This research lab will deal with contemporary trends around digitization. Specifically, groups of 5 students each will work on one of the following sub-topics:

(1) Creativity and machine learning (Schamp, Clegg, 2 groups):

This lab will delve deeper into questions of creative processes and innovation in the context of digitization, with a particular focus on the challenges and opportunities afforded by artificial intelligence, machine learning approaches and algorithms. Students will build upon and deepen their expertise in relation to the topics already covered in the ‘Marketing and Innovation’ course. Potential research project topics include, but are not limited to, creative co-work with artificial intelligence, adoption of creative outputs from algorithms, applications of artificial intelligence in the creative and innovation process, evaluation of creative outputs using artificial intelligence and machine learning approaches, stimulation of innovation by algorithmic systems. 

(2) Creativity and machine learning (Kirrane, 1 group):

This lab will delve deeper into questions of data privacy, security, and trust in the digital economy, with a particular focus on the challenges and opportunities afforded by distributed systems. Students will build upon and deepen their expertise in relation to the topics already covered in ‘Data Management and Analytics’, ‘Distributed Systems’, and ‘Security and Privacy’ courses. Potential research project topics include, but are not limited to, privacy enhancing technologies,  privacy-by-design, digital forensics, network & distributed systems security, information security management, trust in distributed data markets, and trust in distributed data analytics. 

(3) Trust in Socio-Technical Systems: Blockchain and AI (Svetinovic, Suhail, 1 group):

This lab will focus on deep analysis, design, or implementation of trust, security, and privacy mechanisms building upon the state-of-art technologies in blockchain, Artificial Intelligence (AI), Internet-of-Things, and Digital Twins. The required background are the topics covered in Data Management and Analytics, Distributed Systems, and Security and Privacy courses. In consultation with the instructor, the students will perform a standard set of research activities that further the knowledge in the chosen focus area (e.g., a data analytics study, new architecture design with simulation, new algorithm design and implementation, etc.)

Upon completion of the course, students are able to

- Critically evaluate a research question in the broad topic of Digital Economy from the view of (micro)economics and information systems
- Plan a research project to answer such a research question
- Perform a structured literature search on a given topic
- Design an experiment or empirical study for a specific research question
- Identify appropriate analysis methods
- Conduct appropriate statistical analyses for said data
- Interpret the results of said analyses and evaluate them critically
- Write a research paper according to current academic standards from the relevant disciplines describing the research project and its outcomes

The rules on the attendance of a Continuous Assessment Course (PI) apply. See the dedicated page on the WU portal for further information.

In the course, the students carry out an interdisciplinary research project from the question to the literature research, generation of hypotheses, implementation of experimental or empirical studies, analysis of experimental data or empirical data to the interpretation of the results and their critical reflection. In addition to methodological and technical knowledge, the students acquire practical experience in the planning and implementation of a research project from a project management perspective. The students work together in groups and are regularly coached together by the lecturers.

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%)

Grading scale:

90% to 100% Excellent (1)

80% to <90% Good (2)

70% to <80% Satisfactory (3)

60% to <70% Sufficient (4)

<60% Fail (5)

Course "Digital Markets and Strategies"