Available Topics

Information

The following topics are only sample topics that have already been completed. We are generally open to similar topics and questions. Please discuss the details with the supervisor.

Thesis advisor: Matthias Palmer, matthias.palmer@uni-goettingen.de

Main themes:

  • Data Analytics, esp. Topic Mining and Sentiment Analysis

  • Decision Support Systems, esp. in the context of capital markets


Example topic: Opportunities and Challenges Using Sentiment Analysis for Social Media

Sentiment analysis is an important tool in the analysis of social media content. By means of various methodological approaches, data structures of social media platforms can nowadays be individually taken into account. For example, dictionary-based approaches or support vector machines can be used. This thesis aims to develop future opportunities as well as challenges of the application of sentiment analysis from a company-oriented as well as from a theoretical perspective. In addition to presenting theoretical foundations on the subject of sentiment analysis and social media, a structured overview of the literature is intended to present the current state of research. On the other hand, a case study shall illustrate interesting aspects of the application of sentiment analysis in the field of social media.


Literature:

  • Ignatow, G., and Mihalcea, R. 2016. Text Mining: A Guidebook for the Social Sciences, Sage Publications.

  • Pang, B., and Lee, L. 2008. “Opinion Mining and Sentiment Analysis,” Foundations and Trends in Information Retrieval (2:1-2), pp. 1-135.

  • Levy, Y., and Ellis, T. J. 2006. “A systems approach to conduct an effective literature review in support of information systems research,” Informing Science: International Journal of an Emerging Transdiscipline (9:1), pp. 181-212.



Thesis advisor: Jan Röder, jan.roeder@uni-goettingen.de

Main themes:

  • Data Analytics


Example topic: Value of Data – Systematization, Methods and Determinants

As social and economic transactions become increasingly digital, large amounts of structured and unstructured data are generated. These increasingly map interactions between individuals and companies with increased granularity. An example of this is the exchange via social media. In principle, data has been used by companies for many decades.
One example is the use of Enterprise Resource Planning (ERP) systems to map key processes in the areas of capital, personnel or required resources. The storage of such data at different aggregation levels has also long been routine in many companies. In addition to these usually structured data, new forms of partly unstructured data (social media, sensor data) are increasingly being added, from which essential implications for operational processes and strategic initiatives can also be derived.
In what ways can data be used in a business context? What types of data are typically available? Which characteristics of the data determine their value? How can the value of data be determined with selected, already existing methodological approaches? In the course of this work, existing research gaps will be identified. After systematizing the main theoretical concepts, a structured literature review will be used to determine and discuss how the value of data can be determined.


Literature:

  • Akred, J. and Samani, A. 2017. “Your Data Is Worth More Than You Think,” MIT Sloan Management Review.

  • Davenport, T. H. 2006. “Competing on Analytics,” Harvard Business Review (84:1), pp. 1-10.



Thesis advisor: Albert Torno, albert.torno@uni-goettingen.de

Main theme:

  • Digital Transformation of the Finance Industry


Example topic: Blockchain – Hype or Revolution? – Potentials und Limitations of Blockchain-based Applications in the Finance Industry

Blockchain-based applications, for example the cryptocurrencies BitCoin or Ethereum, are being heavily reported on, i.a. because of their dramatic fluctuation of prices. But Blockchain technology can be used in many other finance industry contexts as well, e.g. in the processing of contracts, using so called “Smart contracts”. These possible applications are not yet applied in a wide range of actual financial services and therefore the question can be raised, whether the Blockchain technology is just a “hype” or a revolution within the finance industry domain. The goal of this thesis is to provide a systematic overview of the state-of-the-art research in the domain of blockchain technology. Subsequently, potentials and limitations of Blockchain applications within the finance industry should be identified, based on a literature review. To conclude the thesis a critical analysis of the current and future Blockchain usage in the finance industry context should be given.


Literature:

  • Risius, M. & Spohrer, K. (2017). "A Blockchain Research Framework - What We (don’t) Know, Where We Go from Here, and How We Will Get There," Business & Information Systems Engineering (59:6), pp. 385-409.

  • Nofer, M.; Gomber, P.; Hinz, O. & Schiereck, D. 2017. “Blockchain,” Business & Information Systems Engineering (59:3), pp. 183-187.

  • Webster, J. & Watson, R. T. 2002. “Analyzing the Past to Prepare for the Future: Writing a Literature Review,” MIS Quarterly (26:2), pp. xiii-xxiii.