Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/290194 
Year of Publication: 
2022
Citation: 
[Journal:] Decision Sciences [ISSN:] 1540-5915 [Volume:] 55 [Issue:] 2 [Publisher:] Wiley [Place:] Hoboken, NJ [Year:] 2022 [Pages:] 159-175
Publisher: 
Wiley, Hoboken, NJ
Abstract: 
State-of-the-art revenue management systems combine forecasting and optimization algorithms with human decision-making. However, only a few existing contributions consider the behavioral aspects of revenue management. To extend the related research, we examine the impact of nonstationary demand and two dynamic decision tasks. We examine human decision-making strategies and biases by implementing a related experimental design in a laboratory study and comparing participant decisions to systematic heuristics. Our results highlight that participants struggle to accommodate a nonstationary willingness to pay. In that, they exhibit a combination of optimism and loss aversion biases. We further find that participants anchor their decisions on customers' willingness to pay. We draw implications and further research opportunities to behaviorally inform the design of symbiotic analytics systems from these results.
Subjects: 
analytics
behavioral operations research
pricing
revenue management
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by-nc-nd Logo
Document Type: 
Article
Document Version: 
Published Version

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