Sound of Silence: When to Conceal Attribute Information? The Roles of Consumer Search, Inventory, and Channel Structure

Problem Definition: This study examines a seller’s joint information disclosure and pricing decisions when launching a new product with an easy-to-communicate objective attribute and a hard-to-describe subjective attribute. We analyze how consumer search cost, inventory level, and channel structure shape the seller’s optimal information strategy. Results: For brick-and-mortar retailing, the seller should conceal (disclose) attribute information when the search cost is low (high). Interestingly, when the inventory level is high, the seller conceals information to retain the “diamond-in-the-rough” consumers, aiming to match the abundant inventory with high demand. Conversely, when inventory is low, the seller discloses information to dissuade some consumers with low objective valuations from searching, thereby reducing demand and alleviating consumers’ concerns about product availability. Thus, the seller strategically uses attribute information to balance search and stock. This result remains robust under extensions such as partial disclosure, atomic consumers, and efficient allocation. Moreover, intensified demand-side competition (e.g., a larger consumer base) can unexpectedly reduce the seller’s revenue because of price markdown pressures to ease consumers’ concerns about competition. In an omnichannel setting, where an online channel supplements physical retail, a high inventory level may reverse the optimal information policy. Managerial Implications: Our findings offer insights into how demand-side factors (e.g., search cost) and supply-side factors (e.g., inventory level and channel structure) shape optimal disclosure. We also reveal risks associated with demand-side competition. Funding: X. Fu acknowledges financial support from the University of New South Wales [UNSW Business School Dean’s Research Fellowship]. P. Gao’s research is supported by the National Natural Science Foundation of China [Grants 72522026, 72201234, 72192805], the Collaborative Research Funding Hong Kong [Grant C6032-21G], the Guangdong Provincial Key Laboratory of Mathematical Foundations for Artificial Intelligence [Grant 2023B1212010001], and industry collaborators including Meituan and Fengyi Technology. Y.-J. Chen acknowledges financial support from the Hong Kong Research Grants Council [Grants 16501722, 16212821, and C6020-21GF]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.0288 .