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Cheap Talk in Operations: Role of Intentional Vagueness

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Book cover Consumer-Driven Demand and Operations Management Models

Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 131))

Abstract

Provision of real-time information by firms to their customers has become prevalent in recent years in both the service and retail sectors. Service providers use delay announcements to inform customers about anticipated service delays, whereas retailers provide the customers with information about the inventory level and the likelihood of a stockout. Often, this information cannot be credibly verified by the customers. The question of which information should the firm share with its customers is a complex one, and its answer depends among other things on the dynamics of the underlying operations and the customer behavior.

This chapter addresses these issues by proposing a model in which customers treat information provided by the service provider as unverified and non-binding. The model thus treats customers as strategic in the way they process information, as well as in making the decisions (that is, in service settings whether to join or balk, and whether to buy or wait in retail), and the firm as strategic in the way it provides the information. The customers and the firm are assumed to be self-interested in making their decisions: the firm in choosing which announcements to make and the customers in interpreting these and making the decisions. This allows us to characterize the equilibrium language that emerges between the firm and its customers. By doing that, not only do we relax the assumption that customers are naive in their treatment of the announcements, but we also demonstrate that many of the commonly used announcements arise in equilibrium in such a model.

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Correspondence to Gad Allon .

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Allon, G., Bassamboo, A. (2009). Cheap Talk in Operations: Role of Intentional Vagueness. In: Tang, C., Netessine, S. (eds) Consumer-Driven Demand and Operations Management Models. International Series in Operations Research & Management Science, vol 131. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-98026-3_1

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