Marketing & Innovation (Univ. Prof. Dr. Christina Schamp, Dr.rer.pol. Margeret Hall)
 

Digital technologies have profoundly changed the way and pace of companies interacting with their customers and innovating their products. For instance, today's marketers and researchers are faced with an enormous amount and variety of data that consumers share online - from product feedback via text messages and reviews, to images and videos posted by branded experiences on social media. At the same time, companies also strive to interact with their customers on various social media channels by producing compelling and relevant multimedia content for their target audiences, and to incorporate customer feedback and needs in their product development.

This course intends to provide the theoretical foundations behind state-of-the art marketing and innovation in the digital economy to address these challenges in digital management practice.

Algorithms and Behavioral Science (Dr. Melanie Clegg)

From ranking of news media and social media content to the communication via voice assistants and chat bots – algorithms are ubiquitous. Considering the huge potential of algorithms and artificial intelligence, business leaders and scholars need to attain a deeper understanding of chances and challenges of algorithms, as well as their multiple implications on consumers.

This course is designed to provide participants with a deeper understanding about the applications of AI and algorithms in consumer contexts and in behavioral research, as well as psychological mechanisms and ethical considerations that influence consumer experiences with AI and algorithms (e.g., experiences of data collection, classification, task delegation, decision making, social and communication).

Although we cover the theory of the technology of algorithms and AI (no coding required), the main focus is the application of algorithms and AI in consumer and marketing research and behavioral science more generally. The course will start with foundations and a definition of AI. It will then dive into different applications of AI in the consumer experience by analyzing and discussing cutting-edge research and practical examples. Based on this theoretical foundation and enriched by guest lectures, students will work on an own business problem or research question related to algorithms in marketing/consumer contexts and behavioral science.

Research Lab (Univ. Prof. Christina Schamp, Dr. Melanie Clegg, Dr. Sabrina Kirrane, Sabah Suahil, PhD. Univ. Prof. Davor Svetinovic)

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

Creativity and machine learning:

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.

Creativity and machine learning:

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.

Trust in Socio-Technical Systems: Blockchain and AI:

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.)