The goal of this course is to prepare management students for the particular challenges and opportunities of digital transformation in organizations. Concretely, this course will prepare our students –as future managers– to build bridges between on the one hand the technical elements that relate to digital transformation and on the other hand the processes that are at the core of organizational value creation. In other words, students learn to disentangle the managerial trade-offs for value-adding processes as a result of new technological possibilities, but also as a result of new technological concerns.

The content of this course is structured around a set of topics that are currently dominating the debate on digital transformation. These topics include artificial intelligence, big data, Internet of Things, data protection and security, technologies for hybrid working, automation and robotization, Open Access/Open Data/ Open Knowledge, customer data platforms, etc. In doing so, students will in the first place remain up to date on current evolutions in the area of digital transformation. However, these topics will be discussed as cases and application areas for elaborating the more fundamental and timeless trade-off that organizations face when integrating new technologies in their daily operations. Concretely, we will use these application areas to understand the fundamental managerial dynamics that are inherent for the adoption of new technologies in organizations. In doing so, students will learn about the actual management challenges that tend to remain in organizations, also after the technology buzzwords will have changed in a few years/decades from now. Hence, students will develop a more profound insight in managerial functions that relate to business process management, project management, dealing with ethical dilemmas, lateral organizational functions, etc.

• Knowledge of Management and Digital Transformation acquired through reflection on social processes in the course;
• Apply this knowledge on practical cases.
• Complete work assignments while meeting deadlines;
• Participate in solving case studies in small groups;
• Identify the essential core of a concept as well as a case study and present it to a larger group using appropriate means;
• Apply the acquired knowledge directly through research and the implementation of small exploratory projects.

Examination-immanent courses (PI-LV) are courses with a high interactive part. Active participation is thus a requirement, and highly valuable for the students’ learning processes. The only valid reason for not being in class is for severe reasons ((illness, accident, or death of a close relative), and a formal confirmation of the absence (e.g. medical certificate) must be submitted. Moreover, for a positive completion of the course, 80% attendance is required. Should there be an absence from a course due to an important reason, a maximum of 20% of the total course duration can be missed. In case of cumulative absences of more than 20%, the course must be repeated.

The teaching/learning design focuses on learning theoretical concepts and models and the ability to use these concepts and models to make informed management decisions. To achieve this, the following teaching/learning methods are used:

• Preparation: students prepare themselves for the respective course units, e.g. by reading cases or examples.
• Self-study: Students read and study reading materials (Uploaded on Canvas) that provide the necessary foundations for the content of this course.
• Interactive learning: In the course units, we deal with business cases, examples, learning games, and we discuss –guided by the course instructor– theoretical insights for these practical cases.
• Self-reflection: the students have to reflect on the most important learning from the course material as well as from the interactive elements in the course units.

The assessment of this course consists of three main parts:

EXAM (individual assignment; 50% of the total assessment): Students in this course will be given a standardized exam (based on a selection of content of the course: slides and content of lectures, students’ course notes, reading materials, practitioner cases). For the individual Exam: Students can NOT rely on artificial intelligence (AI) (e.g., ChatGPT) to generate or fine-tune answers. All exams will be standardly tested for plagiarism and for use of AI. When suspicion exists that AI has been used for the individual exam, students will have to do an additional oral examination that replaces the written take-home exam. (Only) for the exam, no formal references/citations are needed to course materials and content of lectures. However, when extra resources are used (e.g. newspaper articles, or other scientific articles), these need to be correctly and consistently cited and references.

CASE 1 and 2:

CASE 1 (Group work; 25% of the total assessment): Groups of up to five students, upload their solution to the first case. Details on the case are given in the course sessions.

CASE 2 (Group work; 25% of the total assessment): Groups of up to five students, upload their solution to the second case. Details on the case are given in the course sessions.

There is no minimum number of points to be achieved in each grade component.

For the group works (Cases 1 and 2), your manuscript/document needs a statement (on the first page), whether or not you used artificial intelligence (e.g., ChatGPT) for your work. In case you did not use AI, formulate this explicitly (“I/we did not use any form of AI for this work”); In case you did use AI, pinpoint clearly for what aspects of your work you used what type of AI (e.g. as search engine, to do grammar check, etc.). Correct and consistent referencing of all resources is needed.

Grading scale in %:

100-90 Very good

89.5-70 Good

69.5-60 Satisfactory

59.5-50 Sufficient

49.5-0 Not sufficient

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Ask questions in class; possible also available by email.