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 

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.