When to Broadcast? Inventory Disclosure Policies for Online Sales of Limited Inventory

Online sales of limited inventory such as flash sales and lightning deals have become popular among e-commerce retailers including Amazon and eBay. This study focuses on the retailer’s best timing of disclosing inventory information to maximize the expected sales in a finite horizon. We consider how two prominent customer mechanisms, herding effect and scarcity effect, affect the relative performance of different policies. We analyze the following common policies in practice: “always disclose,” “never disclose,” and the fixed threshold policy, which broadcasts the inventory level once it drops below a predetermined level. We also propose a novel time-dependent threshold policy, which we prove to be the optimal policy under reasonable assumptions. We devise efficient algorithms to optimize the policy parameters, and we compare all policies through a numerical study. We find that both threshold policies significantly outperform the two simple policies. Moreover, herding effect and scarcity effect have significant impacts on the relative performance of different policies. Our study provides not only effective and efficient algorithms for policy optimization but also guidelines for policy selection. Policymakers can refer to our study to identify the most appropriate policy, depending on the relative strength of the two customer mechanisms on the platform.

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Publication Details

Journal
Information Systems Research
Published
2025-05-15
DOI
https://doi.org/10.1287/isre.2023.0746
Citations
2
Primary Topic
Consumer Market Behavior and Pricing
Type
article
Field-Weighted Citation Impact
4.15
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article

When to Broadcast? Inventory Disclosure Policies for Online Sales of Limited Inventory

Z. Liu, Kamran Moinzadeh, Yong Tan, Shi Chen
2 citations
Information Systems Research
Consumer Market Behavior and Pricing
4.15
article

When to Broadcast? Inventory Disclosure Policies for Online Sales of Limited Inventory

Z. Liu, Kamran Moinzadeh, Yong Tan, Shi Chen
article en
2 citations

Abstract

Online sales of limited inventory such as flash sales and lightning deals have become popular among e-commerce retailers including Amazon and eBay. This study focuses on the retailer’s best timing of disclosing inventory information to maximize the expected sales in a finite horizon. We consider how two prominent customer mechanisms, herding effect and scarcity effect, affect the relative performance of different policies. We analyze the following common policies in practice: “always disclose,” “never disclose,” and the fixed threshold policy, which broadcasts the inventory level once it drops below a predetermined level. We also propose a novel time-dependent threshold policy, which we prove to be the optimal policy under reasonable assumptions. We devise efficient algorithms to optimize the policy parameters, and we compare all policies through a numerical study. We find that both threshold policies significantly outperform the two simple policies. Moreover, herding effect and scarcity effect have significant impacts on the relative performance of different policies. Our study provides not only effective and efficient algorithms for policy optimization but also guidelines for policy selection. Policymakers can refer to our study to identify the most appropriate policy, depending on the relative strength of the two customer mechanisms on the platform.

Information Systems Research
University of Washington (US), Fudan University (CN)
Openalex Percentile: Top 7%
Consumer Market Behavior and Pricing
4.15
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