Digital platform ecosystems have become a dominant organizational form for interorganizational value creation. Large technology firms such as Alphabet, Meta, Apple, Microsoft, and Amazon continue to grow by leveraging the scalability and generativity of their platforms, while technology startups increasingly launch successful platforms in areas such as artificial intelligence (AI), e-commerce, and digital payments.

Digital platforms can be understood as sets of digital resources that enable value-creating interactions between external actors acting as producers and consumers (Constantinides et al., 2018). These interactions give rise to complex ecosystems composed of largely autonomous actors with heterogeneous goals and capabilities (Adner, 2017).

Platform owners establish and sustain such ecosystems by orchestrating participants’ activities to enhance the overall value proposition (Kindermann et al., 2022). This orchestration is enacted through platform governance mechanisms (Rietveld & Schilling, 2021), defined as activities by which platform owners shape ecosystem functioning (Chen et al., 2022). Unlike traditional command-and-control approaches, platform governance relies on connect-and-coordinate measures to influence actors who are not hierarchically controlled and may resist centralized authority (Tilson et al., 2010).

Algorithmic governance has become a central element of platform governance as platforms increasingly embed AI-driven coordination, control, and decision-making into their core operations. Intelligent algorithms transform boundary resources into active mediators that interpret data, recommend actions, allocate tasks, and make decisions on behalf of platform actors (Wessel et al., 2025). Consequently, platform owners must govern algorithmic systems that shape interactions and outcomes across the ecosystem while ensuring fairness, reliability, and accountability.

The growing reliance on AI-based evaluation amplifies concerns about fairness and accountability, as algorithmic decisions may appear arbitrary or discriminatory (Rosenblat & Stark, 2015; Wiener et al., 2023). At the same time, calls for transparency and explainability introduce additional complexity: while algorithmic explanations can enhance comprehension and trust, excessive transparency may overwhelm users or enable system gaming (Zhang et al., 2022).

A central challenge remains algorithmic opacity, as participants often lack insight into how algorithms rank, match, evaluate, or sanction them due to system complexity and limited disclosure (Kellogg et al., 2020; Möhlmannn et al., 2023). This opacity undermines trust and constrains actors’ ability to assess fairness. Moreover, increased automation reduces opportunities for human interaction, negotiation, and feedback, potentially leading to isolation and dehumanization among workers and complementors (Möhlmann et al., 2021; Wiener et al., 2023).

Platforms further employ algorithmic nudging mechanisms, such as personalized prompts or gamification, to steer behavior. While efficient, these mechanisms risk manipulation and reinforce power asymmetries if not transparently governed (Benlian et al., 2022). Moreover, the growing reliance on AI-based evaluation intensifies concerns about fairness, bias, and accountability, as algorithmic decisions may appear arbitrary or discriminatory (Rosenblat & Stark, 2015; Wiener et al., 2023).

Research Question: What central research streams emerge from the existing literature on algorithmic governance in digital platform ecosystems, and which open research questions remain?

Goal: Against this background, this thesis aims to develop a structured research agenda for algorithmic governance in digital platform ecosystems. To achieve this goal, the study will first identify and systematize key research streams within the existing literature on algorithmic governance in platform ecosystems. Particular attention will be given to challenges specific to transaction and innovation platforms (Gawer, 2014; Hein et al., 2020), as well as to distinct application domains.

The identified research streams and their associated problem spaces will then be synthesized into an integrative overview. Building on this synthesis, the thesis will develop a dedicated research agenda for each stream, highlighting central challenges and deriving promising directions for future research.

To delineate the relevant literature corpus, a systematic literature review (SLR) will be conducted in accordance with the guidelines proposed by Webster and Watson (2002) and by vom Brocke et al. (2015). The analysis will involve qualitative coding of the literature, drawing on established methodological approaches by Bandara et al. (2015) and Wolfswinkel et al. (2013). The use of reference management software (e.g., Zotero or Citavi) and qualitative data analysis software (e.g., MAXQDA) is mandatory.

This thesis is intended primarily aimed at Bachelor’s students. Writing the thesis in English is also possible and preferred.

The thesis is particularly well suited for students who have previously conducted a systematic literature review in the context of a seminar at the SOFTEC chair and/or who already possess foundational knowledge of SLR methodologies and the associated software tools.

Interested students are required to submit an extended proposal that details the systematic literature review (search terms and data sources) as well as an outline of the thesis (up to the second level of structure).

References:

Adner, R. (2017). Ecosystem as Structure. Journal of Management, 43(1), 39–58.

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.

Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic Management. Business & Information Systems Engineering, 64(6), 825–839.

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.

Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—Platforms and Infrastructures in the Digital Age. Information Systems Research, 29(2), 381–400.

Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–1249. doi.org/10.1016/j.respol.2014.03.006

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

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410.

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).

Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control. MIS Quarterly, 45(4), 1999–2022.

Möhlmannn, M., Salge, C. A. d. L., & Marabelli, M. (2023). Algorithm Sensemaking: How Platform Workers Make Sense of Algorithmic Management. Journal of the Association for Information Systems, 24(1), 35–64.

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

Rosenblat, A., & Stark, L. (2015). Uber's Drivers: Information Asymmetries and Control in Dynamic Work. SSRN Electronic Journal.

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., Riemer, K., Niehaves, B., Plattfaut, R., & Cleven, A. (2015). Standing on the Shoulders of Giants: Challenges and Recommendations of Literature Search in Information Systems Research. Communications of the Association for Information Systems, 37(1).

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

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.

Wiener, M., Cram, W. A., & Benlian, A. (2023). Algorithmic control and gig workers: a legitimacy perspective of Uber drivers. European Journal of Information Systems, 32(3), 485–507.

Wolfswinkel, J. F., Furtmueller, E., & Wilderom, C. P. M. (2013). Using grounded theory as a method for rigorously reviewing literature. European Journal of Information Systems, 22(1), 45–55.

Zhang, A., Boltz, A., Wang, C. W., & Lee, M. K. (2022). Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.

Digital platform ecosystems have become a dominant organizational form for interorganizational value creation. Large technology firms such as Alphabet, Meta, Apple, Microsoft, and Amazon continue to grow by leveraging the scalability and generativity of their platforms, while technology startups increasingly launch successful platforms in areas such as artificial intelligence (AI), e-commerce, and digital payments.

Digital platforms can be understood as sets of digital resources that enable value-creating interactions between external actors acting as producers and consumers (Constantinides et al., 2018). These interactions give rise to complex ecosystems composed of largely autonomous actors with heterogeneous goals and capabilities (Adner, 2017).

Platform owners establish and sustain such ecosystems by orchestrating participants’ activities to enhance the overall value proposition (Kindermann et al., 2022). This orchestration is enacted through platform governance mechanisms (Rietveld & Schilling, 2021), defined as activities by which platform owners shape ecosystem functioning (Chen et al., 2022). Unlike traditional command-and-control approaches, platform governance relies on connect-and-coordinate measures to influence actors who are not hierarchically controlled and may resist centralized authority (Tilson et al., 2010).

Algorithmic governance has become a central element of platform governance as platforms increasingly embed AI-driven coordination, control, and decision-making into their core operations. Intelligent algorithms transform boundary resources into active mediators that interpret data, recommend actions, allocate tasks, and make decisions on behalf of platform actors (Wessel et al., 2025). Consequently, platform owners must govern algorithmic systems that shape interactions and outcomes across the ecosystem while ensuring fairness, reliability, and accountability.

The growing reliance on AI-based evaluation amplifies concerns about fairness and accountability, as algorithmic decisions may appear arbitrary or discriminatory (Rosenblat & Stark, 2015; Wiener et al., 2023). At the same time, calls for transparency and explainability introduce additional complexity: while algorithmic explanations can enhance comprehension and trust, excessive transparency may overwhelm users or enable system gaming (Zhang et al., 2022).

A central challenge remains algorithmic opacity, as participants often lack insight into how algorithms rank, match, evaluate, or sanction them due to system complexity and limited disclosure (Kellogg et al., 2020; Möhlmannn et al., 2023). This opacity undermines trust and constrains actors’ ability to assess fairness. Moreover, increased automation reduces opportunities for human interaction, negotiation, and feedback, potentially leading to isolation and dehumanization among workers and complementors (Möhlmann et al., 2021; Wiener et al., 2023).

Platforms further employ algorithmic nudging mechanisms, such as personalized prompts or gamification, to steer behavior. While efficient, these mechanisms risk manipulation and reinforce power asymmetries if not transparently governed (Benlian et al., 2022). Moreover, the growing reliance on AI-based evaluation intensifies concerns about fairness, bias, and accountability, as algorithmic decisions may appear arbitrary or discriminatory (Rosenblat & Stark, 2015; Wiener et al., 2023).

Research Question: What central research streams emerge from the existing literature on algorithmic governance in digital platform ecosystems, and which open research questions remain?

Goal: Against this background, this thesis aims to develop a structured research agenda for algorithmic governance in digital platform ecosystems. To achieve this goal, the study will first identify and systematize key research streams within the existing literature on algorithmic governance in platform ecosystems. Particular attention will be given to challenges specific to transaction and innovation platforms (Gawer, 2014; Hein et al., 2020), as well as to distinct application domains.

The identified research streams and their associated problem spaces will then be synthesized into an integrative overview. Building on this synthesis, the thesis will develop a dedicated research agenda for each stream, highlighting central challenges and deriving promising directions for future research.

To delineate the relevant literature corpus, a systematic literature review (SLR) will be conducted in accordance with the guidelines proposed by Webster and Watson (2002) and by vom Brocke et al. (2015). The analysis will involve qualitative coding of the literature, drawing on established methodological approaches by Bandara et al. (2015) and Wolfswinkel et al. (2013). The use of reference management software (e.g., Zotero or Citavi) and qualitative data analysis software (e.g., MAXQDA) is mandatory.

This thesis is intended primarily aimed at Bachelor’s students. Writing the thesis in English is also possible and preferred.

The thesis is particularly well suited for students who have previously conducted a systematic literature review in the context of a seminar at the SOFTEC chair and/or who already possess foundational knowledge of SLR methodologies and the associated software tools.

Interested students are required to submit an extended proposal that details the systematic literature review (search terms and data sources) as well as an outline of the thesis (up to the second level of structure).

References:

Adner, R. (2017). Ecosystem as Structure. Journal of Management, 43(1), 39–58.

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.

Benlian, A., Wiener, M., Cram, W. A., Krasnova, H., Maedche, A., Möhlmann, M., Recker, J., & Remus, U. (2022). Algorithmic Management. Business & Information Systems Engineering, 64(6), 825–839.

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.

Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—Platforms and Infrastructures in the Digital Age. Information Systems Research, 29(2), 381–400.

Gawer, A. (2014). Bridging differing perspectives on technological platforms: Toward an integrative framework. Research Policy, 43(7), 1239–1249. doi.org/10.1016/j.respol.2014.03.006

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

Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410.

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).

Möhlmann, M., Zalmanson, L., Henfridsson, O., & Gregory, R. W. (2021). Algorithmic Management of Work on Online Labor Platforms: When Matching Meets Control. MIS Quarterly, 45(4), 1999–2022.

Möhlmannn, M., Salge, C. A. d. L., & Marabelli, M. (2023). Algorithm Sensemaking: How Platform Workers Make Sense of Algorithmic Management. Journal of the Association for Information Systems, 24(1), 35–64.

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

Rosenblat, A., & Stark, L. (2015). Uber's Drivers: Information Asymmetries and Control in Dynamic Work. SSRN Electronic Journal.

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., Riemer, K., Niehaves, B., Plattfaut, R., & Cleven, A. (2015). Standing on the Shoulders of Giants: Challenges and Recommendations of Literature Search in Information Systems Research. Communications of the Association for Information Systems, 37(1).

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

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.

Wiener, M., Cram, W. A., & Benlian, A. (2023). Algorithmic control and gig workers: a legitimacy perspective of Uber drivers. European Journal of Information Systems, 32(3), 485–507.

Wolfswinkel, J. F., Furtmueller, E., & Wilderom, C. P. M. (2013). Using grounded theory as a method for rigorously reviewing literature. European Journal of Information Systems, 22(1), 45–55.

Zhang, A., Boltz, A., Wang, C. W., & Lee, M. K. (2022). Algorithmic Management Reimagined For Workers and By Workers: Centering Worker Well-Being in Gig Work. In Proceedings of the 2022 CHI Conference on Human Factors in Computing Systems.

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.