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
Please apply exclusively via the contact form linked here.
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.