INSEAD - SU Alliance - PhD in Management

Instructor: Sujin Jang - https://www.insead.edu/faculty/sujin-jang

Prerequisites: 

Syllabus: Here

Course description: (as last taught by Prof Ella Miron-Spektor - 2026):

Groups and teams have become the fundamental units of work in contemporary organizations. From executive boards to research collaborations and cross-functional project teams, much of what organizations accomplish today depends on collective effort rather than individual performance. Understanding how groups function, why they succeed or fail, and how they can be designed and managed effectively is therefore central to both organizational theory and practice. 

This course examines the major theories and empirical research on groups and teams in organizations. Drawing on more than a century of scholarship on group dynamics, it explores the psychological, social, and structural processes that shape how teams function and achieve their goals. The seminar will address several core streams of research, including team composition and diversity, collective learning and cognition, conflict and emotion, creativity and cooperation, and the interplay between teams and their broader organizational and cultural context. We will also consider how global and technological developments, such as virtual collaboration, the integration of artificial intelligence, and the emergence of the metaverse, are transforming the nature of teamwork and the challenges of managing collective performance.

Course objectives:

The purpose of the course is to deepen understanding of the mechanisms and dynamics that underlie teamwork and collective performance. By engaging with classic and contemporary research, students will learn to identify foundational debates, integrate multiple theoretical perspectives, and develop their own ideas for advancing the study of groups and teams.

Instructor: Mohammed Abdellaoui, HEC Paris  - https://www.hec.edu/fr/faculty-research/faculty-directory/faculty-member/abdellaoui-mohammed

Prerequisites: 

Syllabus: Here

Course description and objectives: (as last taught - 2023):

The course consists of three fundamental modules. The first focuses on the standard model of rational choice under uncertainty (with known and unknown probabilities). The second focuses on two recent extensions of the standard model: (i) the Prospect Theory family of models where probabilities (known or unknown) are replaced by willingness to bet coefficients that account for considerations related to perception of uncertainty (Tversky & Kahneman, 1992; Wakker, 2010); (ii) Multiple priors family where decision makers are assumed to hold imprecise (subjective) probabilities about uncertain events. In particular, we will present Gilboa & Schmeidler (1989) Maxmin expected utility, Ghirardato, Maccheroni & Marinacci (2004) alpha-Maxmin, and the Klibanoff, Marinacci & Mukerji (2005) smooth ambiguity model. The third module provides an introduction to discounted utility (Koopmans, 1960), and a common approach to decision-making over time and under uncertainty (Prelec and Loewenstein, 1991; Loewenstein and Elster, 1992).

Instructor: Nicolas Vayatis - Université Paris Saclay - https://centreborelli.ens-paris-saclay.fr/fr/annuaire-des-personnes/nicolas-vayatis

Prerequisites: 

Syllabus: Here

Course Description and Objectives (as last taught 2026)
AI and Machine Learning have become central topics of discussion in the popular press after being developed for over 50 years in Academia – by computer scientists and, in more recent years, by mathematicians and statisticians. These fields are expected to have a major impact in potentially every aspect of research as well as business: from basic science fields such as life sciences, to Decision Sciences, Finance, but also areas like Sociology, Economics, and other Social Sciences. However, while one can be a “reasonable” user of some popular machine learning and AI methods, gaining an edge in terms of innovation in research and practice but also taking full advantage of the capabilities offered by these technologies requires a more fundamental understanding of the principles behind these booming fields.
The goal of this course is to:
- Provide the foundations of Machine Learning and AI, so that students can better understand these methods, use them, and potentially develop their own custom based ones that can also use to advance their respective fields;
- Provide an overview of some of the most important machine learning methods used in research and practice;

- Provide students not only with a historical perspective of these fields, but also with a view of the state-of-the-art methodologies and research advances as well as views on future directions;
- Help students use machine learning methods appropriately in their research fields, with the aim of developing insights that are only feasible due to the usage of these new “microscopes”.

Instructor: Sujin Jang -  https://www.insead.edu/faculty/sujin-jang

Prerequisites: 

Syllabus: Here

Course Description: (as last taught - 2023)

The course consists of seven three-hour sessions. Session 1 provides an overview of the course and the areas we will cover. In Sessions 2 to 6, we will focus on various core topics in organizational psychology. Session 7 will be dedicated to a special workshop on the publication process. 

Course Objectives (as last taught - 2023)
The objective of this course is to introduce students to a set of core topics in organizational psychology. The course will draw primarily from psychological concepts and perspectives, but will also cover material drawn from sociological fields. Throughout the course we will discuss various theoretical concepts and frameworks, as well as different empirical methods.
In addition, the course will provide an opportunity to develop the skill of conducting research. In each session we will devote time to discussing students’ new research ideas related to the core topic of the session. Students will receive feedback on their ideas from the instructor and from one another.

Instructor: Marc Le Menestrel - https://www.insead.edu/faculty/marc-le-menestrel

Prerequisites: 

Syllabus: Here

Course Description and Objectives (as last taught - 2025)

This course uses the concept of Decisions to guide us into the foundations of sciences. Students learn about the history and the philosophy of science and develop a personal understanding of the foundational concepts of science. Broadly speaking, the course intends to celebrate how the science of Management is able to combine objective and subjective knowledge without reducing one to the other.
Along the course, students apply their learning to their own decisions about their own research (topic and strategy) for their Ph.D. In fine, students will have improved the positioning of their research and of themselves as researchers with respect to Management Sciences and science in general.
The course is open to all Ph.D. students independently of their area or the stage of their studies. Each session requires one accessible reading and the preparation of a brief reflection answering a few key questions. At the end of the course, students collect how these questions, their answers and the discussion in class have helped the scientific positioning and ambition of their research. The list of references is under construction and will be adapted to students’ interest.

Instructors: 

• Hyunjin Kim - https://www.insead.edu/faculty/hyunjin-kim
• Victoria Sevcenko - https://www.insead.edu/faculty/victoria-sevcenko

Prerequisites: 

Syllabus: Here

Course Description and Objectives (as last taught - 2025)
Provide in-depth learning for selected topics of interest for PhD students in management, at the intersection  of the professors’ specific unique areas of expertise.

Module A: Victoria Sevcenko

1. What is human capital – the classics
2. Value creation and capture from human capital
3. Emerging questions in human capital research

Module B: Hyunjin Kim

1. Data, predictive AI, and strategy
2. Generative AI and strategy
3. AI and competition

Module C (Joint)

1. Publishing research in strategy

Learning Outcomes:

• A broad overview of the human capital literature from its foundational texts to more recent  contributions
• An overview of the emerging literature on data, AI, and strategy
• A deeper understanding of the publishing and job market process

Instructor: Asher Lawson - https://www.insead.edu/faculty/asher-lawson

Prerequisites: The course is designed to be accessible to participants without prior coursework in psychology. The course is conducted in a seminar format and relies heavily on critical reading and discussion of primary research articles, students are expected to have —a foundational understanding of statistical reasoning and empirical research methods commonly used in the social sciences.

Syllabus: Here

Course Description  (as last taught 2023)

This course is about the psychology of decision making. However, it differs from many such courses on the topic in that it does not bombard you with hundreds of papers tracking the history of decision making thought from homo economicus to today. Such courses have their purpose, but I think that to train early career researchers in critical thinking and generating research questions, it is better to focus on a smaller set of papers and engage with them more deeply. The literature included in this course has been carefully selected to give you examples of the relationship between data and conclusions, and deep knowledge of specific pockets of decision making theory to stimulate future research.

Offer to be confirmed