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An experimental investigation of newsvendor decisions under ambiguity

Author

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  • Abhishek Shinde
  • Peeyush Mehta
  • R. K. Amit

Abstract

The literature on decision biases in the newsvendor model assumes classical version of the problem where the distribution of random demand is known. This context is decision-making under risk. In many real-life settings, firms are not able to elicit complete and exact information about the demand distribution. This results in decision-making under ambiguity. We examine the newsvendor ordering preferences under ambiguity. Our study is the first attempt in behavioural operations management research to examine the biases in newsvendor decisions under ambiguity. We design experiments to understand the ordering preferences under ambiguity and risk. The experimental results show that subjects deviate from the normative benchmarks. We observe ‘pull-to-center’ bias in newsvendor decisions under ambiguity. We also observe that subjects exhibit ‘asymmetry in ordering’. Both these biases have significant implications for both theory and practice. Our research is a building block for research in a variety of normative models in operations management literature where ambiguity in demand is a highly relevant context for decision-making.

Suggested Citation

  • Abhishek Shinde & Peeyush Mehta & R. K. Amit, 2021. "An experimental investigation of newsvendor decisions under ambiguity," International Journal of Production Research, Taylor & Francis Journals, vol. 59(19), pages 5960-5971, October.
  • Handle: RePEc:taf:tprsxx:v:59:y:2021:i:19:p:5960-5971
    DOI: 10.1080/00207543.2020.1797206
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    Cited by:

    1. Chang, Shuhua & Li, Jiajing & Sethi, Suresh P. & Wang, Xinyu, 2024. "Risk hedging for VaR-constrained newsvendors," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 181(C).
    2. Rung-Hung Su & Dong-Yuh Yang & He-Jhen Lin & Yu-Cheng Yang, 2023. "Estimating conservative profitability of a newsboy-type product with exponentially distributed demand based on multiple samples," Annals of Operations Research, Springer, vol. 322(2), pages 967-989, March.

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