Schedule of courses

Credits: 3 ECTS

Prof. B. Garbinato and M. Humbert

Spring semester

Main objectives

Digital transformation is the next step of the digital revolution: Information and Communication Technology (ICT), after having essentially been a means to optimize existing business processes, is becoming today the vector of profound transformations, even brutal disruptions. In addition, ICT being a market of innovation, today's technological breakthroughs tend to become commodities very quickly, often in just a few years.

For this reason, being able to closely monitor, understand and critically assess innovations in computer science is an essential skill for any organisation or manager willing to keep up with its competitors, or even better, to get a competitive edge. Here however, merely reading executive summaries and professional journals reporting what other companies are doing is not enough, especially if one has the ambition to be the disruptor rather than the disrupted.

The goal of this course is precisely to address this challenge, by teaching students the basis of the computer science research methodology, i.e., how the research community in computer science is ideating, developing, testing and validating new ideas and concepts. Through this course, students will learn to identify and read relevant research papers with a critical view, to extract and understand key innovations from those papers and to evaluate the scope and validity of those innovations. This course is also an introduction to research in computer science for students who consider pursuing their curriculum with an academic master thesis and/or doctoral studies in ICT.

Content overview 

The course is structured around a set of fundamental questions related to computer science research and associated methodology, some of which are listed hereafter.
 

• What is research? Why do it in universities? What is the basis of the scientific method?
• What is specific about computer science research? What is its methodology?
• What is the notion of state of the art and why is it important in research?
• What is a scientific publication and what is the process to produce one?
• How is research in computer science evaluated? Where is it published?
• How is the computer science research community organized?
• What are some key research areas in computer science?

Link to Syllabus

Credits: 3 ECTS

Prof. Stéphanie Missonier

Spring semester

Content overview and approach

This course is designed to provide PhD students the fundamental aspects of IS theories and methodologies. The purpose of this course is to provide an overview of theories using and developing in IS and the main methodological approaches. 

The course is designed to be very interactive and thus participation is crucial. Each student contributes to the success of the class. Therefore, each student should come to class having read and thought about the articles/readings for the week (details of the reading list and assignments Moodle : IS Theories and Methods Spring 2017). The purpose of classes is to discuss what you have learnt from the readings and to clarify points you did not understand. 

Main objectives

The main objective of the course is to familiarise students with the basic assumptions, concepts, theories and methodologies underlying the IS field. More precisely, at this end of this course, students will:

1. Develop a broad foundation of knowledge of the theories associated with IS research

2. Understand the key theories and classic writings used in IS research

3. Understand the strengths and weaknesses of the commonly used research methodologies in IS

4. Learn to evaluate theoretical contribution in research

5. Be well versed in the process of publishing IT related research in IS

Please contact the Professor in charge for the syllabus of the course.

Credits: 3 ECTS

Prof. Mauro Cherubini

Fall semester

Main objectives

The goal of the course is to provide PhD students with an understanding of the  main perils that researchers can face during their careers.  Specifically, the course will support students in developing a critical mind necessary to spot issues affecting reproducibility and research integrity in class.

Content overview and approach

The course will tackle three main areas of the contemporary debate on research excellence: research ethics, scientific integrity, and reproducibility. One of the aims of the instructors is to show how these topics are tightly interlinked. Another objective of this course is to provide students with practical tools and strategies to prevent experimental design mistakes, improve their scientific rigor, and conduct reproducible research.

The course will be taught using contrasting case studies: each core topic of the course will be presented through two case studies that reveal different facets of the same topic. Students will be asked to study the case studies, and prepare summaries highlighting the major ethical issues identified that will be presented and discussed.
 

Please contact the Professor in charge for the syllabus of the course.

Credits: 3 ECTS

Prof. D. Philippe and J. Petty

Fall semester

Content overview and approach

This course aims at offering an introduction to qualitative and mixed research methods. It is designed for doctoral students who are interested in pursuing qualitative or mixed-methods research projects as well as those who plan on mobilizing qualitative methods more marginally in their research. 

The course will help participants acquire the necessary skills to design, execute, report, and review qualitative and mixed-methods research in management. Students will gain knowledge of the foundations of these research methods and of the considerations that are embedded in the design of projects using such methods. You will be expected to come prepared to all sessions, which means that you must read the assigned articles before class, unless indicated otherwise. These assigned readings will be a mix of theoretical/methodological pieces (e.g., editorials, book chapters) and illustrative empirical studies. You will also find in this syllabus suggestions for additional readings should you wish to further explore what will be discussed in class. These additional readings are not part of the mandatory preparation.

Link to Syllabus

Credits: 6 ECTS

Prof. C. Peukert and I. Engeler

Fall semester

Main objectives

The objective of this course is to provide students with an understanding of causality in empirical research, and why experiments are so useful to uncover causal relationships. It is tailored for PhD students with an interest in doing research in areas such as behavioral economics, consumer behavior, organizational behavior, strategy and (business) policy evaluation. 

Content overview and approach

We discuss methods for observational data, where the researcher cannot actively design an experiment, but must rely on variation from natural or quasi experiments. We make use of simulation to build an intuition for when these methods work well, and when they are better avoided. The course further covers the construction of experimental designs in the lab and field, the development of experimental tasks and stimuli, how to avoid confounds and other threats to validity, procedural aspects of administering experiments, and the analysis of experimental data. We will replicate published research to give you hands-on experience in applying the methods covered in the course. Sessions are conducted in an interactive seminar format, with extensive discussion of concrete examples, challenges, and solutions.

Link to Syllabus

Credits: 6 ECTS

Prof. Yash Raj Shrestha

Fall semester

Main objectives

• To critically analyze the transformative impact of AI on organizations.
• To understand and evaluate the integration of AI within organizational decision-making and problem-solving processes.
• To develop understanding in utilizing AI and machine learning techniques for advanced management research.
• To foster a nuanced understanding of the ethical, theoretical, and empirical considerations surrounding AI in management research.

Content overview and approach

This doctoral-level course offers an in-depth examination of the multifaceted impact of Artificial Intelligence (AI) within the realm of management research. The course will begin with general technical foundations of machine learning and related advanced technologies (such as deep learning and generative AI). Following this, the course is structured into three comprehensive modules: AI as a change agent, AI as an organizational agent, and AI as a research tool. Each module is designed to provide researchers with both theoretical insights and empirical applications, fostering a nuanced understanding of AI's transformative role. While the first two modules will focus on the AI as phenomenon of inquiry, third model will focus on AI as a research method in management.

Please contact the Professor in charge for the syllabus of the course.

Credits: 6 ECTS

Prof. Michalis Vlachos

Fall semester

Main objectives

Today, enterprises collect troves of data about their clients: historical purchases, responses to marketing events, web search logs, etc. In today’s data-driven economy, data can assist us in better understanding our customers, and in taking more informed decisions about our business.

Our goal in this class is to understand the basic terminology of data science and machine learning (regression, classification, visualization, text analytics, neural networks, recommender systems, etc), comprehend the potential pitfalls, get a general understanding of how to address real-world problems using Python code. A tangent goal of this class is to increase your confidence in your coding skills and to see and think how to innovate in an enterprise using machine learning.

Content overview

• Applications and Techniques of Data Science and Machine Learning.
• Social & Sustainability Issues of Machine Learning and AI.
• Where to find data, how to visualize data.
• Regression and Evaluation
• Classification and Evaluation.
• Internet of Things
• Text Analytics
• Neural Networks
• Recommender Systems

Link to Syllabus