INSEAD - SU Alliance - PhD in Management

Instructor: Sujin Jang

Prerequisites: This course is designed for doctoral students with a foundation in organisational behaviour or a related social science. Students should be comfortable engaging with research that spans multiple literatures and methods, including qualitative, experimental, and computational approaches. No prior coursework on collaboration or boundary spanning is assumed, but intellectual curiosity and willingness to engage with unfamiliar disciplines are essential.

Important: Students must be fully comfortable reading, discussing, and writing about academic research in English, as the course involves intensive seminar discussion and written assignments.

https://www.insead.edu/faculty/sujin-jang

Workload: Approximately 5–6 research papers to read per session, a weekly paper (~1 page) combining real-world observation with a research question and study sketch, and a final integrative paper (10–15 pages). Active class participation is essential.

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Course description:

This course explores the science of collaboration across boundaries. Drawing on research from organisational behaviour, social psychology, sociology, and adjacent fields, we will examine five types of boundaries that shape how people work together: (1) knowledge and expertise, (2) status and power, (3) identity, (4) culture, and (5) geography. For each, we will explore the following questions: What makes this boundary hard to cross? When and why does crossing it pay off? And what are the conditions that facilitate effective collaboration across the boundary?
The course is designed to expose students to both classic and cutting-edge research on these topics. Rather than staying within a single theoretical tradition, each session draws on multiple disciplines and approaches, modeling the very boundary crossing it studies. Throughout, we will also reflect on what this research means for our own practice as scholars, discussing how we identify questions, choose collaborators, and navigate the boundaries within academia itself.

Instructor: Mohammed Abdellaoui, HEC Paris

Prerequisites: 

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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

Prerequisites: 

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Course Objectives:
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 Public Policy, 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”.

The course will be run as a combination of lectures, discussions of important papers, exercises, and a class project.

Instructor: Sujin Jang

Prerequisites: 

Students should be enrolled in a doctoral program in organisational behaviour, management, psychology, or a related field. No specific coursework is required, but students should be comfortable reading and critically evaluating empirical research papers across a range of methods (quantitative, qualitative, and theoretical). Familiarity with core OB topics (e.g., motivation, teams, leadership, culture) at the level of a master's degree or first-year PhD curriculum is expected.

Important: Students must be fully comfortable reading, discussing, and writing about academic research in English, as the course involves intensive seminar discussion and written assignments.

Workload: Approximately 5–6 research papers to read per session, a short weekly reflection paper (~1 page), and a final integrative paper (10–15 pages). Active class participation is essential, including engagement with guest scholars.

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Course Description: 

This course introduces doctoral students to core and emerging topics in organisational psychology. While the course draws primarily from psychological concepts and perspectives, it also engages with material from sociological and adjacent fields. Throughout the course we will discuss various theoretical concepts and frameworks, as well as different empirical methods.
A distinctive feature of this course is its “Conversations with the Minds Behind the Research” format. In each topical session, a faculty member from our own department whose work is central to the session's theme will join us to take us behind the scenes of their research. This is a rare opportunity to engage with the scholars in your intellectual community not just as teachers or advisors, but as fellow researchers—to hear how they think, how they work, and how their ideas developed over time.
Guests will be invited to share the stories that don’t make it into published papers, such as how they arrived at their research questions, the methodological choices and trade-offs they made, any surprises or setbacks they encountered, and what they learned along the way. The goal is not only to deepen your understanding of the topics we cover, but to give you a window into the practice of research as lived by active scholars.

Instructor: Marc Le Menestrel

Prerequisites: This course is designed to be open to all PhD students in Management Sciences, regardless of their specialisation, methodological background, or stage of doctoral study. No prior knowledge of mathematics, philosophy, or decision theory is required.
Students are expected to be actively engaged in a research project, regardless of its stage of development, and to be willing to reflect on it critically and openly. The course is structured around the use of each participant’s own research as the primary material for learning. Emphasis is placed not on prior knowledge, but on intellectual curiosity and a readiness to examine foundational questions concerning the purpose, assumptions, and direction of one’s work.

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Course Description and Objectives

Instructors: 

• Hyunjin Kim
• Victoria Sevcenko

Prerequisites: 

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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

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.

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Course Description  (as last taught - 2025)

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