Bachelor and master theses

Platform ecosystems involve key actors, including platform owners, users, producers, third-party developers, and regulators. Previous research has explored the dynamics among these actors, focusing on aspects like network effects, value co-creation, competition, collaboration, user duality, and governance structures. Recently, decentralized platform ecosystems have emerged, functioning without a central authority, employing, for instance, blockchain technology for enhanced security and transparency. These ecosystems facilitate peer-to-peer interactions, enabling direct resource sharing among users. Participants often engage in self-governance through community voting, fostering collaboration while enhancing user privacy and data control. This evolution prompts new research questions concerning power dynamics, governance, and user privacy. A final thesis could address these aspects further.

Supervision: Prof. Dr. Thomas Kude

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• Clough, D. R., & Wu, A. (2022). Artificial intelligence, data-driven learning, and the decentralized structure of platform ecosystems. Academy of Management Review, 47(1).
• Parker, G., & van Alstyne, M. (2008). Managing Platform Ecosystems. ICIS 2008 Proceedings, 53.

As platforms gather vast amounts of personal data, users risk exposure to identity theft, surveillance, and misuse of their information. Moreover, centralization often creates single points of failure, making platforms vulnerable to cyberattacks. How can platforms enhance data security while maintaining user trust? What frameworks can be developed to ensure user privacy rights in compliance with regulations? Exploring these questions could allow students to explore in their final thesis how to manage privacy and security concerns in platform ecosystems.

Supervision: Prof. Dr. Thomas Kude

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• Bélanger, F., and Crossler, R. E. 2011. Privacy in the Digital Age: A Review of Information Privacy Research in Information Systems. MIS Quarterly, 35(4).
• Culnan, M. J., and Williams, C. C. 2009. How Ethics Can Enhance Organizational Privacy: Lessions Fom the Choicepoint and TJX Data Breaches. MIS Quarterly, 33(4).
• Teubner, T., & Flath, C. M. (2019). Privacy in the sharing economy. Journal of the Association for Information Systems, 20(3).

The emergence of Artificial Intelligence (AI) as a new player in the corporate environment has introduced significant uncertainties, especially regarding the potential displacement of human labour. Many organizations currently lack a clear legal framework to navigate this transition, leaving them to develop their own strategies for integrating AI technologies. This situation raises important ethical questions about the impact of AI on employment and the workplace environment. To address these challenges, the debate is all about AI Governance, but how does AI Governance unfold in organizations? This interesting research question could be addressed in a final thesis.

Supervision: Prof. Dr. Thomas Kude

Level: Master

Methodology: Empirical Investigation

Literature:

• M?ntym?ki, M., Minkkinen, M., Birkstedt, T., & Viljanen, M. (2022). Defining organizational AI governance. AI and Ethics, 2(4), 603–609.
• M?ntym?ki, M., Minkkinen, M., Zimmer, M., Birkstedt, T., & Viljanen, M. (2023). Designing an AI governance framework: From research-based premises to meta-requirements. ECIS 2023 Research Papers.

Digital data does not automatically become useful information. In order to support decisions, be reused, or be shared across organizations, data must be generated, cleaned, structured, documented, interpreted, and embedded in concrete contexts of use. This work on and with data is often referred to as data work.

This topic area focuses on how data gain meaning as they move through organizations and data ecosystems. Data do not have fixed or self-evident meaning. Already during data collection, certain assumptions, categories, and purposes are inscribed into them. In later phases, data are processed, enriched, curated, standardized, and documented so that other actors can understand, assess, and reuse them.

These processes are rarely linear. Data move through complex data journeys in which different actors, technologies, platforms, standards, and organizational units interact. This applies both within individual organizations and in interorganizational data ecosystems, such as data spaces, smart city initiatives, research data infrastructures, or sector-specific data networks.

The aim of this topic area is to better understand how data are transformed, contextualized, and made usable through data work. This also raises questions of responsibility: Who decides which data are relevant? Who documents their origin, quality, and limitations? How is data translated between different contexts of use? And what is needed to ensure that data is not only technically available, but also meaningful, traceable, and responsibly usable?

Possible research questions concern the role of different actors in creating data meaning, the importance of metadata, standards, and documentation, the translation of data between different contexts, responsibilities along the data lifecycle, or the conditions for trustworthy data use. 

Students may develop their own empirical or conceptual research topic within this broader topic area.

Supervision: Stefanie Ulschmid

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• Leonelli, S. (2020). Learning from data journeys. In Data journeys in the sciences (pp. 1-24). Cham: Springer International Publishing.
• Jarvenpaa, S. L., & Essén, A. (2023). Data sustainability: Data governance in data infrastructures across technological and human generations. Information and Organization, 33(1), 100449.
• Oliveira, M. I., Barros Lima, G. D. F., & Farias Lóscio, B. (2019). Investigations into data ecosystems: a systematic mapping study. Knowledge and Information Systems, 61(2), 589-630.

Platformization, what we define as the increasing adoption of digital platforms by organizations, is transforming operational processes and structures within companies. This shift enables greater efficiency through streamlined communication, enhanced collaboration, and improved data management. However, in highly regulated sectors like healthcare and education, this transformation presents unique challenges. Organizations must navigate compliance with stringent regulations while leveraging the benefits of digital platforms. This necessitates a re-evaluation of data governance policies, privacy considerations, and security measures. So far, limited research has been conducted in this field, presenting an opportunity for a final thesis to explore it further, e.g. what are the implications of “Platformization” for highly regulated sectors, such as healthcare and education? How can AI be used in healthcare and/or education?

Supervision: Stefanie Badmann

Level: Bachelor / Master

Methodology: Empirical Investigation

Literature:

• Ozalp, H., Ozcan, P., Dinckol, D., Zachariadis, M., & Gawer, A. (2022). “Digital Colonization” of Highly Regulated Industries: An Analysis of Big Tech Platforms’ Entry into Health Care and Education. California Management Review, 64(4), 78–107.
• Setia, P., Soh, F., & Deng, K. (2020). Platformizing organizations: a synthesis of the literature. Oxford Research Encyclopedia of Business and Management.

Data is increasingly regarded as one of the central foundations of digital value creation. Companies, public organizations, and platforms collect, connect, and analyze data to improve decisions, automate processes, develop new services, or transform business models. At the same time, it often remains unclear what the value of data actually consists of. Does it emerge through better information, new forms of automation, personalized offerings, more efficient coordination, or only once data is economically used and monetized?

This question becomes particularly relevant in the context of algorithmic automation, data-driven decision-making, and AI-based business models. In these contexts, data is not simply a resource that organizations own or analyze. It also changes how organizations work, make decisions, and interact with other actors. Data influences value creation processes, coordination mechanisms, power relations, and the boundaries of firms.

The aim of this topic area is to examine the value of data from an organizational and information systems theory perspective. The central question is how data become valuable within organizations and under what conditions this value can actually be realized. Different forms of data value may be distinguished, such as the truth and knowledge value of data, its contribution to data-driven value creation, or its role in capturing and appropriating economic value potential.

Possible research questions concern digital business models, data-based automation, and organizational change: How do data-driven value creation logics emerge? How can the knowledge value, use value, and economic value of data be distinguished? What organizational capabilities, structures, and governance mechanisms do companies need in order to use data meaningfully? How do algorithmic systems and AI change the functions, boundaries, and responsibilities of organizations? And when does data-driven automation actually lead to value creation, rather than merely generating new complexity, dependencies, or control problems?

Students may develop their own theoretical-conceptual or empirical research topic within this broader topic area. Possible theses could focus, for example, on data-driven business models, AI-based automation, data governance, platform companies, decision support systems, or the question of how organizations use data to establish new value creation and business model logics.

Supervision: Stefanie Ulschmid

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• Alaimo, C., Kallinikos, J., & Aaltonen, A. (2020). Data and value. In Handbook of digital innovation (pp. 162-178). Edward Elgar Publishing.
• Constantiou, I., Joshi, M., & Stelmaszak, M. (2023). Organizations as digital enactment systems: A theory of replacement of humans by digital technologies in organizational scanning, interpretation, and learning. Journal of the Association for Information Systems, 24(6), 1770-1798.
• Xu, D., Indulska, M., Someh, I. A., & Shanks, G. (2024). Time to reassess data value: The many faces of data in organizations. The Journal of Strategic Information Systems, 33(4), 101863.

Content moderation on platforms is a central issue in the digital world. Platform operators are responsible for striking a balance between freedom of expression and protection against harmful or inappropriate content. Platforms are increasingly using algorithm-based moderation systems to identify and remove unwanted content. This raises the question of how effective these systems are and what impact they have on the user experience and engagement on platforms. Algorithmic moderation and human intervention can lead to different outcomes in different contexts, which also influences user trust in the platforms. Particularly on platforms such as Reddit or social networks, where user interaction is paramount, the question of accountability and transparency in moderation decisions poses new challenges. On platforms such as live-streaming services and social media channels, content moderation significantly influences the dynamics of user participation. A potential thesis could explore how content moderation through algorithms and human intervention shapes user behavior, platform governance, and social norms on digital platforms.

Supervision: Stefanie Badmann

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• 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. https://doi.org/10.1287/isre.2021.0036
• Zhang, X., Wei, Z., Du, Q., & Zhang, Z. (2026). Social Media Moderation and Content Generation: Evidence From User Bans. MIS Quarterly, 50(1), 211–242. https://doi.org/10.25300/MISQ/2025/18108
• Zhao, K., Hong, Y., Ma, T., Lu, Y., & Hu, Y. (2025). Lost in the Crowd: How Group Size and Content Moderation Shape User Engagement in Live Streaming. Information Systems Research, 36(4), 2076–2095. doi.org/10.1287/isre.2022.0086

Since the the pandemic, a significant number of office employees have become familiar with platform-based teamwork, making virtual collaboration an integral part of the modern work experience. Today, it is nearly unimaginable for teams to operate without some form of online interaction. Various tools, particularly cloud-based solutions, have emerged to facilitate this collaboration, enhancing communication and productivity across geographically dispersed teams. This shift to digital teamwork not only transforms traditional workplace dynamics but also opens a rich field of research opportunities for a final thesis in this field, which could address the role of digital platforms for teamwork in organizations.

Supervision: Stefanie Badmann

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• Barley, S. R., Bechky, B. A., & Milliken, F. J. (2017). The Changing Nature of Work: Careers, Identities, and Work Lives in the 21st Century. Academy of Management Discoveries, 3(2).
• Kude, T., Mithas, S., Schmidt, C. T., & Heinzl, A. (2019). How Pair Programming Influences Team Performance: The Role of Backup Behavior, Shared Mental Models, and Task Novelty. Information Systems Research, 30(4), 1145–1163.
• Kude, T.,  Foerderer, J.,  Mithas, S., &  Heinzl, A. (2023).  How deadline orientation and architectural modularity influence software quality and job satisfaction. Journal of Operations Management, 1– 24.

Digital platforms increasingly shape central areas of business, public administration, and society. They are not merely individual applications but often develop into fundamental infrastructures that connect actors, organizations, technical systems, and data sources. Examples include cloud services, data platforms, smart city infrastructures, mobility platforms, digital government platforms, and platforms in the energy, healthcare, and circular economy sectors.

This process of platformization creates new forms of governance and coordination. Platforms structure access, dependencies, decision-making processes, and responsibilities. In doing so, they can stabilize social and organizational order, for example by distributing resources more efficiently, improving provision and supply processes, supporting sustainable consumption patterns, or enabling new forms of collaboration. Examples range from mobility and sharing platforms to data-based energy systems and platform models aimed at reducing food waste.

At the same time, platformized infrastructures continuously generate digital traces, commonly referred to as trace data. These include usage data, transactions, sensor data, movement data, and communication data. Such data can be analyzed to better understand processes, steer infrastructures proactively, identify risks early, or develop new services. However, they also make behavior visible, enable control and surveillance, shift power relations, and can create new dependencies between platform operators, organizations, and users.

The aim of this topic area is to examine the opportunities and risks of platformized infrastructures at the intersection of digital sovereignty, ecological sustainability, and social order. The central question is under what conditions platforms contribute to stable, open, and sustainable infrastructures, and when they reinforce dependencies, loss of control, ecological externalities, or social inequalities.

Possible research questions concern architecture, governance, and responsibility: How should digital infrastructures be designed so that they remain open, secure, controllable, and ecologically responsible? What role does trace data play in governing critical and sustainability-relevant infrastructures? How can open-source solutions, interoperability, or open standards reduce digital and ecological dependencies? And under what conditions do platformized infrastructures contribute to the stabilization or destabilization of social, organizational, and ecological order?

Students may develop their own literature-based, conceptual, or empirical research topic within this broader topic area.

Supervision:  Stefanie Ulschmid

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• Constantinides, P., Henfridsson, O., & Parker, G. G. (2018). Introduction—platforms and infrastructures in the digital age. Information Systems Research, 29(2), 381-400.
• Veit, D. J., & Thatcher, J. B. (2023). Digitalization as a problem or solution? Charting the path for research on sustainable information systems. Journal of Business Economics, 93(6), 1231-1253.
• Zuboff, S. (2022). Surveillance capitalism or democracy? The death match of institutional orders and the politics of knowledge in our information civilization. Organization Theory, 3(3).

This thesis topic explores the evolving role of the press and traditional media as indispensable intermediaries in ensuring accurate information and fostering informed local communities. While local journalism is critical for democracy and social cohesion, it faces increasing challenges from the dominance of digital platforms and the rise of social media, where misinformation can spread unchecked. Your research will examine how traditional media can adapt to remain relevant, the innovations needed to sustain local journalism, or the potential strategies to counteract the misinformation challenges posed by social media.

Supervision:  Prof. Dr. Thomas Kude

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation / Design Science

Literature:

• Abbasi, A., Greenwood, B. N., Mazmanian, M., Miranda, S., & Seamans, R. Call for Papers: Special Issue-The Institutional Press in the Digital Age.
• Kitchens, B., Johnson, S. L., & Gray, P. (2020). Understanding echo chambers and filter bubbles: The impact of social media on diversification and partisan shifts in news consumption. MIS quarterly, 44(4).
• Matherly, T., & Greenwood, B. N. (2022). No news is bad news: The internet, corruption, and the decline of the fourth estate. Corruption, and the Decline of the Fourth Estate (September 8, 2022).

This topic starts from the premise that many of the traditional teaching and assessment methods in higher education have already become or will likely become obsolete in the near future. For example, asking a relatively simple question for a take-home assignment may not be adequate anymore. Instead of rejecting such developments, this thesis takes a positive stance and aims to explore how higher education needs to evolve in order to make the best possible use of AI tools. How can platforms and tools be used and designed? How do these platforms and tools complement personal, in-class interaction? How can student-led research projects and academic writing evolve and how can student contributions be assessed?

Supervision:  Prof. Dr. Thomas Kude / Stefanie Badmann

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation / Design Science

Literature:

• Betts, M., & Rosemann, M. (2022). The New Learning Economy: Thriving Beyond Higher Education. Routledge.
• Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: the state of the field. International Journal of Educational Technology in Higher Education, 20(1), 22.
• Komljenovic, J. (2021). The rise of education rentiers: digital platforms, digital data and rents. Learning, Media and Technology, 46(3), 320–332.

Are you eager to explore the realm of digital platforms and their impact on firms, industries, and society, but find that none of our suggested topics align with your interests? Perhaps you have a company partner in mind for collaboration? If you believe your interests align with our research focus, please share your ideas with us through the contact field below, providing a brief description (approx. 500-1,000 words) of what you have planned for your final thesis.

Level: Bachelor / Master

Methodology: Literature Review / Empirical Investigation

Literature:

• De Reuver, M., S?rensen, C., & Basole, R. C. (2018). The Digital Platform: A Research Agenda.Journal of Information Technology, 33(2), 124–135.