In many cases, the benefit to a consumer of a product increases with the number of other users of the same product. These demand interdependencies are referred to in the literature as positive demand externalities or network externalities. This paper examines the dynamic pricing behaviors of an incumbent and a later entrant, with special attention to the impacts of demand externalities, compatibility, and competition on prices and profits. Defining market power as the ability to price above a competitor without losing market share, we show how demand externalities and installed base combine to confer market power. We model optimal pricing as a differential game with the optimal price trajectory established as Nash open-loop controls. For a duopoly durable goods market with strong demand externalities, the results show an increasing price trajectory can be optimal. As expected, a new entrant is better off if its products are compatible with those of the incumbent, especially when demand externalities are strong and the installed base of the incumbent is large. Less intuitively, the incumbent as well may be better off agreeing on common standards. The comparison of monopoly and duopoly shows that under strong demand externalities and a small installed base, the incumbent profits from compatible entry.diffusion, new products, dynamic pricing, duopoly competition, network externalities, compatibility, standards

JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Marketing Association is collaborating with JSTOR to digitize, preserve and extend access toThe authors introduce a new estimation procedure, Augmented Kalman Filter with Continuous State and Discrete Observations (AKF(C-D)), for estimating diffusion models. This method is directly applicable to differential diffusion models without imposing constraints on the model structure or the nature of the unknown parameters. It provides a systematic way to incorporate prior knowledge about the likely values of unknown parameters and updates the estimates when new data become available. The authors compare AKF(C-D) empirically with five other estimation procedures, demonstrating AKF(C-D)'s superior prediction performance. As an extension to the basic AKF(C-D) approach, they also develop a parallel-filters procedure for estimating diffusion models when there is uncertainty about diffusion model structure or prior distributions of the unknown parameters. KalmanFilter Estimation o f New Product Diffusion Models The desire to forecast the diffusion of new products has inspired a large body of research during the past two decades. The accurate prediction of new product diffusions is critical in designing marketing strategies for new product planning and management. Before predicting sales, diffusion model specifications must be determined and parameters must be estimated. A variety of estimation methods for estimating diffusion models have been proposed. (For a review of the literature on these estimation techniques, see Mahajan, Muller, and Bass 1990.) In their article, Mahajan, Muller, and Bass (1990) classify diffusion model estimation procedures into two groups: time-invariant estimation procedures and timevarying estimation procedures. Time-invariant estimation procedures include the conventional estimation methods such as ordinary least square (OLS) (Bass 1969), maximum likelihood estimation (MLE) (Schmittlein and Mahajan 1982), and nonlinear least squares (NLS) (Srinivasan and Mason 1986). for their helpful suggestions. They also are thankful to JMR editor Vijay Mahajan and three anonymous reviewers for their insightful and constructive comments.(2) x(t)= [p + q n(t -1) i[m n(t -1)] = oi + a2n(t -1) + oa3n2(t -I), t = 1, 2,.... where x(t) is the number of new adopters in the tth interval, and 378 This content downloaded from 150.135.239.97 on Thu,

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