This course will delve deeper into questions of digitalisation within big and small enterprises from the perspectives of information systems, knowledge management, and digital ecosystems. In concrete projects and driven by concrete research questions within enterprises, we will touch on one or several of the following umbrella topics (which might be subject to extensions):

· Data and Knowledge Structuring in the Enterprise: While enterprises and holdings acquire and manage more and more data, the data assets often become less manageable through as data grows faster than its accumulation can be documented and structured. Many companies, therefore currently recognize Data Cataloguing and Data Governance Processes as a key requirement, in order to establish a function data ecosystem across departments and divisions.

· Digital products and interfaces in business ecosystems: Many business processes including interactions with clients and partners are carried out predominantly online, or at least leave a rather comprehensive digital trail. This poses new challenges for data management and storage – and new opportunities for innovative digital products and services.

· Digital Transformation of small and medium-sized enterprises: Digitalization provides unprecedented opportunities for small and medium-sized enterprises (SMEs). Although structurally flexible, many SMEs lack the organizational resources (e.g., capital, knowledge) necessary to leverage digital technology compared to large enterprises. Therefore, SMEs must not only build digital dynamic capabilities and establish corresponding business models and strategies, but also nurture a supportive corporate philosophy and organizational culture, which are prerequisites for a profound digital transformation of the company.

· Data-Ecosystems citizen participation in the public sector; Releasing public information (open data) can have various benefits such as promoting public innovation, increasing transparency and, in particular, advance public accountability and trustworthiness of the political-administrative system. Consequently, governments at different levels have released data in the last years. However, the extent to which public information is disclosed greatly varies. Next to quantity, however, the quality and the themes of open data are decisive for meliorating the citizen-government relationship. It is thus pertinent to understand which public information or datasets should be released by government entities to improve the citizen-government relationship, also from an ethical perspective.

The course will be divided into project teams working on one of these topics. The teams will meet regularly with their supervisors and assigned business partners who provide concrete real world use cases around digital transformation in the above topical areas, as well as periodically with the other groups to synergistically exchange their findings in mutual presentations of the major milestone results.

Upon completion of the course, students are able to

- Choose an appropriate set of research methods to tackle an applied research question in interaction with a “real customer”.

- Develop technical prototypes or socio-technical processes to address real-world problems faced by small, medium-sized or large enterprises in the digital economy.

- Evaluate different proposed prototypes against the background of the specific situation in the enterprise.

- Plan a research project to answer a developed research question.

- Perform a structured literature search on a given topic.

- Design an experiment, prototype, or empirical study for a specific research question.

- Identify and deploy appropriate analysis and evaluation methods and interpret their results.

- Write a research report according to current academic standards from the relevant disciplines describing the research project and its outcomes, including a perspective of how the research results could be implemented in the enterprise.

- Translate the specific project results into generalizable suggestions for other companies facing similar problems (e.g., design patterns)

The rules on the attendance of a Continuous Assessment Course (PI) apply. This means that attendance is mandatory and student performance is not exclusively assessed by means of a single final examination at the end of the course. It is rather based on the set of performance components outlined which you have to complete. For more information, see the dedicated page on the WU portal.

In this course, students bridge the ‘theory-practice gap’ by conducting an interdisciplinary practice-triggered and research-based project. Students go through all phases of the project from developing the research question, conducting a literature research, generating hypotheses, implementing experimental or empirical studies, analyzing experimental data or empirical data, to interpreting the results and critically interpreting them. In addition to methodological and technical knowledge, students gain practical experience in planning and implementing a research project in collaboration with practitioners. Students work together in groups and are regularly coached by the course instructors.

The final grade will be composed of:

1. Research project plan incl. tasks, responsibilities, milestones (20%)
2. Intermediate result report incl. update of research project plan, draft of research paper (20%)
3. Draft report (pass / no pass)
4. Final presentation (20%)
5. Final report incl. research paper, critical reflection on project plan and project work („lessons learned“), project result poster and 1-minute elevator pitch (40%)