Digital Transformation (DT or DX) is a trendy topic and multi-level phenomenon not limited to its necessary component of digitization defined as the conversion of analogue to digital information. In its broadest definition, DT relates to the change associated with the application of digital technology in all aspects of human society. People envision smart cities and build smart factories (Industry 4.0) by introducing, for example, automation, the Internet of Things, robotics, 3D printing, cloud computing, big data analytics (datafication), and artificial intelligence (AI). How can firms successfully manage DT and benefit from a global paradigm shift towards open and shared innovation? The course of “DT” illuminates advanced topics of digital challenges within and between organizations. Students will learn about essential theories and concepts of open innovation to understand the context and “bigger picture” of DT in our society. Students will learn about the DT of various industries, unique characteristics of digital platforms, and digital infrastructures to understand how a firm must adapt its digital strategy to maintain competitive in a digital world. From a practical point of view, students will learn about and experiment with emerging digital tools (e.g., ChatGPT). The five interactive sessions combine elements of traditional lectures coupled with quizzes, group tasks, case studies, literature reviews, presentations, and in-depth discussions. By the end of this course, students will gain a deeper understanding of DT and its future challenges related to responsible and ethical AI for managerial decision making in today’s global economy characterized by volatility, uncertainty, complexity, and ambiguity (VUCA).

Students who have passed this course successfully are able to:

• understand the bigger picture of DT as a multi-level phenomenon,
• explain the basic mechanisms and underlying logics of most prominent digital technologies,
• discuss the implications of digital technologies for different industries,
• identify the antecedents and consequences of firm-level digitalization,
• understand the impact of DT on firm competitiveness and the future of work,
• understand novel work-related digital challenges (e.g., virtuality trap, technostress, black-boxing),
• apply emerging digital tools and critically assess their outcomes,
• conduct a thorough analysis of opportunities and threats related to firm-level digitalization.

In addition, students will improve their digital and social skills by working in international (virtual) teams and learning how to give constructive peer feedback. In doing so, students will experiment with digital tools and understand the limits of AI-enhanced decision-making.

Attendance in the first and the last session is mandatory. The last session is reserved for the final exam which will be based on a real-life or fictional case study analysis. You are only allowed to miss one of the remaining three sessions. If you miss more than one session, you will automatically fail the course. Please inform the instructor about any absence prior to the session.

The course builds on a variety of teaching methods including traditional lectures, literature reviews, in-class discussions, case studies, reading assignments, group assignments and presentations of group work. The lecture supports self-learning and team learning objectives. Students will acquire theoretical know-what related to the subject of digital transformation, as well as practical know-how related to group presentations in collaborative-competitive scenarios. The course provides an up-to-date literature review of relevant journal articles and does not closely follow any specific textbook on digital transformation. The two referenced textbooks below provide a good introduction and supplementary material. In addition, all referenced course materials will be accessible for revision of individual sessions and in preparation of the final exam. The use of AI-based software for text generation, creation of presentations, and writing support (e.g., ChatGPT, Bard, SlidesGPT or Grammarly) is explicitly allowed but must be indicated.

Individual performance: ∑ 50%

• In-class participation: 20%
• Peer evaluation: 10%
• Three multiple-choice mini-quizzes (only your best two account for 10% each): 20%

Group assignments: ∑ 20%

• Two group tasks à 10%

Final case study analysis: ∑ 30%

Overall grading:

There are no specific pre-requirements apart from a first-come-first-serve course registration.

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