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: Convergence: A Literature Review of IS research utilizing the concept of convergence (contact: steven.goerlich[at]uni-kassel.de)
  Objective: This thesis aims to conduct a comprehensive literature review to explore how convergence is currently treated in IS Literature across various domains. The review will identify examples, effective methodologies, highlight gaps in current research, and suggest potentially fitting procedures to analyse job convergence over time.
  Scope:
  - Identify cases and methods of convergence
  - Synthesize findings from diverse research to build a holistic and exhaustive overview of theoretical and practical concepts to describe and identify convergence

• MA: Methods and Applications of Statistical Physics in Information Systems: A Literature Review (contact: steven.goerlich[at]uni-kassel.de)
  Objective: This thesis will conduct a comprehensive literature review of which concepts from statistical physics, such as chaos, entropy, phase transitions, and statistical mechanics, have been applied in Information Systems research. 
  The objective is to create a dictionary with terms related to statistical physics that will be utilized for identifying research papers. As a result it will synthesize i) existing usages of terms that are related to statistical physics in IS, ii) applications and procedures that are founded in concepts of stat. physics and iii) gaps in research where statistical physics could further enhance the understanding and effectiveness of IS practices, particularly in managing complex systems and big data environments.
  Scope:
  - Determine a dictionary comprising terms related to statistical physics
  - Conduct a systematic search of IS related academic databases via the dictionary.
  - Summarize and categorize findings based on application areas, benefits, and limitations.
  - Identify under-researched areas where IS uses terms of statistical physics without applying the numerical concepts

• MA: Evaluating Observables in Digital Transformation: A Meta-Analysis of Maturity and Transformation Models (contact: steven.goerlich[at]uni-kassel.de)
  Objective: This thesis will perform a meta-analysis of existing maturity- and transformation models that focus on managing digital transformations within organizations. The study aims to critically assess which observables or measurable criteria are used in these models and determine their impact on enhancing practical feasibility of transformation processes. By examining the justification, effectiveness and impact of these observables, the thesis seeks to determine which widely used, objective, measurable observables can be used to quantify and predict the success of digital transformation initiatives.
  Scope:
  - Collect and review existing literature on maturity models and transformation models related to digital transformation.
  - Analyse the models to extract and categorize the observables used.
  - Evaluate and structure these observables, e.g. by measurability and objectivity. 
  - Investigate the theoretical foundations and empirical validations of these observables.
  - Identify gaps in current models where statistical physics concepts could potentially enhance the understanding of digital transformation processes.
  - Propose a framework for selecting and applying the most effective observables in digital transformation initiatives.
  - Discuss the potential for developing new observables based on statistical physics principles that could offer unique insights into organizational change dynamics.
  - Explore the possibilities of creating a unified model that integrates the most robust and widely applicable observables from existing frameworks.
  - Provide recommendations for future research directions in developing more quantitative and predictive models for digital transformation.

• 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.