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.

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.

---

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