Organizations enter alliances with each other to access critical resources, but they rely on information from the network of prior alliances to determine with whom to cooperate. These new alliances modify the existing network, prompting an endogenous dynamic between organizational action and network structure that drives the emergence of interorganizational networks. Testing these ideas on alliances formed in three industries over nine years, the authors show that the probability of a new alliance between specific organizations increases with their interdependence, but also with their prior mutual alliances, common third parties, and joint centrality in the alliance network. The differentiation of the emerging network structure, however, mitigates the effect of interdependence and enhances the effect of joint centrality on new alliance formation. 3

Quantitative models have proved valuable in predicting consumer behavior in the offline world. These same techniques can be adapted to predict online actions. The use of diffusion models provides a firm foundation to implement and forecast viral marketing strategies. Choice models can predict purchases at online stores and shopbots. Hierarchical Bayesian models provide a framework to implement versioning and price segmentation strategies. Bayesian updating is a natural tool for profiling users with clickstream data. I illustrate these four modeling techniques and discuss their potential for solving Internet marketing problems.

We explore opportunities for targeted pricing for a retailer that only tracks weekly storelevel aggregate sales and marketing-mix information. We show that it is possible, using these data, to recover essential features of the underlying distribution of consumer willingness to pay. Knowledge of this distribution may enable the retailer to generate additional profits from targeting by using choice information at the checkout counter. In estimating demand we incorporate a supply-side model of the distribution channel that captures important features of competitive price-setting behavior of firms. This latter aspect helps us control for the potential endogeneity generated by unmeasured product characteristics in aggregate data. The channel controls for competitive aspects both between manufacturers and between manufacturers and a retailer. Despite this competition, we find that targeted pricing need not generate the prisoner’s dilemma in our data. This contrasts with the findings of theoretical models due to the flexibility of the empirical model of demand. The demand system we estimate captures richer forms of product differentiation, both vertical and horizontal, as well as a more flexible distribution of consumer heterogeneity.

Currently, there is an important debate about the relative merits of models with discrete and continuous representations of consumer heterogeneity. In a recent JMR study, Andrews, Ansari, and Currim (2002; hereafter AAC) compared metric conjoint analysis models with discrete and continuous representations of heterogeneity and found no differences between the two models with respect to parameter recovery and prediction of ratings for holdout profiles. Models with continuous representations of heterogeneity fit the data better than models with discrete representations of heterogeneity. The goal of the current study is to compare the relative performance of logit choice models with discrete versus continuous representations of heterogeneity in terms of the accuracy of household-level parameters, fit, and forecasting accuracy. To accomplish this goal, the authors conduct an extensive simulation experiment with logit models in a scanner data context, using an experimental design based on AAC and other recent simulation studies. One of the main findings is that models with continuous and discrete representations of heterogeneity recover household-level parameter estimates and predict holdout choices about equally well except when the number of purchases per household is small, in which case the models with continuous representations perform very poorly. As in the AAC study, models with continuous representations of heterogeneity fit the data better.

We propose a utility-theoretic brand-choice model that accounts for four different sources of state dependence: 1. effects of lagged choices (structural state dependence), 2. effects of serially correlated error terms in the random utility function (habit persistence type 1), 3. effects of serial correlations between utility-maximizing alternatives on successive purchase occasions of a household (habit persistence type 2), and 4. effects of lagged marketing variables (carryover effects). Our proposed model also allows habit persistence to be a function of lagged marketing variables, while accommodating the effects of unobserved heterogeneity in household choice parameters. This model is more flexible than existing state-dependence models in marketing and labor econometrics. Using scanner panel data, we find structural state dependence to be the most important source of state dependence. Marketing-mix elasticities are systematically understated if state-dependence effects are incompletely accounted for. The Seetharaman and Chintagunta (1998) model is shown to recover spurious variety-seeking effects while overstating habit-persistence effects. Ignoring habit persistence type 1 leads to an underestimation, while ignoring habit persistence type 2 leads to an overestimation of structural state-dependence effects. We find lagged promotions to have carryover effects on habit persistence. Ignoring one or more sources of state dependence underestimates the total incremental impact of a sales promotion. We draw implications for manufacturer pricing.

The multinomial logit model with random effects is often used in modeling correlated nominal polytomous data. Given that there is no standard software of fitting it, we advocate using either a Poisson log-linear model or a Poisson nonlinear model, both with random effects. Their implementations can be carried out easily by many existing commercial statistical packages including SAS. A brand choice data set is used to illustrate the proposed methods. KEY WORDS: Discrete choice model

suggestions. They thank the participants at the Summer Meeting of the North American Econometric Society at the

Allowing for variations in behaviour across respondents is one of the most fundamental principles in discrete choice modelling, given that the assumption of a purely homogeneous population cannot in general (or ever) be seen to be valid. Two approaches have classically been used to address this problem; the use of deterministic segmentations of the population, and the use of a random continuous representation of variations in tastes across respondents. In this paper, we discuss an alternative approach, based on the use of discrete mixtures of underlying GEV models over a finite set of distinct support points. The paper presents two applications; one illustrating the performance of the model with the help of simulated data, and one showing, on real data, how the model can be used to test the validity of hypotheses such as the presence of individuals with zero valuations of travel-time changes.

hopping experience applies to all attribute levels. The consumer is also allowed to respond dierently to a product's marketing mix activities over time. The model incorporates three key features: 1. The consumer's marginal utility from consuming an attribute level depends on her level of familiarity with it. This allows us to capture potential satiation with a familiar attribute level that has been consumed previously. 2. The consumer accumulates a shopping experience, which also depends on attributelevel familiarity. If shopping experience increases with familiarity, the consumer retrieves more familiar attribute levels more readily during shopping and chooses products based on memory cues. Both the attribute-level product satiation and shopping experience provide a natural way to model variety-seeking behavior commonly found in these product categories. Our model reveals which product attribute becomes more readily satiated and predicts that the consumer is more likely to switch to

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