Topics for theses

Current calls for proposal by topic

General information

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

Please apply exclusively via the contact form linked here.

Thesis applications - only here!

Digital transformation of companies

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. One research focus in this research field is, for example, the investigation of digital capabilities and competencies on company and individual level. What (new) digital capabilities and competencies are needed for the digital transformation and beyond? How are these built up? Where are the capabilities and competencies located in a company? And how are capabilities, competencies, knowledge and skills connected? 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.
Management of Digital Innovation in Companies
The diffusion of digital technologies in society poses 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 dedicated 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 aim is to explore DIUs and digital innovation activities in organizations in a differentiated way, 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 to work on the topic, cooperations with companies are possible. Exemplary research questions are: How does the digital innovation process work in established companies (or "digital-born" companies)? What are the reasons why digital innovation processes in organizations are discontinued?
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. Disclaimer: Supervision is only possible up to and including September 2024.
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.
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).

Deepfakes in the context of digital disinformation and misinformation

The current media landscape is undergoing an unprecedented transformation, driven by the exponential development of digital technologies and the enormous amount of data available. In this era of digital revolution, the application of (generative) AI takes center stage. AI technologies such as machine learning and deep neural networks have radically reshaped the media industry, offering possibilities that were unthinkable just a short time ago. In this context, deepfakes - artificially generated media content - are becoming increasingly important and present both challenges and opportunities.

As synthetic media content, deepfakes have the potential not only to shake our understanding of reality and authenticity, but also to influence the credibility of media organizations and the dissemination of information. They are therefore a reflection of current times and represent one of the most recent and important developments in media technology. As an integral part of digital misinformation and disinformation, deepfakes raise urgent questions and require in-depth analysis.

In the wake of these developments, there is an acute need to explore and understand the multiple aspects of deepfakes in the context of digital disinformation and misinformation. Accordingly, a thesis could examine various aspects of the topic:

A) Technological focus

Deepfake creation: The thesis could focus on the techniques and methods used to create deepfakes, including the use of generative neural networks and their further development.
Deepfake detection and mitigation: Investigate the opportunities and challenges in detecting and mitigating deepfakes to reduce their proliferation.

B) Stakeholder perspectives

End-user/consumer perspective: Analyze how end-users perceive deepfakes and the impact of these perceptions on trust in media organizations and their content.
Organizational perspective (especially media organizations): Conduct case studies to examine how media organizations deal with deepfakes in practice and develop strategies to address this challenge.
Deepfake generators: Investigate the motives behind the creators of deepfakes and analyze different dimensions of these activities.
Targeted individuals: Analyze the impact of deepfakes on those who fall victim to fake content and examine potential legal, societal as well as business consequences

Potential questions:

How are deepfakes used in different areas of media production, distribution, and monetization?
What are the social implications of the use of deepfakes in the media, particularly in terms of opinion-forming and social discourse? And how do these affect the value creation of media companies?
To what extent do deepfakes change the role and self-image of media professionals?
Which new business models are emerging through the use of deepfakes and how is this emergence influencing competition in the media landscape?

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

Data-based business concepts

Data protection for digital services // 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.

Business models in data-driven ecosystems
With the ongoing digital transformation, companies are increasingly interconnected within ecosystems where they collaborate to generate mutual value. Additionally, the rising datafication enables the realization of new data-driven business models. Data are increasingly regarded as a strategic resource, gaining significance for companies ("data are the new oil"). Harnessing data impacts value creation, value proposition, and value realization within ecosystems, thereby influencing the competitive advantage of companies.
In practice, decentralized ecosystems are emerging as alternatives to private platforms. While a focal company controls private digital platforms, decentralized ecosystems aim to ensure data sovereignty for participants, fostering a trustworthy collaboration. This new setting impacts the business models pursued by the ecosystem participants.
From that, questions arise such as: How can ecosystem participants co-create value through this decentralized infrastructure by leveraging data? What factors influence value creation in decentralized data ecosystems? How can value be fairly distributed within the ecosystem? Can decentralized ecosystems contribute to achieving societal goals, such as sustainability objectives? What data-driven products and services emerge in decentralized ecosystems?
If you are interested in this topic, please contact Jana Ammann via the contact form (link at the top of this page). Submissions 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. Literature reviews and empirical research methods are suitable for dealing with the topic, cooperation with companies is possible.

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 or Master 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. Depending on the scope and focus of the review this topic can be addressed in a bachelor or master thesis. 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.
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.
Digital Transformation Strategies (Master Thesis)
Digital Transformation Strategies specify how organizations approach their digital transformation endeavors. While previous research has considered the different components that are important when developing such strategies, the literature does not explain how and why digital transformation strategies change over time. In this thesis, you should conduct a multiple case study to address this gap. Specifically, based on interviews with key stakeholders, you should develop a longitudinal perspective on the digital transformation strategy formation and enactment process. 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.
Managing Worker in Algorithmic Management (Bachelor and Master)
Companies are increasingly turning to algorithmic management to make their operations more efficient and compete in the market. Algorithmic mangement refers to the use of algorithms and artificial intelligence to support or replace management decisions. This innovative approach presents both opportunities and challenges, and raises interesting questions, particularly regarding the design of such systems and their integration into existing management structures. If you are interested in this topic, please use the contact form (link on top of the page) to get in touch with Luc Becker. The following topics are currently open for application:
- Experiments: Effects of algorithms' opacity in payment and work information on platform workers' behavior (Master)
- Qualitative 1: A Taxonomy of user modifyability in AI & Algorithmic Management Systems (Master)
- Qualitative 2: Investigating how analytics influence highly-skilled employees. For exampe Sports Analytics or Planing in Hospitals (Master)
- Literature: A Short History of Algorithms at Work / Defining Algorithmic Management / Between Control and Support by Algorithms at Work / Defining Resistance, Algoactivism and Workarounds (Bachelor)