Event
Matthias Seifert: Beyond Pull-to-Center: How System Neglect Shapes Ordering Decisions Under Shifting Demand
06.05.2026, 12:00–13:30
Kühne Logistics University
Großer Grasbrook 17, 20457 Hamburg, Germany, Room 5th floor lecture 2 and Zoom Research Seminar
English
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Abstract
We examine how decision makers adjust newsvendor ordering decisions in response to demand signals that are indicative of a demand shift. Demand shifts driven by market changes, seasonality, or external shocks create uncertainty that decision makers frequently encounter in practice. By integrating models that explain the well-known pull-to-center (PTC) effect in static newsvendor environments with models of system neglect that explain how probability judgments react to signals indicative of a demand shift, we predict when PTC and system neglect mitigate versus amplify one another. Specifically, for high-margin products in stable environments with noisy signals and low-margin products in unstable environments with precise signals, system neglect and PTC effect tend to counteract each other, resulting in ordering decisions closer to optimal levels. In the remaining profit margin and demand environments, system neglect amplifies the underordering and overordering behavior resulting from the PTC effect. We test these predictions in experimental settings with uncensored and censored demand signals and find support for our model predictions. We explore the effectiveness of decision support systems and discuss implications for providing managerial interventions in environments with salient demand shifts.
Bio
Matthias Seifert is an Associate Professor of Decision Sciences in the Operations & Business Analytics Area at IE Business School- IE University as well as Senior Associate Fellow at the NATO Defense College. He received his PhD from Judge Business School at Cambridge University and previously worked as a postdoctoral scholar at the London Business School. Matthias’ research focuses on Behavioral Decision Analysis, where he uses experimental and analytical methods to study individual and collective decision making under uncertainty across a variety of contexts.
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