Marketing is an applied science that tries to explain and influence how firms and consumers behave in markets. Marketing models are usually applications of standard economic theories, which rely on strong assumptions of rationality of consumers and firms. Behavioral economics explores the implications of the limits of rationality, with the goal of making economic theories more plausible by explaining and predicting behavior more accurately while maintaining formal power. This article reviews six behavioral economics models that are useful to marketing. Three models generalize standard preference structures to allow for sensitivity to reference points and loss aversion, social preferences toward outcomes of others, and preference for instant gratification. The other three models generalize the concept of game-theoretic equilibrium, allowing decision makers to make mistakes, encounter limits on the depth of strategic thinking, and equilibrate by learning from feedback. The authors also discuss a specific marketing application for each of these six models. The goal of this article is to encourage marketing researchers to apply these models. Doing so will raise technical challenges for modelers and will require thoughtful input from psychologists who study consumer behavior. Consequently, such models could create a common language both for modelers who prize formality and for psychologists who prize realism.

For most firms, new product development is the engine for growth and profitability. A firm's new product success depends on its ability to manage the product development process in a way that employs scarce resources to achieve the goal of the firm as well as the specific project's objectives. Simple and measurable performance metrics have been proposed and applied to monitor and compensate the development teams. In this paper, we develop a modeling frame work to analyze the implications of setting managerial priorities for three commonly used new product performance metrics: (1) time-to-market, (2) product performance, and (3) total development cost. We model new product development as a “product performance production” process that requires scarce development resources. Setting a target for development teams for each of these performance metrics can constrain this performance production process and, thereby, affect the other performance metrics. We model the constrained process as a restricted case of a general process that does not have such constraints. We benchmark each constrained process against the optimal, unrestricted process with respect to the level of the resource intensity employed during the development process, the time-to-market, and the performance level of the new product at launch. We show that an overly ambitious time-to-market target leads to an upward bias in resource intensity usage and a downward bias in product performance (i.e., evolutionary product innovation). In addition, our results suggest that the target time-to-market approach may ignore the effect of cannibalization and, thus, can perform suboptimally if a significant degree of cannibalization in the existing product market is expected. Given a target product performance, we show that the coordination between marketing and R&D is easier because the resulting development resource intensity and time-to-market decisions becomes separable. However, an overly ambitious product performance target leads to an upward bias in the development resource intensity and a delayed product launch that misses the window of opportunity. Finally, we show that the target development cost approach can lead a downward bias in product performance and a premature product launch. The above analyses are performed for a monopolistic firm, and they are extended to passive and active competitive environment.

Service firms have increasingly been competing for market share on the basis of delivery time. Many firms now choose to set customer expectation by announcing their maximal delivery time. Customers will be satisfied if their perceived delivery times are shorter than their expectations. This gap model of service quality is used in this paper to study how a firm might choose a delivery-time commitment to influence its customer expectation, and delivery quality in order to maximize its market share. A market share model is developed to capture (1) the impact of delivery-time commitment and delivery quality on the firm's market share and (2) the impact of the firm's market share and process variability on delivery quality when there is a congestion effect. We show that the choice of the delivery-time commitment requires a proper balance between the level of service capacity and customer sensitivities to delivery-time expectation and delivery quality. We prove the existence of Nash equilibria in a duopolistic competition, and show that this delivery-time commitment game is analogous to a Prisoners' Dilemma.

This special issue, by addressing problems surrounding marketing and operations management, depicts state-of-the-art approaches, methodologies, and insights to improve a firm's or supply chain's overall performance. Top scholars in the field address many of the ways in which companies can synchronize their marketing and operations departments or their supply chain partners to improve competitiveness and profit. The information in this issue should be of interest both to academics and managers, and represents the current thoughts in an emerging area of marketing and operations interfaces.

We study the demand forecast-sharing process between a buyer of customized production equipment and a set of equipment suppliers. Based on a large data collection we undertook in the semiconductor equipment supply chain, we empirically investigate the relationship between the buyer's forecasting behavior and the supplier's delivery performance. The buyer's forecasting behavior is characterized by the frequency and magnitude of forecast revisions it requests (forecast volatility) as well as by the fraction of orders that were forecasted but never actually purchased (forecast inflation). The supplier's delivery performance is measured by its ability to meet delivery dates requested by the customers. Based on a duration analysis, we are able to show that suppliers penalize buyers for unreliable forecasts by providing lower service levels. Vice versa, we also show that buyers penalize suppliers that have a history of poor service by providing them with overly inflated forecasts.

We consider the order-fulfillment process of a supplier producing a customized capital good, such as production equipment, commercial aircraft, medical devices, or defense systems. As is common in these industries, prior to receiving a firm purchase order from the customer, the supplier receives a series of shared forecasts, which are called “soft orders.” Facing a stochastic internal manufacturing lead time, the supplier must decide at what time to begin the fulfillment of the order. This decision requires a trade-off between starting too early, leading to potential holding or cancellation costs, and starting too late, leading to potential delay costs. We collect detailed data of shared forecasts, actual purchase orders, production lead times, and delivery dates for a supplier-buyer dyad in the semiconductor equipment supply chain. Under the assumption that the supplier acts rationally, optimally balancing the cancellation, holding, and delay costs, we are able to estimate the corresponding imputed cost parameters based on the observed data. Our estimation results reveal that the supplier perceives the cost of cancellation to be about two times higher and the holding costs to be about three times higher than the delay cost. In other words, the supplier is very conservative when commencing the order fulfillment, which undermines the effectiveness of the overall forecast-sharing mechanism.