Theses

This site provides information for students interested in writing their Bachelor or Master thesis with the chair of digital transformation management.

The following topics are currently available:

• MA: Metaverse Startup Business Models - Key Characteristics and Archetypes (contact: hanelt[at]uni-kassel.de)

  The Metaverse becomes increasingly relevant in the theoretical discourse and for practitioners. Both, established companies such as Meta, formerly known as Facebook, and startup companies (e.g. Decentraland) are interested in the Metaverse and conceivable applications of it. This raises the questions how business models of those startups may differ from traditional business models and how they differentiate from each other.

  This thesis will shed light on these questions by developing a business model taxonomy based on a sample of Metaverse related startup businesses and derive archetypes of business models.

• BA/MA: Scrum Master: Exploring their role in digital transformation (contact: hanelt[at]uni-kassel.de)

• MA: Analysis and Identification of Ecosystem Patterns (contact: henrik.pohsner[at]uni-kassel.de)
  Ecosystems in a business context are a widely researched phenomenon in IS and management literature. Among other things, researchers attempted to understand ecosystem characteristics and different types of ecosystems, but there is currently no systematic and comprehensive examination of ecosystem patterns.

  The aim of this thesis is to provide a comprehensive analysis of ecosystem patterns and underlying characteristics that classify ecosystems and can be used to identify patterns. This is achieved by developing a taxonomy for ecosystems which includes important dimensions characterizing different types of ecosystems. The focus of the thesis lies on the identification of known characteristics, develop a taxonomy and to define archetypes/patterns that can be found in practice.

• MA: Developing a Taxonomy for Ecosystem Modelling Approaches (contact: cornelius.reh[at]uni-kassel.de)
  To help managers understand their company's ecosystem better and make informed decisions, it's crucial to grasp how their ecosystem functions and what relationships in the ecosystem are critical for their firm. Visualizations are handy for spotting strengths and weaknesses in comparison to other ecosystems. However, there's currently no standard way to model ecosystems, leading to various approaches. The thesis aim is to fill this gap by developing a taxonomy, for these modeling approaches. Following Nickerson et al.'s (2013) methodology for taxonomy development and from insights of existing literature, the thesis should seek to systematically organize and categorize the different ways ecosystems are modeled.

• BA/MA: Identifying and Analyzing Data Sources for Potential Business Partners (contact: cornelius.reh[at]uni-kassel.de)

• MA: Digital innovation: A meta-analysis (contact: hanelt[at]uni-kassel.de)
• MA: The basis to revolutionize Recruiting Management – A technical Literature Review on Machine Learning solutions in Job-Candidate Matching (contact: steven.goerlich[at]uni-kassel.de)
  While successful recruiting is essential to every businesses’ long term profitability, little is known outside the HR sector of the immense costs involved in web based recruiting processes.
  The aim of this thesis is the revision of previous attempts in academic and industry that utilize advance data processing techniques to match job applicants with job descriptions. Focus lies on a technical review while neglecting the widely discussed ethical implications of such a solution.
  For Master Thesis candidates with strong technical background the thesis title can be modified to create technical designs based on previous approaches (lit review still included).
• MA: Trendsetter Machine Learning – design artefacts for data-driven, real-time HR trend Detection (contact: steven.goerlich[at]uni-kassel.de)
  Trends and topics emerge and fade on an increasing rate in a digitalized world. For news outlets, content creators and related industries it is an extraordinary challenge to detect and track trends on social media and other communication-based networks.

  A candidate with a technical background will design machine learning based artefacts that help to identify and monitor trend, such as news and frequently discussed topics within HR Management and Operations. This includes a review of existing approaches and the elaboration and evaluation of data sources.

• MA: Building Data Lakes for Text-Based Machine Learning: A Design Science Approach (contact: hanelt[at]uni-kassel.de)

If you are interested in one of the topics, please contact the respective advisor via e-mail.