Institute for Business Analytics

Finalisation work

Open theses at the Institute for Business Analytics

Bachelor's theses (BA) and master's theses (MA) in cooperation with the Institute for Business Analytics are different from what you might expect. We need "thinkers" and "doers", and you will be given the opportunity to apply the knowledge you have learnt and tried and tested techniques in a targeted manner (sometimes directly in companies). An IBA employee will provide you with technical and methodological support.

Chair of Digital Business – Prof. Dr Steffen Zimmermann

Digital platforms have established themselves as the dominant business model of the modern economy by efficiently matching supply and demand and leveraging network effects. This topic area examines the mechanisms and evolution of these ecosystems, which are increasingly being revolutionised by artificial intelligence. The focus is on both the algorithmic control of interactions (e.g. through generative AI) and the strategic implications for market participants, from the disintermediation of entire industries to new forms of collaboration.

Possible topics

Generative AI as a game changer: Analysis of new use cases and changed value creation potential on digital B2B or B2C platforms through the use of GenAI. (BA & MA; contact: Leonie Embacher, Jonathan Ibele)
#GenAI, #literature-based

From assistant to autonomous actor: The potential of ‘agentic AI’ in the platform economy – when AI agents independently take over transactions and negotiations. (BA & MA; contact: Leonie Embacher, Jonathan Ibele)
#AgenticAI, #Literature-based

Real vs fake: How do we recognise AI-generated content and how should digital platforms deal with it? (BA & MA; contact: Christopher Tille)
#FakeContent, #MachineLearning, #Coding

GenAI-based summarisation of customer reviews (BA & MA; contact: Kilian Züllig)
#CustomerReviews, #GenAI, #Experiment

The role of collaborative reviewing in coping with the flood of information. (BA & MA; Kilian Züllig)
#CustomerReviews, #Analytics, #Coding

Power and market structure: Investigation of disintermediation effects (BA & MA; contact: Jana Ruß)
#MarketPower #Economics, #Literature-based

Structural constraints and dependencies for suppliers and consumers on dominant platforms. (BA & MA; contact: Jana Ruß) 
#Market power, #Economics, #Literature-based

The content and methodology of the topics are individually agreed and specified with the students. The requirements for the topics vary depending on the degree (Bachelor's/Master's).

The Recommender Systems topic area deals with the development and use of AI-supported recommendation systems that assist users in selecting products, information or services. By analysing large amounts of data and recognising individual preference patterns, recommender systems can generate personalised suggestions that simplify decision-making processes and reduce information overload. The focus is on the interplay between algorithmic predictive power and human judgement: while AI calculates recommendations based on past interactions, users retain control over selection, evaluation and final decision-making.

Possible topics

How Spotify & Co generate recommendations – implementation and evaluation of a recommender system (BA/MA; contact: Leonie Embacher)
#Analytics, #Programming

Trust in AI-supported recommendation systems – How does explainability affect user acceptance? (BA/MA; contact: Leonie Embacher)
#Explainability, #AI, #Experiment

Explainability of review-based recommender systems – A systematic literature review (BA/MA; contact: Leonie Embacher)
#Explainability, #Literature-based

Human judgement and algorithmic recommendations – The influence of recommender systems on human decision-making processes (BA/MA; contact: Leonie Embacher)
#Decision quality, #Literature-based

Recommender systems in (social) media – Approaches to reducing filter bubbles and echo chambers (BA/MA; contact: Kilian Züllig)
#Filter bubbles, #Literature-based, #Analytics

The topics will be individually coordinated and specified with the students in terms of content and methodology. The requirements for the topics vary depending on the degree (Bachelor's/Master's).

In an increasingly digitalised world, people are spending more and more time in digital environments and making decisions there. Digital nudging involves the strategic use of design elements to encourage users in digital decision-making environments, such as digital platforms, to make decisions through small prompts (known as nudges) – without coercion or prohibitions.

Possible topics

Digital green nudging: An analysis of the effectiveness of digital nudges in promoting ecological behaviour. (BA & MA; contact: Jana Ruß, Christopher Tille)
#Sustainability, #Experiment or #Literature-based

Identification of spillover effects of digital nudges on other decisions (e.g. different context, different time, etc.) (BA & MA; Contact: Jana Ruß, Christopher Tille) 
#Sustainability, #Experiment or #Literature-based

Personalised digital nudges and their potential. (BA & MA; Contact:Jana Ruß, Christopher Tille)
#Individualisation, #Analytics, #Coding or #Experiment

Context-aware nudging: Development of situational digital nudging in real time. (BA & MA; Contact: Jana Ruß, Christopher Tille)
#Indivisualisation, #Analytics #Programming or #Literature-based

The topics are individually coordinated and specified with the students in terms of content and methodology. The requirements for the topics vary depending on the degree (Bachelor's/Master's).

Modern professional sport has evolved from purely gut-based decisions to data-driven decision making. The use of modern tracking technologies generates huge amounts of data that harbour untapped potential. Sports analytics deals with the systematic analysis of this data in order to identify competitive advantages. The aim is to use advanced statistical methods and machine learning to gain objective insights for tactics, player recruitment, load management and economic decisions.

Possible topics

Development of a 360-degree player performance dashboard Design and implementation. (BA & MA; contact: Kilian Züllig)
#Basketball, #Application-oriented, #Analytics, #Coding

Beyond goals & assists: Evaluating the quality of individual player actions in Bundesliga football using advanced methods. (BA & MA; contact: Kilian Züllig)
#Football, #Literature-based, #Analytics, #Coding

Injury prevention through machine learning: Analysis of stress data (GPS, heart rate) to predict injury risks in competitive sports. (BA & MA; Contact: Kilian Züllig) 
#Team sports, #Analytics, #Coding or #Literature-based

Tactical pattern recognition: Use of position data for automated recognition of plays and formations in team sports. (BA & MA; contact: Kilian Züllig)
#Team sports, #analytics, #coding or #literature-based

The content and methodology of the topics are individually agreed upon and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's).

The Societal AI research area explores how artificial intelligence can help overcome societal challenges. In a societal context, the focus is on ethical, fair and transparent AI applications that strengthen social inclusion, responsible decision-making support and societal benefits.

Possible topics

How does good collaboration between humans and GenAI work: What are the consequences of blind trust in GenAI for society? (BA & MA; contact: Christopher Tille, Kirsten Pitz)
#GenAI, #trust, #literature-based, #experiment

What influence does GenAI have on the education system? Which skills are becoming increasingly important with the comprehensive integration of GenAI into our everyday lives? (BA & MA; contact: Christopher Tille, Kirsten Pitz)
#GenAI, #Education, #Literature-based

Disinformation with GenAI – What are the effects on political opinion formation? How is the internet changing due to the flood of AI-generated content? (BA & MA; contact: Christopher Tille, Kirsten Pitz)
#GenAI, #PoliticalOpinionFormation, #Literature-based

How should AI-supported educational programmes be designed to effectively promote democracy and prevent extremism? (BA & MA; Contact: Jana Ruß, Kirsten Pitz)
#GenAILiteracy, #Democracy, #Literature-based

Ethical tensions of AI in a social context (e.g. greenwashing, extremism, transparency, etc.) (BA & MA; contact: Christopher Tille, Kilian Züllig)
#Ethics, #Literature-based

Social recommendation – opportunities and risks of recommender systems in social networks (BA & MA; contact: Jana Ruß, Kilian Züllig)
#RecommenderSystems, #SocialMedia, #Literature-based

The content and methodology of the topics are individually agreed and specified with the students. The requirements for the topics vary depending on the degree (Bachelor's/Master's). 

More and more people are managing their own wealth accumulation. However, despite increased interest, young investors in particular often lack the necessary financial education to achieve sustainable wealth accumulation. This trend is reflected in social media: so-called finfluencers now reach thousands of people with their content. On the one hand, they draw attention to financial topics, but on the other hand, they also harbour risks: they often oversimplify risks or provide insufficient information due to conflicts of interest. As the phenomenon of finfluencers is still relatively new, it is important to understand and quantify the potential opportunities and risks.

Possible topics

How do videos by finfluencers affect consumers' financial education? (BA & MA; contact: Jonathan Ibele)
#Education, #SocialMedia, #Experiment

Do viewers recognise manipulative elements (financial signals, social pressure) in videos by finfluencers? (BA & MA; contact: Jonathan Ibele)
#Manipulation, #SocialMedia, #Experiment

What effect does clear labelling of advertising have on the credibility of finfluencers' messages? (BA & MA; contact: Jonathan Ibele)
#Marketing, #SocialMedia, #Experiment

How do finfluencers deal with the risks of financial products in their videos? (BA & MA; contact:Jonathan Ibele)
#Risks, #GenAI, #Analytics, #Coding

How can videos by finfluencers be automatically fact-checked? (BA & MA; contact: Jonathan Ibele)
#FactCheck, #GenAI, #Coding

How can promotional content be identified in videos by finfluencers, and to what extent do the characteristics of self-promotion and third-party advertising differ? (BA & MA; contact: Jonathan Ibele) 
#Marketing, #GenAI, #Coding

The topics will be individually coordinated and specified with the students in terms of content and methodology. The requirements for the topics vary depending on the degree (bachelor's/master's). 

The Green AI topic area explores how artificial intelligence can help overcome environmental challenges. In an environmental context, this involves optimising energy and resource use, preventing environmental disasters, reducing greenhouse gas emissions and promoting sustainable processes.

Possible topics

AI systems for reducing emissions or electricity consumption in the global supply chain, businesses, and residential buildings (BA & MA; contact: Christopher Tille, Kilian Züllig)
#ElectricityConsumption, #MachineLearning, #Coding, or #LiteratureBased

Use of AI to detect natural disasters and environmentally harmful activities (e.g. using satellite images) (BA & MA; contact: Christopher Tille, Kilian Züllig)
#NaturalDisasters, #MachineLearning, #Coding

AI-supported sustainability reporting to improve preparation and auditing (BA & MA; Contact: Christopher Tille, Kilian Züllig)
#Sustainability reporting, #Analytics, #Coding or #Literature-based

Ethical tensions of AI in an ecological context (e.g. greenwashing, transparency, etc.) (BA & MA; Contact: Christopher Tille, Kilian Züllig)
#Ethics, #Literature-based

Energy communities and tenant electricity. The role of AI in decentralised energy supply. (BA & MA; contact: Kilian Züllig) 
#P2P electricity trading, #machine learning, #coding or #literature-based

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

If you are interested in writing your thesis, please contact
directly to discuss the next steps. 
Procedure for writing a thesis at the Professorship of Digital Business (pdf)

Péter Horváth Endowed Chair for Business Information Management – Prof. Mathias Klier

Brief description:

A recent study by the Bertelsmann Foundation shows that more than three-quarters of respondents in Germany reject fully automated decisions, while only 31 percent of the population see opportunities in them. On the other hand, intelligent systems are already being used successfully in many areas of everyday life (e.g. in medical diagnostics or creditworthiness assessments) and are becoming increasingly important in practice. It is therefore essential, especially for companies that use intelligent systems, to increase their acceptance among their customers and consumers. This does not primarily require ‘algorithm transparency,’ as has been discussed and demanded by politicians for some time. Rather, the solution lies in explanations that are understandable to laypeople, making the decisions made by artificial intelligence in individual cases comprehensible and transparent.

Possible topics:

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Brief description:

In the course of digitalisation, organisations today have access to very large and ever-growing amounts of data (keyword: ‘big data’). However, empirical evidence shows that the data analysed and used is often characterised by poor data quality – even in internal company customer databases, an average of approximately 30% of the stored data values are incorrect. This results in additional costs of $15 million per year for an average American company, for example. However, poor data quality is not only a major problem for companies – in times of ‘fake news’, the need for reliable information is also increasing in politics and society. This is why quantitative methods for measuring, controlling and improving data quality are needed.

Data quality is also becoming increasingly important in the context of generative systems. In particular, retrieval-augmented generation (RAG) systems, such as those underlying ChatGPT, place high demands on the quality of the underlying data. Data quality defects can arise both in the document base and in the retrieval process, for example due to incomplete documents or imprecise user queries, and have a direct impact on the quality of the generated responses. There is therefore a growing need for methods to detect and address data quality issues in RAG systems.

Possible topics:

  • Detection of data quality defects in retrieval-augmented generation systems (BA/MA; contacts: Mike Rothenhäusler)
  • Strategies for resolving data quality defects in retrieval-augmented generation systems (BA/MA; contacts: Mike Rothenhäusler)
  • What isn't measured isn't managed: Development of data quality metrics (BA/MA; contacts: Andreas Obermeier)
  • Data quality in IoT data: Overview & metrics of individual data quality dimensions (BA/MA; Contacts: Andreas Obermeier)
  • Data quality in wikis & unstructured data: Overview & metrics of individual data quality dimensions (BA/MA; Contacts: Andreas Obermeier)
  • Use of text mining to determine data quality defects in wikis (MA; contact: Andreas Obermeier)
  • Influence of data quality defects on the performance of artificial intelligence (BA/MA; contacts: Andreas Obermeier)
  • Uncertain machine learning: Incorporating uncertainty into artificial intelligence methods (BA/MA; contacts: Andreas Obermeier)
  • Data quality and its effect on AI-supported systems (BA/MA; contacts: Hannah Knehr; Anna-Lena Kubilus)
  • Data timeliness as a key factor in data-driven decision-making (BA/MA; contacts: Hannah Knehr; Anna-Lena Kubilus)

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Brief description:

Information systems, and social media in particular (e.g. online social networks, wikis, rating and review communities, and discussion forums), have become an integral part of our society. They enable parts of the value chain, such as product development, sales, branding, and service, to be distributed and digitised in collaboration with market participants. Social media can also make a valuable contribution within companies – for example, through improved information and knowledge exchange in enterprise social networks. In this context, the question of quantifying the network effects that occur in social media is of great importance in both science and practice. In addition, social media and the internet provide companies with enormous amounts of data in structured (e.g. relationships between network actors) or unstructured form (e.g. text content of tweets). The targeted and well-founded analysis of this data using automated methods from the fields of social network analysis and text mining enables companies to improve their decision-making support and offers great potential, for example in customer relationship management.

Possible topics:

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Brief description:

Nowadays, companies have access to very large and ever-growing amounts of data – for example, via social media and the internet (e.g. online social networks, wikis, rating and review communities, discussion forums), but also in traditional databases (e.g. data warehouses, customer databases) and in direct customer contact (e.g. humanoid robots such as Pepper) – have access to very extensive and ever-growing amounts of data. The vast majority of the available data is stored in unstructured form (images, videos, texts). Methods for automated analysis are needed to leverage this wealth of data. In the field of big data analytics & AI, concrete applications and the resulting benefits of artificial intelligence methods in the analysis of (un)structured data are being researched.

Possible topics:

  • Agentic AI – How can AI agents be designed to specifically qualify users in task completion? (BA/MA; contacts: Chiara Schwenke)
  • Analytics and AI in the production hall of the manufacturing industry (BA/MA; contacts: Patrick Bedué, Maximilian Förster)
  • ML in sport – Is it possible to predict the results of sporting events using machine learning (BA/MA; contacts: Chiara Schwenke)
  • Telematics data from vehicles: driver/driving manoeuvre recognition (BA/MA; contact: Andreas Obermeier)
  • Aspect-based sentiment analysis – explanation of ratings in customer reviews (MA; contact: Andreas Obermeier)
  • Improved classification of imbalanced data (BA/MA; contact: Andreas Obermeier)
  • Information extraction from image data (MA; contact: Andreas Obermeier)
  • Development of data-driven decision-making (MA; contact: Andreas Obermeier)
  • Hybrid intelligence (human + GenAI) using the example of knowledge networks (BA/MA; contact: Maximilian Förster and Andreas Obermeier)
  • Smart energy markets: AI for designing decentralised energy markets of the future (BA/MA; contact: Maximilian Buck)
  • Human-in-the-loop as an approach to improving AI-supported systems (BA/MA; contact: Hannah Knehr, Maximilian Buck)

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Brief description:

Traditional sales processes often reach their limits in terms of data availability and analysis and processing capabilities. In brick-and-mortar retail, employees are generally unable to memorise all product and customer details. Even if this information were available to service experts (e.g. via a traditional IT system), the large amount of relevant data and its complexity would make manual processing virtually impossible or subject to subjective personal bias. Recommender systems make it possible to recommend products to users objectively on the basis of large amounts of data. Robots such as Pepper have the ability to perceive customers in the physical world, interact with them and also have the computing power to process the data obtained in this way more objectively, quickly and accurately than humans.

Possible topics:

  • Use of the humanoid robot Pepper in customer service (BA/MA; contact: Andreas Obermeier)
  • Use of the humanoid robot Pepper in a hospital environment (BA/MA; contact: Andreas Obermeier)
  • Explainable & Conversational Recommender Systems – Overview (BA; contact: Andreas Obermeier)
  • Customer-oriented evaluation of Explainable & Conversational Recommender Systems (BA/MA; contact: Andreas Obermeier)
  • Natural Language Generation – Systems for automating customer service (BA/MA; contact: Andreas Obermeier)

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Brief description:

The world of work is facing major changes. Rapid technological development and social upheaval require answers to the question of how the future world of work in Baden-Württemberg can be shaped. Digitalisation, automation and artificial intelligence are considered key drivers of future economic growth and will have a significant impact on the world of work. In addition, the transformation of society towards climate neutrality is bringing about far-reaching changes, including a significant reduction in greenhouse gas emissions, the shift to a resource-efficient circular economy and the implementation of the energy transition. In coping with these changes, one thing is particularly important: people who shape economic and social progress. That is why we must now build the skills needed to shape a positive future for all, known as future skills.

Possible topics:

  • Extraction of skills from job advertisements (BA/MA; contact: Maximilian Förster)
  • Creation of future skills lists from job advertisements using machine learning methods (MA; contact: Maximilian Förster)
  • Automated recognition of emerging skills using machine learning methods (BA/MA; contact: Maximilian Förster)
  • Time series analyses of future skills (BA/MA; contact: Maximilian Förster)

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Brief description:

The ability to process large amounts of data from various sources (big data) and the use of artificial intelligence can help companies fundamentally improve their methods for recruiting, developing and retaining employees. Despite the availability of advanced analytical methods, however, HR decisions in many companies are still based on gut feeling and intuition. This is where people analytics comes in: as a central element of future-proof HR strategies, people analytics enables an evidence-based approach to human resources, using technologies to analyse data on current, former and future employees in all areas of HR with the aim of improving human resources and overall company performance.

Possible topics:

  • Innovative applications of people analytics: case studies and best practices (BA/MA; contact: Chiara Schwenke)
  • Artificial intelligence in the world of work: shaping change (BA/MA; contact: Chiara Schwenke)
  • Quality of GenAI use in the workplace (BA/MA; contact: Chiara Schwenke)
  • Empirical investigation of the influence of people analytics on business problems and organisational performance (BA/MA; contact: Chiara Schwenke)
  • AI ethics in people analytics (BA/MA; contact: Chiara Schwenke)
  • Organisational prerequisites for the successful implementation of people analytics (BA/MA; contact: Chiara Schwenke)

The content and methodology of the topics will be individually agreed and specified with the students. The requirements for the topics vary depending on the degree (bachelor's/master's). 

Procedure for a thesis at the Professorship of Business Information Management (pdf)

Chair of Value Creation and Network Management – Prof. Dr Mischa Seiter

Background: Private natural history collections play an important role in expanding and completing museum holdings. However, evaluating them prior to a possible purchase poses considerable challenges for museums: the material, scientific and cultural value of such collections is often difficult to quantify. While simplified procedures are often used in practice, these cannot adequately reflect the actual scientific and historical value of the collection. At the same time, such collections offer considerable opportunities: time and again, collections acquired at comparatively low cost are later found to contain valuable objects, some of which are of scientific significance. A deeper understanding of the valuation processes, their criteria and decision-making logic is therefore important not only for museums, but also for cultural policy and scientific institutions. The case study of a natural history museum offers a practical starting point for systematically examining and further developing valuation mechanisms.

Possible focal points:

  • Analysis of existing valuation practices for natural history collections (e.g. market mechanisms, valuation guidelines, museum valuation processes)
  • Case study-based investigation of selected acquisition processes in natural history museums
  • Development of a criteria or valuation framework that integrates material, scientific and cultural values
  • Reflection on the opportunities and risks of different valuation approaches, particularly with regard to transparency, sustainability and scientific benefit


The specific design (e.g. qualitative interviews, document analysis, framework development) will be determined in consultation with the supervisor.

Background: Information avoidance describes the deliberate avoidance of potentially important information. Despite its growing relevance – for example in the areas of sustainability, healthcare and finance – there is no structured overview of the current state of research.

Research question: What theoretical and empirical findings exist on information avoidance, and what implications does this have for companies?

Objectives of the thesis:

  1. Systematic literature review and categorisation of existing literature (e.g. psychological vs. economic perspectives).
  2. Identification of research gaps and potential solutions.

Background: Agentic AI describes AI systems with a high degree of autonomy and decision-making ability. These systems can prepare or autonomously implement strategic decisions, but their role in organisations has not yet been extensively researched. Increasing autonomy can give rise to new risks and challenges – from liability issues and loss of control to ethical dilemmas. Agentic AI can be particularly problematic for companies with limited AI expertise.

Possible focus areas:

  • Systematic literature review on agentic AI and its fields of application, as well as the development of an application framework.
  • Analysis of current research on the risks and challenges posed by agentic AI, as well as the derivation of best practices for dealing with it.

The specific details will then be developed in consultation with the supervisor.

Research question: What are the (international) best practices for designing hybrid working models?

Background:

‘Yes, Zoom Has an Office. No, It's Not a Place to Work.’ (Cohen, 2022)
As a result of the Covid-19 pandemic, hybrid working models are becoming established in the long term, with employees only working on site part of the time. Hybrid working offers opportunities for employees (e.g. flexibility) and companies (e.g. reduced space requirements), but also poses risks to team cohesion, individual and collective creativity, and corporate culture. This raises the question of how successful hybrid working models are designed in practice.

Objectives of the work:

  1. Conduct research to identify (international) best practices in the design of hybrid working models (e.g. office design, corporate culture, attendance rules, etc.).
  2. Present hybrid working models of individual companies in the form of case studies.

Introductory literature:

Background: Economic turbulence has increased massively in recent years. In addition to climate change, the Covid pandemic and geopolitical shocks such as Russia's war in Ukraine, a long-term decoupling of the global economy can be observed. As a highly networked, export-oriented economy, German industry in particular faces a multitude of challenges in various areas.

Possible areas of focus:

  • Developing a method for analysing different stress factors and establishing a cause-and-effect model for assessing the impact of crises on SMEs
  • Defining important target and control variables for companies under stress and establishing a KPI driver tree to identify early warning indicators
  • Identification, analysis and preparation of freely accessible data sources for the early detection of external disruptive factors in corporate networks
  • Evaluation and development of incentive systems to increase crisis resilience

The specific details will then be developed in consultation with the supervisor.

Introductory literature:

  • Lamorgese, A., Patnaik, M., Linarello, A., & Schivardi, F. (2024). Management practices and resilience to shocks: Evidence from COVID-19. Management Science, 70(12), 9058-9072.
  • Hayne, C. (2022). The effect of discontinuous and unpredictable environmental change on management accounting during organizational crisis: A field study. Contemporary Accounting Research, 39(3), 1758-1796.
  • Hyun, J. H., Matejka, M., Oh, P., & Ahn, T. S. (2022). Performance targets and ex post incentive plan adjustments. Contemporary Accounting Research, 39(2), 863-892.

Research question: How can a web-based reporting tool for managing sustainability strategies be designed?

Background:

Through the consistent implementation of sustainability strategies, small and medium-sized enterprises (SMEs) can strengthen their environmental and social responsibility and realise long-term competitive advantages. Increasing pressure from stakeholders and regulatory requirements mean that sustainability strategies must be made increasingly transparent and systematically managed. Effective implementation requires an integrated, resource-efficient (in)formal management system. For SME-friendly implementation, a web-based and user-friendly reporting tool can contribute significantly to the management of these strategies.

Objectives of the work:

  1. Modelling a mock-up for a web-based reporting tool based on an existing management system for sustainability strategies.
  2. Validating the mock-up in expert interviews.

Introductory literature:

  • Benyon, D. (2019): Designing User Experience. A guide to HCI, UX and interaction design. Pearson.
  • Garrett, J. J. (2011): The Elements of User Experience. New Riders.
  • Cheng, M., et al (2023): Sustainability and Management Accounting Research. Journal of Management Accounting Research 35(3), 1–11.
  • Derchi, G., et al. (2023): Green incentives for environmental goals. Management Accounting Research 59, 100830.

Background: The ratcheting effect describes the tendency for achieved goals to be set as new targets in future periods – a phenomenon that has implications for both motivation and behavioural economics. Despite its relevance in the context of performance evaluation and incentive design, there is a lack of systematic analysis of the current state of research on this effect.

Research question: What theoretical and empirical findings exist on the ratcheting effect in the context of goal setting and performance management?

Objectives of the study:

  • Systematic literature review and categorisation of existing studies on the ratcheting effect.
  • Analysis of key influencing factors (e.g. incentive systems, information asymmetries, employee motivation).
  • Derivation of implications for goal setting, planning and control in companies.

Introductory literature:

  • Aranda, C., Arellano, J., & Davila, A. (2014). Ratcheting and the role of relative target setting. The Accounting Review, 89(4), 1197–1226. https://doi.org/10.2308/accr-50733
  • Charness, G., Kuhn, P., & Villeval, M. C. (2011). Competition and the ratchet effect. Journal of Labor Economics, 29(3), 513–547. https://doi.org/10.1086/659347
  • Matějka, M., Mahlendorf, M. D., & Schäffer, U. (2022). The ratchet effect: Theory and empirical evidence. Management Science, 70(1), 128–142. https://doi.org/10.1287/mnsc.2022.4641

Background: Open data is becoming increasingly important in business and research. In the field of forecasting, freely available data sets from areas such as economics, weather, mobility and social media offer great potential for improving forecasts and developing new models. At the same time, there is currently little transparency about which open data sources exist, what data quality they offer and for which forecasting purposes they are suitable.

Research question: How can open data sources be systematically identified and classified according to relevance, quality and possible uses in forecasting?

Objectives of the work:

  • Systematic research and recording of relevant open data sources related to forecasting applications
  • Development of a classification scheme (e.g. according to data type, timeliness, accessibility, relevance to forecasting)
  • Analysis of the strengths, weaknesses and potential uses of various data sources for forecasting models

Einstiegsliteratur:

  • Kugler, P., Vogt, H., Meierhofer, J., Dobler, M., Strittmatter, M., Treiterer, M., & Schick, S. (2024). Daten im B2B-Ökosystem teilen und nutzen: Wie KMU Voraussetzungen schaffen und Hürden überwinden. In Digitale Plattformen und Ökosysteme im B2B-Bereich (S. 209–240). https://doi.org/10.1007/978-3-658-43130-3_8
  • Petropoulos, F., Apiletti, D., Assimakopoulos, V., Babai, M. Z., Barrow, D. K., Ben Taieb, S., et al. (2022). Forecasting: Theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001

If you are interested in writing a thesis, please contact Natalie Rupp directly at
with your preferred topic from the list above or your own topic suggestion to discuss the next steps. We are also happy to supervise theses in cooperation with companies.