Bachelor/Project Thesis

General Informations

The supervision of bachelor/project theses at our chair is based on a sequentially structured process that is repeated every six months. Deviations from the deadlines are not possible. The application period begins three weeks before the beginning of the lecture period of the respective semester and lasts two weeks. During the first week of lectures, the selection interviews take place, following which you will receive either an acceptance or a rejection.

Application process

If you want to write your thesis in the summer term 2021 at our chair, please note the application period: 22.03.2021 – 05.04.2021

  • Announcement of the topics expected as of 01.03.2021
  • Selection interviews: 12.04.2021 – 17.04.2021
  • Seminar for the Bachelor thesis: 21.04.2021 – 07.07.2021
  • Latest date for registration of the work: 14.07.2021

It is essential that you stay in regular contact with your supervisor in order to coordinate your work.

Seminar for Bachelor Thesis

Our chair offers a bachelor thesis seminar, which is especially aimed at students of business informatics and those interested in other courses of study who would like to write a bachelor thesis at the Schöller Endowed Chair for Business Informatics. The seminar is mandatory for students that write their bachelor thesis at our chair.


Course of study Conditions
International Business Studies attended in-depth module at the chair or application for exemption
International Production Engineering and Management attended module at the chair
Sozialökonomik attended in-depth module at the chair or application for exemption
Wirtschaftsinformatik none
Wirtschaftsingenieurwesen attended module B24, B25 or B26 at the chair
Wirtschaftswissenschaften mit Schwerpunkt Wirtschaftsinformatik none
Wirtschaftswissenschaften mit anderen Schwerpunkten attended in-depth module at the chair or application for exemption

Open Topics

If you are interested, please apply using the application form and attach your curriculum vitae, a current transcript of grades, and any graduation certificates you may already have. In the case of initiative applications, we also ask you to describe your intention briefly but meaningfully in the text of your e-mail.

Please send the complete application documents to

Thema Sprache Betreuer

Recommendation systems (so-called recommender systems) are used in numerous companies, as they effectively lead users to better and faster results and thus facilitate the decision-making process. Recommender systems can often be found in the field of e-commerce (e.g. Amazon or Zalando) or streaming services such as Netflix or Spotify.
A similar recommender system or AI-based consulting system can be developed to support students during their studies in selecting suitable courses. For example, individual course recommendations can be made and early predictions made based on student evaluations and identical preferences for courses. A further criterion for the selection of elective modules can be requirement profiles from job advertisements. In this way, it is possible to find courses for which a student might want to enrol but which he/she had not previously thought of or which are necessary for the design of a career.
Another scenario is the support and recommendation in selecting a course of study. The Friedrich-Alexander-University currently offers 261 courses of study, of which 77 are Bachelor, 92 Master and 92 State Examination courses. A recommendation system can be used to support prospective students based on their personal interests and academic performance in selecting a suitable one of a study program while at the same time reducing the number of dropouts or changes of study program.
The idea is to develop a recommendation system that enables students or prospective students to make better decisions when choosing their course or program.
To this end, recommendation/evaluation data and a suitable filter technique (e.g. content-based, collaborative or hybrid) will be identified and, based on this, a prototype will be modelled or alternatively questions of acceptance will be analysed.
GER + ENG Daniel Schömer

Algorithm aversion describes the phenomenon that individuals tend to reject recommendations based on complex algorithmic models. Although early research from the 1950s showed that even basic statistical algorithms (e.g., linear regression) outperform human experts in predicting suitable alternatives, individuals often hesitate or refuse to include AI-based recommendations in their decision making.
The aim of this thesis is to review the state of research on this refusing behavior by means of a systematic literature review.
GER + ENG Jessica Ochmann

The HR industry is faced with the challenging task of filling vacancies on the labour market with increasingly complex candidate profiles and highly competitive, scarce talent. Within the framework of the bachelor thesis, the requirements of the candidates as well as the individual acceptance of innovative technologies in personnel management (e.g. chatbots, data-driven recruiting) are to be examined on the basis of explorative interviews.
GER + ENG Jessica Ochmann

The increasing use of AI in organizations raises numerous ethical questions. How do we deal with them if AI is to take over decisions in private and professional everyday life? Does AI actually act more fairly than humans? What effects does the involvement of AI in decision-making processes have on humans? The aim of this paper is to review the state of research on ethical issues in the context of artificial intelligence by means of a systematic literature analysis.
GER + ENG Jessica Ochmann

Students focus on digital student advisory, which subsumes the use of artificial intelligence and data analytics regarding the support of students during their studies. The idea is to develop an early warning system that enable students and student advisor to identify challenges during the student lifecycle early on. For this purpose, use scenarios for different contexts known from one’s own study (for example, applying for a degree program, basic studies, advances studies) can be studied, a literature review can be conducted, concrete challenges can be solved by prototypes or questions of acceptance can be analyzed.
GER + ENG Prof. Sven Laumer, Daniel Schömer

Students focus on different questions regarding „digital learning“. For this purpose, use scenarios for different contexts known from one’s own study (for example, online tutorials, online tests, online exams, chatbots for Q&A) can be studied, a literature review can be conducted, concrete challenges can be solved by prototypes or questions of acceptance can be analyzed.
GER + ENG Prof. Sven Laumer

“Digital nudging is the use of user-interface design elements to guide people’s behavior in digital choice environments.” (Weimann et al. 2016). Students can focus on different questions regarding digital nudging in different contexts (learning, advisory, sports, health, etc.). For this purpose, a literature review can be conducted, concrete challenges can be solved by prototypes or questions of acceptance can be analyzed.
GER + ENG Prof. Sven Laumer

Artificial intelligence is currently on everyone’s lips. In many non-scientific formats and discussions, it is considered a revolutionary solution to a wide range of problems, including those in the healthcare, retail and manufacturing sectors. Behind it are often commercial interests, e.g. the desire to sell consulting services for the design and implementation of AI strategies and systems. But how is the topic of AI represented in science? The goal of this thesis is to systematically analyze and present the consideration of the topic AI in the leading scientific journals since 2010. In particular, the focus is on which topics the use of AI is primarily discussed and which AI methods and algorithms are primarily in the center of interest.
GER + ENG Quirin Demlehner

In most cases, the use of artificial intelligence (AI) requires enormous amounts of structured data. To obtain this data, a systematic and strategic approach is usually required, i.e. a data strategy. The goal of this thesis is to work out, based on the existing literature, what science already knows and has published so far about data strategy in relation to the processing of data with AI algorithms. The thesis will also discuss whether and how an AI-oriented data strategy differs from a conventional data strategy.
GER + ENG Quirin Demlehner

In their work, students focus on the question of how apps can be used for communication in research projects and with students. The goal is to develop a communication platform that supports app-based communication to specific target groups.
GER + ENG Prof. Sven Laumer

The goal is to explore how digital technologies can be used in change management using different methodological approaches. Thus, the focus is not on the digital transformation, but on the question of how this transformation can be digitally designed.
GER + ENG Prof. Sven Laumer

In their work, students focus on different questions in the field of “People Analytics”. For this purpose, different methodological approaches could be chosen.
GER + ENG Prof. Sven Laumer

Society increasingly relies on data-driven models for automated decision-making processes. The algorithms, behind these models, and the data they work with are often biased due to the nature of historical data and fuzziness in observations. As a result, practitioners and researchers are increasingly looking for ways to mitigate the biases in algorithms and data. This paper will focus on two different research questions, “What is discrimination and how can it be identified?”; “How can discrimination be measured and mitigated?”. The methodology to be chosen is systematic literature review.
GER + ENG Florian Meier

In this thesis, the data-driven support of Human Resources Management (HRM) in different areas will be analyzed. Students will investigate the question in which areas of HRM which methodological approaches are used for analysis. In addition to the technologies used, the data basis will also be considered.
GER + ENG Florian Meier

The use of artificial intelligence has gained considerable momentum in various fields, especially in the manufacturing industry. The aim of this paper is to identify different application areas of AI in manufacturing based on the existing literature and to show which problems have been solved or not solved in the process and which factors (e.g. costs, time, quality) could be improved.
GER + ENG Daniel Schömer

TDespite the enormous potential of artificial intelligence (AI), many companies are hesitant to use it productively. A fundamental problem here is understanding and trust in AI technology, which is hampered and inhibited primarily by increasingly complex machine learning models. The goal of this paper is to (1) analyze and present causes and factors that inhibit explainability, transparency, and lack of trust in these technologies. It also aims to (2) identify strategies/methods from the literature that can improve explainability and transparency and promote trust.
GER + ENG Daniel Schömer

Artificial intelligence is currently on everyone’s lips and holds enormous potential for new and improved products and services. The aim of this work is to analyze and present business models from the process industry and discrete industries with the help of AI and machine learning methods based on the existing literature. The focus here should be on distribution in the value chain, i.e., what new or improved products and services can be created and offered to end customers through the use of these technologies.
GER + ENG Daniel Schömer

While large parts of the web are dominated by large commercial groups, some platforms such as Wikipedia or OpenStreetMap, whose content was created without commercial interests and is freely accessible (“Open Data”), also enjoy great popularity. However, recent cases (e.g., show that problems with the data quality of Open Data can have intransparent effects on the behavior of end applications. Thus, the question arises which factors are significant for the use of Open Data platforms and which measures are taken to ensure the quality of the data and thus the trust of users in corresponding services. In the context of this bachelor thesis, answers to one of these questions are to be found by means of a systematic literature analysis and/or an empirical investigation.
GER + ENG Kian Schmalenbach

Data-driven applications such as recommender systems or decision support systems are becoming increasingly popular in both corporate and private applications. However, this can be problematic if the algorithms have learned their behavior by evaluating training datasets that contain discriminatory or otherwise misleading biases. This phenomenon is referred to in the literature as “algorithmic bias” and can have negative ethical or economic implications if, for example, personnel decisions are made based on such algorithms. Therefore, the goal of this thesis is to use a structured literature review and/or empirical data analysis to identify cases of algorithmic bias and its effects, or to develop and review measures to address problems related to algorithmic bias.
GER + ENG Kian Schmalenbach

In an age where digital media and platforms are responsible for much of the information gathering and processing, the question of the trustworthiness of these media and platforms is becoming increasingly important (“digital trust”). For example, the question arises as to how it can be that in the development of technologies for pandemic control, there is often a great deal of skepticism with regard to questions of data protection and thus trustworthiness, while commercial social media applications are often used rather unreflectively in everyday life. Based on such observations, it becomes clear that companies must also attach importance to the trustworthiness of their digital applications if they want to ensure a high level of acceptance and thus effectiveness of the applications. The aim of this work is therefore to use a structured literature analysis and/or empirical surveys to identify factors that are causal or hindering for the establishment of digital trust and by which measures these can be favored or hammered.
GER + ENG Kian Schmalenbach