Social Media in Business Context

In the business context, social media is used in different scenarios. On the one hand, companies can internally use their own social media-based platforms, called Enterprise Social Software Platforms (ESSPs), to improve communication and collaboration between employees. On the other hand, user-generated data from external social media, e.g., Twitter or Facebook, are systematically evaluated for business purposes.





Enterprise Social Software Platforms

Within organizations, ESSPs transform traditional communication structures by breaking down rigid, hierarchical reporting paths and enabling a flat, direct flow of information. This increases the transparency of work processes. Additionally, contacts for processes and projects can be identified and contacted more easily. Expertise can thus be used more efficiently in the company. ESSPs thus have a direct impact on the quality of communication and collaboration of knowledge workers. In addition to these general effects on the company as a whole, individual departments also profit from social media-based application systems, e.g., product development. Through more efficient communication mechanisms, ESSPs can be used to unlock the innovation potential of corporate employees, improve the quality of products and product development processes and simultaneously reduce the time spent on certain development activities. This allows, for example, to improve the quality of products by matching customer requirements with product characteristics.
In the context of Enterprise Social Software Platforms, the Chair of Application Systems and E-Business deals with the question of how appropriate platforms can be designed for enterprise-wide use, but also for specific application areas such as product development, and which framework conditions have to be considered therein from the perspective of IT and the departments.





Analysis of Social Media

In the company-external context, social media enables companies to spread their information in a timely manner through targeted external communication aided by the available external platforms and without an editorial detour via traditional mass media. However, this can result in new, little-known and therefore underestimated risks that arise from the continuous handling of public opinion, as well as the criticism of decisions, actions or measures. As a result, evolutionary adaptation processes have to take place within the company’s departments, so that e.g., changes in crisis management and marketing are able to control or avoid the hitherto unknown business-damaging processes in social media. Furthermore, social media provides a basis for finding information for different application domains (e.g., decision support in the financial industry). The tool portfolio used for such extraction methods is diversely designed and, for instance, uses the methods of web mining, social media monitoring and/or mood analysis.
The aim of this focus area is to explore user-generated content of external social media within the business and to extract information for investment decisions, business crisis analysis and business crisis prognosis.





Balance Sheet Analysis

In the field of AI-assisted balance sheet analysis, new possibilities are emerging through the use of modern methods. While multivariate statistical methods were primarily used in the past to calculate bankruptcy risks, today the focus is increasingly on the analysis of unstructured data. This development is made possible by increased computing capacity, allowing the analysis of unstructured data, such as text data from annual financial statements, and transforming it into relevant key figures.

An important approach is the supplementation of classical, quantitatively oriented balance sheet analysis with qualitative analyses of unstructured data. By using text mining methods, it is possible not only to identify correlations but also to capture causal relationships that provide insights into a company's financial situation. Although few specialized text mining approaches exist for this use case in current research, AI-based balance sheet analysis offers great potential. Practitioners receive valuable guidance on how to implement these methods and effectively integrate unstructured data into their models to create a more transparent and well-founded decision-making basis.






Currently Worked-on Questions

• Enterprise social software platforms as tools for company-internal knowledge work in industrial companies


• Integration of social media into product life cycle management


• Credit check using data from social media


• IT-based early detection of digital company-related social outrage waves in social media

Completed Research Questions

• Credit check using data from social media

Cooperation Partners

• Prof. Schumann GmbH


• Volkswagen AG

Publications

• Publication Filter (external)