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Application of Machine Learning in Management Research

The amount of available data has increased continuously in recent years of which academic research has also benefited to a great extent. Management research, for example, has been able to derive significant benefits in recent years from combining multiple data sources and tapping into new data sources (such as transcribed podcasts). In this way, it is possible to explore new variables and establish new relationships. Machine learning has made important contributions in the preparation and reduction of data sets. At the same time, the application areas of Machine Learning have steadily increased in recent years and its results have gotten better and better (e.g., in the area of text classification). On the other hand, there are still hurdles in using Machine Learning in academic research, for instance, regarding the  procedures of applying suitable algorithms.

For this reason, the research project aims to demonstrate the application of typical machine learning models (such as text classification, clustering, deep network representation learning, and graph analysis) in the context of established and new data sources and thus make them accessible. In this context, typical sources of error will be discovered and possible solutions identified.

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