Objectives
Over the past decade, empirical research has been gaining traction in top operations management journals. This has been mainly due to improvements in suitable software and computing power, as well as the increased availability of data – both primary (i.e., collected by the researcher) and secondary (i.e., available from other public or proprietary sources).

By the end of the course, students will: (1) Understand various aspects of empirical research in OM; (2) understand the theoretical background of, and be able to apply selected mathematical techniques for hypothesis testing; and (3) be able to judge other work in this area and identify new research directions.

Course description
The course is heavily literature-based. The first half of the day will be devoted to a broad introduction to the empirical SCM literature, its foundations and the methodologies used. Particular attention will be paid to recent research output using experimental, primary, and secondary data. The second half of the day will introduce the main mathematical concepts used for hypothesis testing in the literature (e.g., regression models, fixed/between/random effects models, interaction effects). The second day will consist of in-depth discussions of a number of seminal papers, led both by the instructor and by the students (as part of their first assignment) and followed by an introduction to a number of specialized econometric techniques (e.g., logistic regressions, survival models, post-hoc analyses). Additionally, the second assignment will be further explained.

More details on the course in the attached document below.

Lecturer: Dr. Maximiliano Udenio (KU Leuven)

Course dates: 2 & 9 December 2020

Time: 10.00 -16.00 h.

Location:  Utrecht

Room: t.b.a.

ECTS: 0.5 (attendance) – 2 (attendance + assignment)

Participation: free for TRAIL/Beta/OML members and PhD students of participating faculties

Registration: via the GP-OML website – click here

GP-OML capita selecta: Empirical Methods in Supply Chain Management

This course is part of the GP-OML program (www.GPOML.nl)