Topics for theses

Current calls for proposal by topic

Please find here our current calls for applications for theses by topic, as well as the name of the respective supervisor

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Digital transformation management

Beyond Digital Transformation: What comes next?
Digital transformation is a special management approach. It requires extensive investment, a suitable structure and, not least, the attention of top management. Within the digital transformation, companies continuously deal with new digital technologies that represent important opportunities or critical threats and, in the process, develop a systematic use of these digital technologies. However, it will not work to put a company permanently in the "special state" of digital transformation. What is required in this case is rather a "digitally defined organization" geared to constant challenges posed by digital technologies. What such a "digitally defined organization" looks like can only be roughly identified today. Companies at this stage of digital transformation need established strategic and operational mechanisms to continue to successfully drive and manage digital innovations (digital product and service innovations, digital process innovations and digital business model innovations). To investigate these mechanisms and their approaches, this future-oriented research field offers great potentials. Theses in this research field therefore offer diverse opportunities. These could, for example, deal with new concepts of digital transformation and innovation (e.g., "employee-driven digital innovation") or examine established theories of IS and from other relevant research fields (e.g., "practice theory") for as yet undiscovered potentials and connections for digital transformation. Literature reviews as well as empirical research methods are suitable for dealing with the topic, cooperations with companies are possible. If you are interested in this topic, please contact Mathias Bohrer via the contact form (link at the top of this page). Work in English is preferred.

Digital transformation in pluralistic ecosystems
The digital enterprise is more connected than ever before. Digital technologies that drive interconnectedness require a holistic and systemic view of the enterprise, its business models, and relationships with a variety of actors in digital ecosystems. This inter-organizational nature of digital business models also impacts the digital transformation of enterprises. Interdependencies of actors as well as their logics, perceptions and fairness logics, which have a major influence on the success of cooperation with partners and stakeholders, come into the focus of digital transformation management. How must the governance of digital transformation change to take account of the influence of digital ecosystems? What role do pluralistic assumptions play in the context of digital business models? How can stable compromises be established that are perceived as fair? What influence do multiple logics have on digital business models and ecosystems? If you are interested in this topic, please contact Simon Engert via the contact form (link at the top of this page). Theses in English are preferred.

Management of digital innovations in companies
The spread of digital technologies in society brings with it new requirements for established companies to develop innovations. Digital innovation is "the creation of market offerings, business processes, or models that result from the use of digital technology" (Nambisan et al. 2017, p. 224). Organizations engaged in digital innovation are undergoing digital transformation - a phenomenon that affects all areas of organizational functioning and forces companies to redefine their structures, processes, capabilities, and overall value creation.
A popular approach for established companies is to set up special structures to centralize digital innovation efforts in the form of digital innovation units (DIUs). DIUs are a new phenomenon that has only recently begun to stimulate scientific discussion.
The goal is the differentiated research of DIUs and digital innovation activities in companies, as well as implications that arise from the implementation and use of digital innovations at the organizational and employee level (post-adoption). Literature reviews as well as empirical research methods are suitable for the treatment of the topic, cooperations with companies are possible. If you are interested in this topic, please contact Laura Lohoff via the contact form (link at the top of this page). Theses in English are preferred.

Digital transformation of Mittelstand firms
The emergence of digital technologies changes customers’ expectations and organizations’ competitive landscape, which demands them to transform digitally. Among other things, this requires organizations to alter their value-creation processes, organizational structures, and culture. While research on the digital transformation of corporations is versatile, knowledge of Mittelstand firms’ digital transformation is scarce. Mittelstand firms, as the backbone of many economic areas, are currently facing challenges when managing digital transformation. Therefore, Mittelstand firms require new management approaches that consider their unique characteristics.
Investigating the underlying mechanisms when managing Mittelstand firms’ digital transformation offers great potential for this research field. The focus of the study is on the management of digital transformation in Mittelstand firms. For this purpose, various topics of digital transformation management can be investigated, such as the change in value creation through digital innovation (e.g., business model innovation) or the necessary prerequisites (e.g., organizational structures) to enable these changes, up to frameworks for the management of digital transformation (e.g., a digital transformation strategy for Mittelstand firms).
If you are interested in this topic, please contact Linus Lischke via the contact form (link at the top of this page). Theses in English are preferred.

Governance of the digital transformation
The digital transformation poses enormous challenges for organizations in almost all industries. Companies are confronted with the associated effects on their external and internal environment and must fundamentally rethink their previous value creation logic. Against this backdrop, organizations must make decisions about how to distribute the responsibilities for managing the digital transformation. Due to the strategic nature of the tasks, top management assumes a leading position in this process.
To cope with the requirements induced by digitization, the establishment of a new type of top management position is increasingly observed in practice: the Chief Digital Officer. In other contexts, however, the Chief Information Officer also takes the lead in digital transformation. In addition, the Chief Executive Officer is involved by setting the vision. In summary, we see various top management positions entrusted with digital transformation tasks, although the exact configuration and allocation are still unclear. Work in this research area offers diverse possibilities (investigation of a specific position; interplay of different positions; consideration of the governance of digital transformation detached from positions). If you are interested in this topic, please contact Christian Sciuk via the contact form (link at the top of this page). Work in English is preferred.
Governing digital ecosystems
The increasing interconnectedness of devices, individuals and companies through digital technologies opens up new opportunities for the collaborative creation of value in digital ecosystems. These ecosystems are characterised by complex relationships, strong interdependencies and turbulent dynamics. As a result, new challenges in terms of partner management are emerging within these ecosystems. This is especially the case for data ecosystems, which require a high degree of trust between the involved partners and which are organised in decentralised structures. Managing the diverse partners and their individual interests, therefore, requires innovative approaches.
This gives rise to many different questions that can be investigated in the context of a thesis: How can the architecture of an ecosystem be designed to successfully include the often extremely diverse partners? How can collaborative governance mechanisms look like? How can comprehensive strategies for the entire ecosystem be developed, and what are central components of these overarching strategies? How does the ecosystem influence the behaviour of individual participants?

If you are interested in this topic area, please contact Pauline Liebert via the contact form (link at the top of this page). Theses in English are preferred.

Digital media companies

Application of Artificial Intelligence in the Media Industry The media industry is undergoing a profound transformation driven by the exponential increase in digital technologies and data. A new driver of this change is the application of (generative) artificial intelligence (AI). AI technologies, including machine learning and deep neural networks, offer new opportunities for the media industry, from personalized content to automated journalism and advanced user behavior analytics. While AI is important in many industries, the media industry offers a particularly rich and complex landscape for the research and application of AI due to its inherent creativity and consumer-facing nature. Consequently, there is an urgent need to analyze the diverse fields of application of AI in the media landscape in order to identify opportunities and challenges:

How is AI used in different contexts of media production, distribution and monetization?
What social impacts arise from the use of AI in the media, especially in relation to opinion formation and social discourse?
How does AI change the role and self-image of media professionals?
What new business models are emerging through the use of AI and how does this impact competition and gatekeeping in the media landscape?

The research field offers a wealth of opportunities for theses. These could include conceptual considerations on the role of AI in the media industry as well as empirical studies on current applications and their effects as well as literature reviews.

If you are interested in this topic, please contact Nina Zwingmann using the contact form (link at the top of this page).

Data-based business concepts

Datenschutz bei digitalen Diensten // Metaverse & Web3 Large amounts of user data are collected and processed in digital services, which companies can use to make better decisions, develop new products or optimize marketing measures. New technological solutions also offer improved possibilities for collecting and analyzing the data. However, various scandals in the past have made users more aware of the digital corporations' "data collection frenzy" and brought privacy into focus.The increased importance of privacy and new regulatory frameworks present companies with challenges in designing their services. Furthermore, new digital services are emerging in which there is still little knowledge about the risks associated with user privacy. One example is the metaverse, which describes virtual 3D worlds in which users interact as avatars. New extended reality (XR) technologies offer improved possibilities for data collection, e.g. by means of eye tracking. Another example is data-intensive verification processes in crypto networks. Here, technical measures can be used to increase privacy protection, such as zero-knowledge proofs. Possible questions for a thesis are:
- How can metaverse services be made privacy-friendly?
- How do companies make decisions to determine privacy strategy?
- What privacy protection measures can be used on the Web3?
- How can privacy solutions be embedded in the architecture of digital technologies?

If you are interested in this subject area, please contact Julia Schulmeyer via the contact form (link at the top of this page).

Digital services and data usage
Is personalized advertising an added value for the user? What happens to data about driving behavior (smart car) or health data (smart watches)?
Personal data about users is constantly being collected via smart speakers, streaming services, social media and many other services and products. In this context, the underlying technologies such as IoT or AI require unlimited access to data in order to function. At the same time, the provider benefits from the data through service innovations. In addition, some digital business models (including advertising-based ones) are no longer profitable without data or may no longer be free for the user. The provider is in a favorable position to own the collected user data (information potential) and to profit from it (value potential), which can lead to (data protection) concerns on the part of the user, and a tension arises between use and protection of the data.
Work in this research area offers diverse topics and issues: including the informational or value potential of data, the framing of the tension, the position/ scope of action of users and providers in data use, incentives for data protection, approaches to resolving the tension.
Literature reviews, as well as empirical research methods (qualitative and quantitative) are possible. If you are interested in this topic, please contact Ronja Schwinghammer via the contact form (link at the top of this page). Papers in English are preferred.
AI at Work: Exploring the Factors Behind Employee Data Sharing
The digital transformation of workplaces and the ubiquitous presence of artificial intelligence (AI) is revolutionizing the way we work. In this era of data-driven decision-making and AI-enhanced processes, the sharing of information and data within organizations has become a critical component of success. However, this phenomenon raises a pivotal research question: What motivates employees to willingly share their data in digital workplaces dominated by AI?

The decision to share data in digital workplace environment is not a trivial one and is influenced by a complex interplay of various factors, including trust, privacy concerns, organizational culture, AI integration, and the perceived benefits and risks associated with data sharing. This thesis is motivated by the need to understand the intricacies of this behavior, which is central to the functioning of organizations in the digital age. By doing so, it aims to provide organizations and scholars with valuable insights into how they can harness the full potential of data in AI-driven workplaces while addressing the concerns and motivations of their workforce. In terms of methodology, you will uncover the determinants that drive or inhibit employees' data sharing behavior by utilizing a quantitative approach with an online survey or experiment.

If you are interested in this topic, please use the thesis supervision contact form (link at the top of this page) to get in touch with Dr. Mena Teebken.

Process and algorithmic management

Algorithmic Management Outside the Gig Economy (Master Thesis)
Increasingly algorithms take over managerial functions, i.e. they coordinate and delegate work to humans. Research approaches this phenomenon under the umbrella term "algorithmic management". The existing body of work on algorithmic management focuses on the gig/ platform economy where data and technological infrastructure for algorithms to operate is a given. For example, Uber drivers are managed by an algorithm that pre-selects drivers and determines the route they should follow. However, it is not clear how algorithmic management takes shape in industries outside of the gig/platform economy. In this master thesis, you should address this research gap and empirically investigate under what circumstances and how established organizations use algorithmic management. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Dr. Bastian Wurm.
A Review on Algorithmic Management (Bachelor Thesis)
There is an increasing number of articles that discusses different aspects of algorithmic management, i.e. when algorithms coordinate and assign work to humans. While research on this topic is rapidly growing, an overview over this research area is currently missing. In this thesis, you should review and integrate the literature on algorithmic management by means of a structured literature review. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Dr. Bastian Wurm.
A Review on Failure in Information Systems (Master Thesis)
“Fail fast, fail often”. This statement has become a mantra in the business world to highlight that individuals and organizations can learn tremendously even (or especially?) when they encounter failure. Despite the valuable learnings associated with failure, research on failure in information systems is scarce.
While there are some notable exceptions, we miss a coherent overview over how failure in information systems is treated and what we can learn from it. In this thesis, you address this gap by conducting a literature review on failure in information systems. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Dr. Bastian Wurm.
Scaling Process Mining in large Organizations (Master Thesis)
For many organizations, process mining has become an important technology to analyze and improve their business processes on a daily basis. While there are some insights on how organizations use process mining to create business value, we know little about how organizations can successfully scale process mining initiatives. That is, how can organizations move from one mined process to ten and what organizational set-up is required to do so? In this thesis, you will explore how organizations can successfully grow their process mining initiatives, which obstacles they meet on the way, and what they learned from overcoming them. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Dr. Bastian Wurm.
The Role of Process Mining for Process Improvement and Process Redesign (Master Thesis)
For many organizations, process mining has become an important technology to analyze and improve their business processes on a daily basis. There is some work on how organizations use process mining to create business value on a general level, but insights on how organizations use process mining for continuous process improvement or larger process redesign initiatives are currently missing. In this thesis, you will explore how organizations use process mining to improve their business processes, whether they have built improvement programs around process mining, and how they make sure that improvement ideas translate into increased performance. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Dr. Bastian Wurm.
Predictive Process Mining and its Application in Practice (Master Thesis)
Process mining is concerned with the analysis of business processes. One specific application domain of process mining is predictive process mining (also called predictive process monitoring) that allows organizations to predict the outcome of a certain process or its remaining execution time. There are many studies on predictive process mining that focus on technical aspects. That is, how to make algorithms more computationally efficient or how to increase prediction accuracy.
However, how organizations use predictive process mentoring and how this may impact the way they work has not yet received systematic attention. In this thesis, you will explore the application of predictive process monitoring in
practice. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Dr. Bastian Wurm.
Algorithmic Management in Theorie und Praxis
Unternehmen setzen zunehmend auf algorithmisches Management (AM), um ihre betrieblichen Abläufe effizienter zu gestalten und neue Maßstäbe in der Unternehmensführung zu setzen. AM bezieht sich auf den Einsatz von Algorithmen und künstlicher Intelligenz, um Managemententscheidungen zu unterstützen oder zu ersetzen. Diese innovative Herangehensweise bietet sowohl Chancen als auch Herausforderungen und wirft interessante Fragen auf, insbesondere in Bezug auf das Design solcher Systeme, ihre Einbindung in bestehende Managementstrukturen und die Reaktion der Mitarbeiter darauf.
AM bietet ein breites Spektrum an Möglichkeiten, um den Einsatz von Algorithmen, künstlicher Intelligenz und anderen Technologien wie People Analytics zur Verbesserung der Unternehmensführung zu untersuchen. Ziel ist es zu verstehen, wie Algorithmen komplexe Managemententscheidungen automatisieren können und wie diese neuen technologischen Ansätze die traditionellen Rollen, Hierarchien, Aufgaben und Kommunikation von Managern und Mitarbeitern verändern. Literaturüberblicke, empirische Forschungsmethoden oder gestaltungsorientierte/programmierlastige Methode eignen sich zur Bearbeitung des Themas. Kooperationen mit Unternehmen sind möglich.
Bei Interesse an diesem Themenfeld wenden Sie sich bitte über das Kontaktformular (Link oben auf dieser Seite) an Luc Becker.