We introduce a measure of elasticity of stochastic demand, called the elasticity of the lost-sales rate, which offers a unifying perspective on the well-known newsvendor with pricing problem. This new concept provides a framework to characterize structural results for coordinated and uncoordinated pricing and inventory strategies. Concavity and submodularity of the profit function, as well as sensitivity properties of the optimal inventory and price policies, are characterized by monotonicity conditions, or bounds, on the elasticity of the lost-sales rate. These elasticity conditions are satisfied by most relevant demand models in the marketing and operations literature. Our results unify and complement previous work on price-setting newsvendor models and provide a new tool for researchers modeling stochastic price-sensitive demand in other contexts.

W e study and compare decision-making behavior under the newsvendor and the two-class revenue management models, in an experimental setting. We observe that, under both problems, decision makers deviate significantly from normative benchmarks. Furthermore, revenue management decisions are consistently higher compared to the newsvendor order quantities. In the face of increasing demand variability, revenue managers increase allocations; this behavior is consistent with normative patterns when the ratio of the selling prices of the two customer segments is less than 1/2, but is its exact opposite when this ratio is greater than 1/2. Newsvendors' behavior with respect to changing demand variability, on the other hand, is consistent with normative trends. We also observe that losses due to leftovers weigh more in newsvendor decisions compared to the revenue management model; we argue that overage cost is more salient in the newsvendor problem because it is perceived as a direct loss, and propose this as the driver of the differences in behavior observed under the two problems.

We present an experimental study of the price‐setting newsvendor problem, which extends the traditional framework by allowing the decision maker to determine both the selling price and the order quantity of a given item. We compare behavior under this model with two benchmark conditions where subjects have a single decision to make (price or quantity). We observe that subjects deviate from the theoretical benchmarks when they are tasked with a single decision. They also exhibit anchoring behavior, where their anchor is the expected demand when quantity is the decision variable and is the initial inventory level when price is the decision variable. When decision makers set quantity and price concurrently, we observe no significant difference between the normative (i.e., expected profit‐maximizing) prices and the decision makers’ price choices. Quantity decisions move further from the normative benchmarks (compared to when subjects have a single decision to make) when the ratio of cost to price is less than half. When this ratio is reversed, there is no significant difference between order levels in single‐ and multi‐task settings. In the multidecision framework, we also observe a tendency to match orders and expected demand levels, which subjects can control using prices.

F irms can increase profitability by appropriately motivating managers. We investigate drivers of managerial motivation, and propose how firms can use performance pay to alter motivational patterns. We focus on the agent's optimal effort decision in trading off compensation utility with effort cost in a static and dynamic setting. Surprisingly, we find that lower risk aversion or increased pay are not necessarily motivating factors, and identify the relevant effort drivers underlying the agent's utility and compensation plan. We characterize properties of agents' preferences for output lotteries (risk aversion, aggressiveness, prudence) that trigger systematic motivational patterns with respect to a variety of factors, such as the agent's productivity and past performance, time to evaluation, the firm's capabilities, and market factors. Our insights are robust, holding under very general modeling assumptions on preferences, rewards, and the stochastic effort-output function.

In the standard two‐class revenue management model, the decision maker allocates a fixed resource between two customer classes with hierarchical prices and uncertain demand. The normative (i.e., expected revenue‐maximizing) allocation is given by Littlewood's Rule, but little is known about how decision makers actually form these decisions. We report results of an experimental study that investigates revenue management decision‐making. We find that subjects' behavior is influenced by the decision type. In particular, our subjects reserve more units for the high‐end segment when they are asked to set the protection level (the number of units to set aside for the higher priced class) compared to when they set the booking limit (the number of units available for the lower priced class). We propose that this behavioral pattern can be explained by our subjects’ different valuations of revenues from the high‐ and low‐end sales. We also observe that when there is a change in segment prices, although decision makers adjust allocations in the direction suggested by normative theory, the magnitude of adjustments is greater (and hence closer to the normative level) when the source of the price change matches the class whose allocation they determine.