Artikel
Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
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.
- Language
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Englisch
- Bibliographic citation
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Journal: Decision Sciences ; ISSN: 1540-5915 ; Volume: 55 ; Year: 2022 ; Issue: 2 ; Pages: 159-175 ; Hoboken, NJ: Wiley
- Classification
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Politik
- Subject
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analytics
behavioral operations research
pricing
revenue management
- Event
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Geistige Schöpfung
- (who)
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Cleophas, Catherine
Schüetze, Claudia
- Event
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Veröffentlichung
- (who)
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Wiley
- (where)
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Hoboken, NJ
- (when)
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2022
- DOI
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doi:10.1111/deci.12573
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Artikel
Associated
- Cleophas, Catherine
- Schüetze, Claudia
- Wiley
Time of origin
- 2022
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Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
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Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
Embodied decision biases during walking
Non-stationary Wave Turbulence
Choice-induced biases in perceptual decision-making
Bounded rationality in decision making: An analysis of the decision-making biases
Learning in non-stationary Environments
Non-stationary optical homodyne tomography
Bootstrapping Non-Stationary Stochastic Volatility
Stationary Subspace Analysis: Towards understanding non-stationary data
Choice history biases in value-based decision making
Sequence prediction for non-stationary processes
Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
Decision biases in revenue management revisited: Dynamic decision‐making under stationary and nonstationary demand
Embodied decision biases during walking
Non-stationary Wave Turbulence
Choice-induced biases in perceptual decision-making
Bounded rationality in decision making: An analysis of the decision-making biases
Learning in non-stationary Environments
Non-stationary optical homodyne tomography
Bootstrapping Non-Stationary Stochastic Volatility
Stationary Subspace Analysis: Towards understanding non-stationary data
Choice history biases in value-based decision making