems in Smart City research and practice.

Interested students are invited to send an e-mail to: tim.bree@uni-due.de

Background and Motivation

Smart City initiatives are often associated with digital technologies and data-driven solutions. However, research and practice increasingly show that the success of Smart City development depends not only on technological capabilities, but also on governance arrangements, organizational structures, and coordination mechanisms between involved actors.

In the German context, municipalities differ significantly in how they organize Smart City activities. Some cities establish dedicated Smart City units or cross-departmental coordination bodies, while others integrate Smart City topics into existing administrative structures. In addition, governance arrangements are shaped by interactions between municipal administration, political leadership, public enterprises, private actors, and research institutions.

Medium-sized cities in particular face specific challenges, as they must balance growing strategic ambitions with limited organizational and financial resources. Public funding programs further influence governance structures by shaping priorities, responsibilities, and collaboration patterns. Despite their importance, these organizational and governance dimensions remain underrepresented in many Smart City assessments and studies.

Research Objectives

The objective of this bachelor thesis is to analyze and compare governance and organizational structures in Smart City development across selected medium-sized German cities, to be defined in coordination with the supervisor.

The thesis aims to:

• Describe and compare Smart City governance models in selected German cities with populations between approximately 100,000 and 600,000 inhabitants;

• Analyze the roles and interactions of key actors, including municipal administration, political leadership, public enterprises, private partners, and research institutions;

• Identify coordination challenges and collaboration mechanisms across organizational boundaries;

• Identify archetypical governance structures based on recurring patterns across cases;

• Analyze the advantages and disadvantages of different governance forms;

• Compare governance arrangements in cities with and without external Smart City funding.

Methodology

The thesis will follow a structured qualitative research approach, including:

• A structured literature review on Smart City governance and municipal organization;

• Case study analysis based on publicly available Smart City strategies, policy documents, organizational charts, and project descriptions;

• Structured comparison of cases along predefined analytical dimensions (e.g., organizational setup, actor roles, coordination mechanisms, funding context);

• Identification of governance archetypes and cross-case patterns

Expected Contribution

This bachelor thesis will provide a structured overview of governance and organizational approaches to Smart City development in medium-sized German cities. By identifying archetypical governance structures and their respective strengths and weaknesses, the thesis will contribute to a better understanding of non-technological success factors in Smart City initiatives and support more informed comparison and assessment of municipal Smart City approaches.

Interested students are invited to send an e-mail to: tim.bree@uni-due.de

Background and Motivation

Smart City studies and benchmarking initiatives increasingly rely on data provided directly by municipalities, often through surveys, questionnaires, and structured self-assessments. These instruments are intended to capture information on technological, organizational, and societal aspects of Smart City development. However, the willingness of cities to participate in such studies varies considerably, and response rates often remain limited.

One key reason lies in the perceived effort associated with data collection. Municipal administrations frequently face constraints in terms of time, personnel, data availability, and internal coordination. At the same time, the perceived benefits of participation - such as learning effects, comparability, or strategic guidance - are not always clear or immediate. As a result, even well-designed Smart City studies may struggle to achieve sufficient participation and data quality.

Understanding how municipalities perceive the effort and value of Smart City data collection is therefore crucial for designing surveys and studies that are both scientifically robust and practically feasible. In particular, cooperative and learning-oriented study designs require a careful balance between analytical ambition and administrative burden.

Research Objectives

The objective of this bachelor thesis is to analyze how municipalities perceive the effort and benefits associated with data collection in Smart City studies, and how these perceptions influence their willingness to participate.

The thesis aims to:

• Review existing municipal survey formats and data collection approaches used in Smart City studies;

• Identify organizational, technical, and informational barriers that hinder municipal data provision;

• Analyze factors that influence acceptance, motivation, and participation willingness among municipalities;

• Assess how survey design choices (e.g., length, complexity, required data types) affect perceived effort;

• Derive recommendations for designing Smart City surveys that balance data quality with feasibility.

Methodology

The thesis will follow a structured qualitative research approach, including:

• A structured literature review on municipal data collection, survey acceptance, and administrative burden;

• Document analysis of existing Smart City questionnaires and study materials;

• Qualitative interviews with municipal representatives involved in Smart City initiatives or data reporting, where feasible;

• Synthesis of findings into design principles and practical recommendations for survey-based data collection.

The methodological scope will be aligned with the requirements of a bachelor thesis.

Expected Contribution

This bachelor thesis will contribute to a better understanding of the practical challenges associated with municipal data collection in Smart City studies. By analyzing acceptance and perceived effort from a municipal perspective, the thesis will provide empirically grounded recommendations for improving survey design, increasing participation rates, and enhancing the overall quality of Smart City data collection efforts.

Interested students are invited to send an e-mail to: tim.bree@uni-due.de

Background and Motivation

Smart City initiatives aim to address a wide range of urban challenges, such as sustainable mobility, climate adaptation, flood protection, or energy transition. To monitor and evaluate progress in these areas, municipalities increasingly rely on indicator-based assessments and key performance indicators (KPIs). However, while numerous KPIs are discussed in academic literature and policy frameworks, many of these indicators are not systematically collected or readily available in municipal practice.

In particular, cities often face limitations in terms of data availability, data quality, and organizational capacity, which restrict the feasibility of comprehensive indicator-based assessments. As a result, Smart City studies frequently rely on proxy indicators or technology-driven metrics, while domain-specific and context-sensitive indicators remain underdeveloped or underutilized. 

Domain-specific indicator systems therefore need to balance analytical ambition with practical feasibility. They should reflect the specific characteristics of individual Smart City domains while taking into account which KPIs are realistically measurable and accessible for municipalities. Such systems can support more meaningful assessments and improve the interpretability of comparative Smart City analyses.

Research Objectives

The objective of this bachelor thesis is to develop and analyze an indicator system for one selected Smart City domain. The specific domain (e.g., mobility, climate adaptation, flood protection, energy) will be defined in coordination with the supervisor.

The thesis aims to:

• Identify and review relevant indicators and indicator frameworks for the selected Smart City domain in academic literature and applied studies;

• Systematically structure domain-specific indicators within a coherent indicator system;

• Analyze qualitative relationships and interdependencies between indicators within the selected domain;

• Assess the practical applicability and feasibility of the indicators from a municipal perspective, including data availability and collection effort;

• Derive recommendations for the design of domain-specific indicator systems for Smart City assessments.

Methodology

The thesis will follow a structured qualitative research approach, including:

• A systematic literature review focusing on indicators and KPIs for the selected Smart City domain;

• Conceptual analysis and structuring of indicators into a coherent indicator system;

• Qualitative assessment of indicator relevance, data availability, and feasibility, potentially supported by expert interviews with municipal practitioners;

• Synthesis of findings into a domain-specific indicator system proposal.

The methodological scope will be aligned with the requirements of a bachelor thesis.

Expected Contribution

This bachelor thesis will provide a structured and practice-oriented analysis of indicators for a specific Smart City domain. The results will contribute to a better understanding of how domain-specific indicator systems can be designed to balance analytical rigor with practical feasibility in municipal contexts.

Interested students are invited to send an e-mail to: tim.bree (at) uni-due.de

Flow, the state of optimal experience and complete absorption in an activity, is of growing interest in information systems research. Recent studies have shown that flow states can be classified using machine learning models trained on physiological data, such as heart rate and heart rate variability (HRV). For instance, Rissler et al. (2020) trained a flow classifier using a random forest model and achieved an accuracy of 70%. Traditional machine learning approaches often rely on hand-crafted features (HCFs), such as standard HRV metrics like SDNN or RMSSD. However, these features require expert knowledge and are labor-intensive to compute. Feature learning methods, such as deep neural networks, present a promising approach to overcome these limitations due to their capability to automatically extract relevant features. Therefore, feature learning approaches may outperform HCFs, in particular when dealing with large-scale, noisy, or unstructured data. 

The aim of this thesis is to investigate the differences between HCFs and feature learning approaches for classifying flow states from physiological signals. Students working on this project will have access to a publicly available flow dataset.

Rissler, R., Nadj, M., Li, M. X., Loewe, N., Knierim, M. T., & Maedche, A. (2020). 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.

Information systems can be considered linguistic artifacts (Stamper 1987, Ortner 1993, Frank 2021). They are constituted through software languages and can only be used if they represent concepts prospective users are familiar with. As a result, the integration of information systems can be considered a semantic issue, too (Frank 2008): Different information systems may utilize various domain concepts in different formats, but still must be enabled to effectively and efficiently communicate with each other.

Integration continues to be an issue for many corporations across various domains and industries, caused, among other reasons, by an increasing number of heterogeneous vendors each of which uses its own domain language. Resulting systems communication issues are addressed by various means, e.g, by boling down all concepts to a “global schema” which the concepts used in another information system must be mapped to. Existing solutions are, however, faced with various insufficiencies and may lead to conceptual redundancy, error-prone semantic reconstruction efforts, and miscommunication between systems. These insufficiencies threathen the integrity of information systems and, with that, their effective and efficient use in organizations.

Existing technical landscapes are often based on so-called two-level software languages, such as Java, C#, Python, UML, or the ERM language (cf. Kühne 2007, Atkinson and Kühne 2008). Two-level languages provide developers with control over two levels of abstraction: a type level and an instance level. In object-oriented development, this corresponds to classes and objects. The dominant two-level development style prohibits the use of further abstraction levels to facilitate the communication between information systems: all communication is restricted to a type and an instance level.

This restriction is alleviated in multi-level software languages, which, among others, allow for the definition of an unbounded number of classification levels. Multi-level software languages have been motivated by limitations of two-level languages in various application scenarios, among the issues of integration with two-level languages outlined above (Frank 2022). However, apart from theoretical discussions about potential prospects of using multi-level languages for the integration of information systems, no detailed conception of how to apply multi-level languages for integration has yet been elaborated. As part of this thesis, you are asked to investigate in detail when and how multi-level languages may aid integration issues, what obstacles arise, and how they might be counteracted. 

The thesis is part of an ongoing research project with Oracle. Proficiency in English is a prequisite for this. 

Application Deadline: Application process will be closed as soon as a suited candidate is found. You can submit your application by sending a short statement of motivation, your current transcript of records, and your CV to pierre.maier (at) uni-due.de AND Sekretariat.IIS (at) icb.uni-due.de. 

• Atkinson C, Kühne T (2008) Reducing Accidental Complexity in Domain Models. Software and Systems Modeling 7:345–359
• Frank U (2008) Integration: Reflections on a Pivotal Concept for Designing and Evaluating Information Systems. Information Systems and e-Business Technologies: 2nd International United Information Systems Conference, UNISCON 2008, Klagenfurt, Austria, April 22-25, 2008, Proceedings, pp 111–122
• Frank U (2021) Language, Change, and Possible Worlds: Philosophical Considerations of the Digital Transformation. In: Siegetsleitner A, Oberprantacher A, Frick M-L, Metschl U (eds). Crisis and Critique: Philosophical Analysis of Current Events, Proceedings of the 42nd International Wittgenstein Symposium. De Gruyter: Berlin, Boston, MA, pp 117–138
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Frank U, Töpel D (2020) Contingent Level Classes: Motivation, Conceptualization, Modeling Guidelines, and Implications for Model Management. MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
• Kühne T, Schreiber D (2007) Can Programming be Liberated from the Two-Level Style? Multi-Level Programming with DeepJava. OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-oriented Programming Systems and Applications Companion, pp 229–244
• Ortner E (1993) Software-Engineering als Sprachkritik: Die Sprachkritische Methode des Fachlichen Software-Entwurfs. Universitätsverlag Konstanz: Konstanz
• Stamper R (1987) Semantics. In: Boland RJ, Hirschheim R (eds). Critical Issues in Information Systems Research. John Wiley & Sons: Chichester, pp 43–78

Information systems can be considered linguistic artifacts (Stamper 1987, Ortner 1993, Frank 2021). They are constituted through software languages and can only be used if they represent concepts prospective users are familiar with. As a result, the integration of information systems can be considered a semantic issue, too (Frank 2008): Different information systems may utilize various domain concepts in different formats, but still must be enabled to effectively and efficiently communicate with each other.

Integration continues to be an issue for many corporations across various domains and industries, caused, among other reasons, by an increasing number of heterogeneous vendors each of which uses its own domain language. Resulting systems communication issues are addressed by various means, e.g, by boling down all concepts to a “global schema” which the concepts used in another information system must be mapped to. Existing solutions are, however, faced with various insufficiencies and may lead to conceptual redundancy, error-prone semantic reconstruction efforts, and miscommunication between systems. These insufficiencies threathen the integrity of information systems and, with that, their effective and efficient use in organizations.

Existing technical landscapes are often based on so-called two-level software languages, such as Java, C#, Python, UML, or the ERM language (cf. Kühne 2007, Atkinson and Kühne 2008). Two-level languages provide developers with control over two levels of abstraction: a type level and an instance level. In object-oriented development, this corresponds to classes and objects. The dominant two-level development style prohibits the use of further abstraction levels to facilitate the communication between information systems: all communication is restricted to a type and an instance level.

This restriction is alleviated in multi-level software languages, which, among others, allow for the definition of an unbounded number of classification levels. Multi-level software languages have been motivated by limitations of two-level languages in various application scenarios, among the issues of integration with two-level languages outlined above (Frank 2022). However, apart from theoretical discussions about potential prospects of using multi-level languages for the integration of information systems, no detailed conception of how to apply multi-level languages for integration has yet been elaborated. As part of this thesis, you are asked to investigate in detail when and how multi-level languages may aid integration issues, what obstacles arise, and how they might be counteracted. 

The thesis is part of an ongoing research project with Oracle. As part of thesis, students may be granted an internship at Oracle, providing access to Oracle’s huge data sources which may be used to conduct experiments. Proficiency in English is a prequisite for this. 

Application Deadline: Application process will be closed as soon as a suited candidate is found. You can submit your application by sending a short statement of motivation, your current transcript of records, and your CV to pierre.maier (at) uni-due.de AND Sekretariat.IIS (at) icb.uni-due.de. 

• Atkinson C, Kühne T (2008) Reducing Accidental Complexity in Domain Models. Software and Systems Modeling 7:345–359
• Frank U (2008) Integration: Reflections on a Pivotal Concept for Designing and Evaluating Information Systems. Information Systems and e-Business Technologies: 2nd International United Information Systems Conference, UNISCON 2008, Klagenfurt, Austria, April 22-25, 2008, Proceedings, pp 111–122
• Frank U (2021) Language, Change, and Possible Worlds: Philosophical Considerations of the Digital Transformation. In: Siegetsleitner A, Oberprantacher A, Frick M-L, Metschl U (eds). Crisis and Critique: Philosophical Analysis of Current Events, Proceedings of the 42nd International Wittgenstein Symposium. De Gruyter: Berlin, Boston, MA, pp 117–138
• Frank U (2022) Multi-Level Modeling: Cornerstones of a Rationale. Software and Systems Modeling 21:451–480
• Frank U, Töpel D (2020) Contingent Level Classes: Motivation, Conceptualization, Modeling Guidelines, and Implications for Model Management. MODELS '20: Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems: Companion Proceedings
• Kühne T, Schreiber D (2007) Can Programming be Liberated from the Two-Level Style? Multi-Level Programming with DeepJava. OOPSLA '07: Companion to the 22nd ACM SIGPLAN Conference on Object-oriented Programming Systems and Applications Companion, pp 229–244
• Ortner E (1993) Software-Engineering als Sprachkritik: Die Sprachkritische Methode des Fachlichen Software-Entwurfs. Universitätsverlag Konstanz: Konstanz
• Stamper R (1987) Semantics. In: Boland RJ, Hirschheim R (eds). Critical Issues in Information Systems Research. John Wiley & Sons: Chichester, pp 43–78

For more information: sitm.ris.uni-due.de/news/news/master-thesis-on-mapping-and-understanding-stakeholders-in-public-blockchains-24948/

For more information: sitm.ris.uni-due.de/news/news/master-thesis-on-governing-the-decentralized-a-comparative-case-study-of-the-governance-in-tezos-arbitrum-optimism-starknet-polkadot-ethereum-and-bitcoin-24949/

Artificial neural networks (ANNs) denote a popular class of models used within machine learning. An ANN typically consists of multiple layers of simple processing units, so-called artificial neurons. Most current ANNs involve multiple layers of these processing units, hence the term deep learning is sometimes applied to describe them. Historically, they emerged from a neurophysiological inspiration to express the processing of mammal neurons in mathematical terms (cf. McCulloch and Pitts 1943). There exists a plethora of different approaches to the design of ANNs, some variations include the number of artificial neurons in a layer, the activation function applied, or the connection of artificial neurons between layers. From these variations have emerged several classes of ANN architectures, such as Multi-Layered Perceptrons (MLPs), Generative Adversial Networks (GANs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), or more recently Transformers. It is conspicuous that many papers, which discuss a particular ANN architecture, represent them in some diagrammatic form. This diagrammatic representation, however, does not follow any unified structure. This results in two challenges: First, ANNs are not visually comparable through an analysis of their diagrammatic representations. Second, the depicted diagrams of ANNs might lack relevant information, overseen by the original researchers. In short: It appears that the depiction of ANNs lack a conceptual modeling language.

The present thesis should adress this gap. Therefore, it is relevant to expound on the foundations and variations of ANNs as well as to explore the fundamentals of conceptual modeling languages. Based on an analysis of the design, evaluation, and application of ANNs, requirements for a corresponding modeling method should be derived. Thereupon, these insights should be used to specify a conceptual modeling method for ANNs.

Literature:

• Aggarwal CC (2018) Neural Networks and Deep Learning: A Textbook. Springer International Publishing: Cham
• Du K-L, Swamy MNS (2014) Neural Networks and Statistical Learning. Springer-Verlag: London
• Frank U (2013) Domain-Specific Modeling Languages – Requirements Analysis and Design Guidelines. In: Reinhartz-Berger I, Sturm A, Clark T, Wand Y, Cohen S, Bettin J (eds.) Domain Engineering: Product Lines, Conceptual Models, and Languages. Springer: Cham, pp. 133-157
• Kelleher JD (2019) Deep Learning. The MIT Press: Cambridge, MA, London
• McCulloch WS, Pitts W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5:115-133

If language shapes our reality, changing our language might lead to a different, potentially preferable reality. This thought is echoed throughout a variety of philosophical schools and can, in different variations, with different assumptions, and with different implications, be found in the writings of Ludwig Wittgenstein, Richard Rorty, Friedrich Nietzsche, Immanuel Kant, or Humberto Maturana. Recently, the discussion has received more widespread attention. Motivated in part from feminist philosophy of the 1990s, philosophers have combined their research efforts towards the improvement of language under the moniker of conceptual engineering and conceptual ethics. The amelioration of concepts and language is faced with several theoretical and practical challenges. What makes a concept “better” than another? How could a new concept be adopted by respective language users?

Information systems development is essentially concerned with language development (clarification and sources per request). Broadly, this poses the question if information systems can support conceptual engineering and, if so, in what regards. Machine learning (ML) might be a fruitful first step to guide this analysis. Contemporary ML approaches are inductive (cf. Rescher 1980): they generate potentially novel generalizations based on a set of observations. Researchers like Rees (2022) therefore suggest that they might guide the development of novel concepts.

This master’s thesis should explore the capabilities of ML to support conceptual engineering. You should identify potential tasks of conceptual engineering and what requirements they face. Then you should investigate how different ML approaches (we can disucss which in our first meetings) can serve to address these requirements.

Literature:

• Burgess A, Cappelen H, Plunkett D (eds) (2020) Conceptual Engineering and Conceptual Ethics. Oxford University Press: Oxford
• Butlin P (2021) Sharing Our Concepts with Machines. Erkenntnis
• Cappelen H, Dever J (2019) Bad Language. Oxford University Press: Oxford
• Haslanger S (2012) Resisting Reality: Social Construction and Social Critique. Oxford University Press: Oxford
• Medin DL, Smith EE (1984) Concepts and Concept Formation. Annual Review of Psychology 35(35):113–138
• Montemayor C (2021) Language and Intelligence. Minds and Machines 31:471–486
• Ontañón S, Dellunde P, Godo L, Plaza E (2012) A Defeasible Reasoning Model of Inductive Concept Learning from Examples and Communication. Artificial Intelligence 193:129–148
• Rees T (2022) Non-Human Words: On GPT-3 as a Philosophical Library. Daedalus 151(2):168–182
• Rescher N (1980) Induction: An Essay on the Justification of Inductive Reasoning. Basil Blackwell: Oxford

If language shapes our reality, changing our language might lead to a different, potentially preferable reality. This thought is echoed throughout a variety of philosophical schools and can, in different variations, with different assumptions, and with different implications, be found in the writings of Ludwig Wittgenstein, Richard Rorty, Friedrich Nietzsche, Immanuel Kant, or Humberto Maturana. Recently, the discussion has received more widespread attention. Motivated in part from feminist philosophy of the 1990s, philosophers have combined their research efforts towards the improvement of language under the moniker of conceptual engineering and conceptual ethics. The amelioration of concepts and language is faced with several theoretical and practical challenges. What makes a concept better than another? How could a new concept be adopted by respective language users?

Information systems development is essentially concerned with language development (clarification and sources per request). Broadly, this poses the question if information systems can support conceptual engineering and, if so, in what regards. Machine learning (ML) might be a fruitful first step to guide this analysis. Contemporary ML approaches are inductive (cf. Rescher 1980): they generate potentially novel generalizations based on a set of observations. Researchers like Rees (2022) therefore suggest that they might guide the development of novel concepts.

This master’s thesis should explore the capabilities of ML to support conceptual engineering. You should identify potential tasks of conceptual engineering and what requirements they face. Then you should investigate how different ML approaches (we can disucss which in our first meetings) can serve to address these requirements.

Literature

• Burgess A, Cappelen H, Plunkett D (eds) (2020) Conceptual Engineering and Conceptual Ethics. Oxford University Press: Oxford
• Butlin P (2021) Sharing Our Concepts with Machines. Erkenntnis
• Cappelen H, Dever J (2019) Bad Language. Oxford University Press: Oxford
• Haslanger S (2012) Resisting Reality: Social Construction and Social Critique. Oxford University Press: Oxford
• Medin DL, Smith EE (1984) Concepts and Concept Formation. Annual Review of Psychology 35(35):113–138
• Montemayor C (2021) Language and Intelligence. Minds and Machines 31:471–486
• Ontañón S, Dellunde P, Godo L, Plaza E (2012) A Defeasible Reasoning Model of Inductive Concept Learning from Examples and Communication. Artificial Intelligence 193:129–148
• Rees T (2022) Non-Human Words: On GPT-3 as a Philosophical Library. Daedalus 151(2):168–182
• Rescher N (1980) Induction: An Essay on the Justification of Inductive Reasoning. Basil Blackwell: Oxford

Artificial neural networks (ANNs) denote a popular class of models used within machine learning. An ANN typically consists of multiple layers of simple processing units, so-called artificial neurons. Most current ANNs involve multiple layers of these processing units, hence the term deep learning is sometimes applied to describe them. Historically, they emerged from a neurophysiological inspiration to express the processing of mammal neurons in mathematical terms (cf. McCulloch and Pitts 1943). There exists a plethora of different approaches to the design of ANNs, some variations include the number of artificial neurons in a layer, the activation function applied, or the connection of artificial neurons between layers. From these variations have emerged several classes of ANN architectures, such as Multi-Layered Perceptrons (MLPs), Generative Adversial Networks (GANs), Convolutional Neural Networks (CNNs), or Recurrent Neural Networks (RNNs). It is conspicuous many papers, which discuss a particular ANN architecture,represent them in some diagrammatic form. This diagrammatic representation, however, does not follow any unified structure. This results in two challenges: First, ANNs are not visually comparable through an analysis of their diagrammatic representations. Second, the depicted diagrams of ANNs might lack relevant information, overseen by the original researchers. In short: It appears that the depiction of ANNs lack a conceptual modeling language.

The present thesis should adress this gap. Therefore, it is relevant to expound on the foundations and variations of ANNs as well as to explore the fundamentals of conceptual modeling languages. Based on an analysis of the design, evaluation, and application of ANNs, requirements for a corresponding modeling method should be derived. Thereupon, these insights should be used to specify a conceptual modeling method for ANNs.

Introductory Literature:

• Aggarwal CC (2018) Neural Networks and Deep Learning: A Textbook. Springer International Publishing: Cham
• Du K-L, Swamy MNS (2014) Neural Networks and Statistical Learning. Springer-Verlag: London
• Frank U (2013) Domain-Specific Modeling Languages – Requirements Analysis and Design Guidelines. In: Reinhartz-Berger I, Sturm A, Clark T, Wand Y, Cohen S, Bettin J (eds.) Domain Engineering: Product Lines, Conceptual Models, and Languages. Springer: Cham, pp. 133-157
• Kelleher JD (2019) Deep Learning. The MIT Press: Cambridge, MA, London
• McCulloch WS, Pitts W (1943) A Logical Calculus of the Ideas Immanent in Nervous Activity. Bulletin of Mathematical Biophysics 5:115-133

Vacant master thesis seeks to investigate and assess how municipal enterprises’ innovation culture influences the effectiveness of digital innovation activity

Against the backdrop of climate change and digitalization, cities all over the world are facing the need for a radical transformation towards “smartness” (Gimpel et al., 2021). To meet the increasing amount of customer expectations that cities are facing, municipal enterprises – such as electricity suppliers or waste management services – are continuously working on modernizing their digital service offerings and business models (Hosseini et al., 2018; Mora et al., 2019). Sometimes those offerings represent the replacement of analog tasks with digital tasks, for example, online appointment scheduling or the application of IoT sensors to enhance processes or estimate waiting times[1]. Such novel digital services are often the result of digital innovation activities (Hjalmarsson & Rudmark, 2012). Those innovation activities may be internally and externally driven, and in light of the smart city context, the complexity of the innovation process is increasing (Hjalmarsson & Rudmark, 2012).

This is among the reasons why digital innovations are increasingly critical to the success of municipal enterprises. Yet, the municipal sector could be characterized as rather non-innovative and reluctant to change (Hawlitschek, 2021). While the need for digital innovation is widely acknowledged, implementing the right measures (e.g., competence building, structural adjustments, new processes, and new forms of collaboration) is still a challenge to municipal enterprises. Further, measuring innovativeness is a challenging task (Hinings et al., 2018; Van Looy, 2021).

All those challenges as well as the rapid environmental developments are creating a very demanding situation for municipal companies, which are often characterized by highly bureaucratic processes, a strict matrix organization, and using static workflow processes that remain unchanged possibly even for decades. To this end, research finds that the innovation culture significantly impacts the degree of organizations’ innovativeness (Dobni, 2008; Dodge et al., 2017). However, less attention has been devoted to grasp the influence of municipal enterprises’ innovation culture on (digital) innovativeness. To address those challenges, municipal enterprises may benefit from a systematic approach to evaluate their innovation culture’s maturity level as well as degree of digital innovativeness and compare their maturity level to similar organizations.

To address this issue, we are looking for an engaged student who will address this topic within the scope of a master thesis. First, the student is expected to conduct a profound literature review and gather relevant findings from academia and practice. Further, those findings are to be extended by conducting interviews with representatives from German municipal enterprises to define and uncover the nature and relationships of municipal enterprises’ innovation culture and digital innovativeness. Subsequently, the student is expected to develop a measurement instrument (i.e., survey) that later allows measuring municipal enterprises’ innovation culture, its maturity level as well as its impact on the effectiveness of digital innovation activity.

References

Dobni, C. B. (2008). Measuring innovation culture in organizations: The development of a generalized innovation culture construct using exploratory factor analysis. European journal of innovation management.

Dodge, R., Dwyer, J., Witzeman, S., Neylon, S., & Taylor, S. (2017). The Role of Leadership in Innovation: A quantitative analysis of a large data set examines the relationship between organizational culture, leadership behaviors, and innovativeness. Research-Technology Management, 60(3), 22-29.

Gimpel, H., Graf-Drasch, V., Hawlitschek, F., & Neumeier, K. (2021). Designing smart and sustainable irrigation: A case study. Journal of Cleaner Production, 315, 128048.

Hawlitschek, F. (2021). Interview with Benjamin Scheffler on “The future of waste management”. Business & Information Systems Engineering, 63(2), 207-211.

Hinings, B., Gegenhuber, T., & Greenwood, R. (2018). Digital innovation and transformation: An institutional perspective. Information and Organization, 28(1), 52-61.

Hjalmarsson, A., & Rudmark, D. (2012). Designing digital innovation contests. In International Conference on Design Science Research in Information Systems (pp. 9-27). Springer, Berlin, Heidelberg.

Hosseini, S., Frank, L., Fridgen, G., & Heger, S. (2018). Do not forget about smart towns. Business & Information Systems Engineering, 60(3), 243-257.

Mora, L., Deakin, M., & Reid, A. (2019). Strategic principles for smart city development: A multiple case study analysis of European best practices. Technological Forecasting and Social Change, 142, 70-97.

Van Looy, A. (2021). A quantitative and qualitative study of the link between business process management and digital innovation. Information & Management, 58(2), 103413.

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[1] Example: www.wbd-innovativ.de/projekte/intelligenter-recyclinghof