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Session Overview
Session
WB 11: Behavioral Decisions 1
Time:
Wednesday, 04/Sept/2024:
11:00am - 12:00pm

Session Chair: Christian Jost
Location: Theresianum 2609
Room Location at NavigaTUM


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Presentations

Points and Cards - A behavioral view of the decision-making under equivalent inventory policies

Alexander Daniels, Guido Voigt

University of Hamburg, Germany

Many inventory decisions are made by humans influenced by behavioral effects. These effects can lead to losses through stockouts or excessive inventory. This work tries to shed light on the behavioral impacts on decision-making in frequently used order policies. The two investigated inventory policies are the (r, n*Q, t)-policy and the periodic Kanban.

These inventory policies are very similar and become equivalent if certain criteria are met. In this work, we assume stochastically normal distributed demand, lost sales, and periodic review cycles. A between-subject laboratory experiment was conducted. The findings show significant differences in decision-making between the two policies. Decision-making under both policies seems to be influenced by anchoring on “prominent” or round numbers in their respective decisions.



Can Working Iteratively Drive Innovation?

Evgeny Kagan1, Tobias Lieberum2, Sebastian Schiffels3, Christian Jost4

1Johns Hopkins University; 2CyberDesk GmbH; 3University of Augsburg; 4Technical University of Munich

Motivated by the widespread adoption of project management techniques like Agile and Scrum, we compare the effects of iterative versus sequential workflow on innovative behavior and performance. In our laboratory experiments, we find that in an open-ended creative design challenge, iterative task completion outperforms sequential task completion. In a second experiment, we show that the performance advantage of the iterative workflow persists in settings that do not involve creative idea generation. This result can be explained by the frequent task-switching in the iterative workflow, which promotes a broader exploration of alternative solutions. Indeed, a third experiment reveals that the sequential workflow results in a more myopic idea refinement, often trapping the innovation process in local optima. Taken together, our results suggest that the iterative workflow improves performance across multiple, structurally distinct innovation settings. Moreover, we identify the following three boundary conditions for our result. First, working iteratively helps to achieve quick performance gains, but the performance advantage diminishes over time. Second, iterative workflow mainly benefits low performers and has minimal effect on top performers. Third, the iterative workflow can be detrimental in projects with strong path dependencies.



 
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