utility covariances and context effects in conjoint mnp models experimental conjoint choice analysis is among the most frequently used methods for measuring and analyzing consumer preferences the data from such experiments have been typically analyzed with the multinomial logit mnl model however there are several problems associated with the standard mnl model because it is based on the assumption that the error terms of the underlying random utilities are independent across alternatives choice sets and subjects the multinomial probit model mnp is well known to alleviate this assumption of independence of the error terms accounting for covariances in utilities in modeling choice experiments with the mnp is important because variation of the coefficients in the choice model may occur due to context effects previous research has shown that subjects utilities for alternatives depend on the choice context that is the particular set of alternatives evaluated simonson and tversky s tradeoff contrast principle describes the effect of the choice context on attribute importance and patterns of choice they distinguish local contrast effects which are caused by the alternatives in the offered set only and background contrast effects which are due to the influence of alternatives previously considered in choice experiments these effects are hypothesized to cause correlations in the utilities of alternatives within and across choice sets respectively the purpose of this study is to develop an mnp model for conjoint choice experiments this model is important for a more detailed study of choice patterns in those experiments in developing the mnp model for conjoint choice experiments several hurdles need to be taken related to the identification of the model and to the prediction of holdout profiles to overcome those problems we propose a random coefficients rc model that assumes a multivariate normal distribution of the regression coefficients with a rank one factor structure on the covariance matrix of these regression coefficients the parameters in this covariance matrix can be used to identify which attributes and levels of attributes are potentional sources of dependencies between the alternatives and choice sets in a conjoint choice experiment we present several versions of this model moreover for each of these models we allow utilities to be either correlated or independent across choice sets the independent probit ip model is used as a benchmark given the dimensionality of the integrations involved in computing the choice probabilities the models are estimated with simulated likelihood where simulations are used to approximate the integrals involved in the choice probabilities we apply and compare the models in two conjoint choice experiments in both applications the random coefficients mnp model that allows choices in different choice sets to be correlated rc displays superior fit and predictive validity compared with all other models we hypothesize that the difference in fit occurs because the rc model accommodates correlations among choice sets that are caused by background contrast effects whereas the model that treats choice sets as independent irc accounts for local contrast effects only the irc model shows superior model fit compared with the ip model but its predictions are worse than those of the ip model we find differences in the importance of local and background contrast effects for choice sets containing different numbers of alternatives the background contrast effect may be stronger for smaller choice sets whereas the local contrast effect may be stronger for bigger choice sets we illustrate the differences in simulated market shares that are obtained from the rc irc and ip models in three hypothetical situations product modification product line extension and the introduction of a me too brand in all of those situations substantially different market shares are predicted by the three models which illustrates the extent to which erroneous predictions may be obtained from the misspecified irc and ip models

a budget holdback policy for multi item procurement processes this paper is concerned with the development of an optimal budget holdback policy for a multi item procurement process an annual budget is made available for the purchase and resupply of n items the issue is to determine how much of the budget to commit initially and how much to hold back for spot purchases later in the year an analytic solution to the problem is developed for the single item case and compared to the unconstrained newsboy solution in the multi item case we develop stochastic linear programming with resource models under two views of the organization s procurement process the first view is that the overall budget is allocated to the individual item managers and spot purchase monies earmarked for one item cannot be used for another the computational and data requirements for solution of this model are reasonable the second view is that spot buy monies are pooled and allocated to individual items on an as needed basis the linear programming model of this procurement process is mu

a dynamic multinomial probit model for brand choice with different long run and short run effects of marketing mix variables in this paper we propose a dynamic multinomial probit model in order to estimate the long run and short run effects of marketing mix variables on brand choice the latent variables which contain the unobserved perceived utilities follow a first order vector error correction autoregressive process of order 1 with current and lagged explanatory variables the unrestricted autoregressive parameter matrix concerns the intertemporal correlation in perceived utilities of households over purchase occasions and indicates the persistence in brand choice as explanatory variables we consider relative prices and promotional activities like feature and display an important and novel feature of our model is that it allows for different long run and short run effects of promotional activities thereby extending the models that are currently available in the literature additionally to account for different base preferences for brands across households we allow for consumer heterogeneity our application concerns a panel of households choosing among several brands of a fmcg our estimated model turns out to be an improvement over a static model and over a model with only short run effects in terms of in sample fit and out of sample forecasts copyright 2000 john wiley sons ltd

the augmented latent class model incorporating additional heterogeneity in the latent class model for panel data abstract marketing researchers have actively sought to account for the heterogeneity in households preferences and responses to marketing mix elements e g price promotion when analyzing scanner panel data this is because not doing so biases response coefficient estimates that can potentially alter the conclusions drawn from such analyses in this article the authors present an extension to the latent class model lcm that accounts for additional heterogeneity by allowing households preferences to be a continuous mixture of segment level preferences the authors show how their model generalizes the lcm by the mere addition of s 1 parameters where s refers to the number of segments because a limitation of the traditional lcm is that it does not adequately account for heterogeneity in responses across households the authors model mitigates this criticism leveled at the lcm they then compare their model with the traditional lcm and the equivalent continuous mixture random effects models in

monotone stochastic choice models the case of risk and time preferences suppose that when evaluating two alternatives x and y by means of a parametric utility function low values of the parameter indicate a preference for x and high values indicate a preference for y

an empirical analysis of consumer purchase decisions under bucket based price discrimination bucket based price discrimination is a unique price format that involves monthly subscription fees and instantaneous quotas the number of rental products that can be checked out we propose an empirical model in which consumers make dynamic purchase decisions under consumption uncertainty accounting for the constraints imposed by the instantaneous quota applying the model to an online dvd rental data set we find that 1 consumers incur a large disutility 8 from stockout i e unmet consumption needs 2 such a disutility drives consumers overpurchase of the service quota as a way to avoid potential stockout situations and 3 the dynamics of overpurchase are driven by the interplay between trends in consumption needs and the magnitude of consumers plan switching costs we run counterfactual exercises to better understand how the instantaneous quota and stockout risk affect consumers consumption rates purchase decisions and firm profitability we find that the instantaneous quota induces a greater stockout compared with a monthly quota we further demonstrate that the company should recognize the drivers of the dynamics in overpurchase to balance short and long term profitability for example by offering targeted discounts to customers with excess overpurchase

a measurement error approach for modeling consumer risk preference von neumann morgenstern vn m utility theory is the dominant theoretical model of risk preference recently market researchers have adapted vn m theory to model consumer risk preference but most applications assess utility functions by asking just n questions to specify n parameters however any questioning format especially under market research conditions introduces measurement error this paper explores the implications of measurement error on the estimation of the unknown parameters in vn m utility functions and provides procedures to deal with measurement error we assume that the functional form of the utility function but not its parameters can be determined a priori through qualitative questioning we then model measurement error as if question format and other influences cause the consumer to choose the unknown risk parameter from a probability distribution and to make his decisions accordingly we provide procedures to estimate the unknown parameters when the measurement error is eith

endogeneity in brand choice models applications of random utility models to scanner data have been widely presented in marketing for the last 20 years one particular problem with these applications is that they have ignored possible correlations between the independent variables in the deterministic component of utility price promotion etc and the stochastic component or error term in fact marketing mix variables such as price not only affect brand purchasing probabilities but are themselves endogenously set by marketing managers this work tests whether these endogeneity problems are important enough to warrant consideration when estimating random utility models with scanner panel data our results show that not accounting for endogeneity may result in a substantial bias in the parameter estimates

advertising learning and consumer choice in experience good markets an empirical examination this article empirically analyzes different effects of advertising in a nondurable experience good market a dynamic learning model of consumer behavior is presented in which i allow both informative effects of advertising and prestige or image effects of advertising this learning model is estimated using consumer level panel data tracking grocery purchases and advertising exposures over time empirical results suggest that in this market advertising s primary effect was that of informing consumers the estimates are used to quantify the value of this information to consumers and evaluate the welfare implications of an alternative advertising regulatory regime

a heterogeneous conditional logit model of choice aggregate forecasts using mcfadden s conditional logit model of discrete choice harbor the unrealistic implicit assumption of a random utility distribution that is homogeneous across both alternatives and individuals this article relaxes that assumption a choice model is developed that describes the random utility component as a probability mixture model some numerical results illustrate that the derived model is not constrained by the independence of irrelevant alternatives property an experimental test of visual perceptions demonstrates the potential superiority of the model

a probabilistic choice model for market segmentation and elasticity structure marketing scholars commonly characterize market structure by studying the patterns of substitution implied by brand switching though the approach is useful it typically ignores the destabilizing

multi item quick response system with budget constraint quick response mechanisms based on effective use of up to date demand information help retailers to reduce their inventory management costs we formulate a single period inventory model for multiple products with dependent multivariate normal demand distributions and a given overall procurement budget after placing orders based on an initial demand forecast new market information is gathered and demand forecast is updated using this more accurate second forecast the retailer decides the total stocking level for the selling season the second order is based on an improved demand forecast but it also involves a higher unit supply cost to determine the optimal ordering policy we use a computational procedure that entails solving capacitated multi item newsboy problems embedded within a dynamic programming model various numerical examples illustrate the effects of demand variability and financial constraint on the optimal policy it is found that existence of a budget constraint may lead to an increase in the initial order size it is also observed that as the budget available decreases the products with more predictable demand make up a larger share of the procurement expenditure

purchase frequency bias in random coefficients brand choice models conventional random coefficients models of conditional brand choice using panel data ignore the dependence of the random coefficients distribution on the purchase frequencies we show that this leads to biased estimates and propose a conditional likelihood approach to obtain unbiased estimates unlike alternative approaches that require observation of no purchase occasions our proposed method relies only on purchase data furthermore our approach does not require that the researcher specify the distribution of purchase frequencies as a result estimates of the brand choice model are unaffected by misspecification of the model of purchase frequencies we demonstrate the performance of the proposed approach in simulated data and in scanner data we find that results differ substantively from the conventional latent class model in terms of segment membership probabilities segment characteristics and price elasticities

measuring the effects of new brand introduction on inter brand strategic interaction this paper empirically investigates the effects of a new brand introduction on the market structure of a product market i e the nature of competition among existing brands the model is based on the discrete choice multinomial logit model in which the household specific brand intercepts are decomposed into brand locations in attribute space and households importance weights for these attributes the formulation also incorporates the effects of marketing variables on brand choice behavior the relative proximity of brands in the attribute space provides a measure of the intensity of competitive rivalry among brands a new brand introduction results in an additional location in this space three consequences of the introduction are investigated i impact on brand locations of extant brands ii change in the importance weights assigned by households to the attributes iii effects on households sensitivities to marketing activities as some or all of the above could change these three effects result in eight combinations that need to be studied the general model is one in which all three components are allowed to change and this nests the remaining seven specifications the specification that is most consistent with the data can therefore be isolated via a statistical test of nested hypotheses identifying the consequences of the introduction for market structure has implications for managerial action an empirical application of the model to household scanner panel data using liquid laundry detergents is provided

analysing the determinants of performance of best and worst european banks a mixed logit approach abstract using a dataset of 7635 observations on 1384 commercial banks operating in the eu between 1993 and 2001 we utilise a mixed logit model to identify factors that explain the probability of a bank being a best worst performer the empirical evidence confirms the importance of country level characteristics location and legal tradition and firm level features bank ownership balance sheet structure and size specifically smaller sized banks with higher loan intensity and foreign banks from countries upholding common law traditions have a higher probability of best performance

the impact of heterogeneity in purchase timing and price responsiveness on estimates of sticker shock effects the notion that individuals have an internal reference price against which they compare observed prices is well supported by several psychological theories empirically several papers in the marketing literature employing scanner panel data have modeled the impact of reference prices on brand choice via the sticker shock formulation in which consumers evaluate choice alternatives using differences between shelf prices and reference prices most of the studies reported thus far have not accounted for heterogeneity in price response among consumers and have typically imputed reference prices from the shelf prices of brands that a consumer is supposed to have observed on previous purchases in the category since category marketing activity can differentially affect the purchase timing of households we argue that this measure of reference price may follow certain systematic patterns across consumers and when combined with unaccounted for price response heterogeneity may result in a spurious sticker shock effect specifically we show that estimates of sticker shock are biased upward if households that are price sensitive in the brand choice decision are also more responsive to category promotion activity in their purchase timing decision we discuss some general conditions under which the bias occurs and conduct a simulation experiment to confirm our specific hypotheses our simulation results show that changes in purchase timing are a critical determinant of the bias in the sticker shock effect we also show that unaccounted for price response heterogeneity can in itself result in a biased sticker shock parameter however this requires very large differences in price sensitivities across consumers far greater than what is normally observed we develop a hierarchical bayes version of the nested logit model which models heterogeneity via individual level parameters in a continuous random effects framework we estimate the model on scanner panel data from the yogurt and ketchup categories we find in both categories that the 95 probability interval of the posterior distribution of the mean sticker shock coefficient contains the value zero therefore at least for the data used in this study there is no evidence for the sticker shock effect at the aggregate level in contrast the corresponding coefficient from a standard model which ignores this heterogeneity is highly significant and supports the existence of a possibly spurious sticker shock effect consistent with our explanation of the underlying cause of the bias households that are more price sensitive in the choice decision are also found to be more responsive to category promotion activity in their decision to purchase in the category the results highlight the measurement problems associated with imputing reference prices from past prices since the frequency duration and price level of a retailer s promotional program depend on its size and prevalence accurate estimates of the sticker shock effect are essential for formulating optimal promotion strategies an adequate accounting of consumer heterogeneity is critical to this effort

modeling loss aversion and reference dependence effects on brand choice based upon a recently developed multiattribute generalization of prospect theory s value function tversky and kahneman 1991 we argue that consumer choice is influenced by the position of brands relative to multiattribute reference points and that consumers weigh losses from a reference point more than equivalent sized gains loss aversion we sketch implications of this model for understanding brand choice we develop a multinomial logit formulation of a reference dependent choice model calibrating it using scanner data in addition to providing better fit in both estimation and forecast periods than a standard multinomial logit model the model s coefficients demonstrate significant loss aversion as hypothesized we also discuss the implications of a reference dependent view of consumer choice for modeling brand choice demonstrate that loss aversion can account for asymmetric responses to changes in product characteristics and examine other implications for competitive strategy

a random coefficients logit brand choice model applied to panel data a random coefficients logit model that allows for unobserved heterogeneity in brand preferences and in the responses to marketing variables is empirically investigated using household level panel data the unknown underlying distribution of unobserved heterogeneity is approximated by a discrete distribution the results reveal that there is significant unobserved heterogeneity across households and that ignoring its effects results in a downward bias in the parameter estimates of the marketing variables it is therefore important to account for heterogeneity in both preferences and responses in the absence of any a priori knowledge about the nature of heterogeneity across households

investigating heterogeneity in brand preferences in logit models for panel data in analyzing panel data the issue of heterogeneity across households is an important consideration if heterogeneity is present but is ignored in the analysis it will result in biased and inconsi

removing heterogeneity bias from logit model estimation this paper introduces a new estimation procedure for the logit model that is an extension of a conditional estimation procedure developed by chamberlain chamberlain g 1978 on the use of panel data working paper harvard university 1 52 chamberlain g 1980 analysis of covariance with qualitative data rev econom stud 47 225 238 the method eliminates heterogeneity bias which is present in the estimates produced by traditional estimation techniques it improves the goodness of fit and explanatory power of these models while incorporating purchase event feedback

a price discrimination theory of coupons the objective of this paper is to analyze the consumer s decision in electing to use cents off coupons distributed by manufacturers of consumer products arguing that the decision to use coupons is based on the tradeoff between costs of using coupons and the savings obtained it is shown that coupons can serve as a price discrimination device to provide a lower price to a particular segment of consumers based on a price theoretic model it is shown that the users of coupons are more price elastic than nonusers of coupons and that the opportunity cost of time and other household resource variables are determinant factors in consumers decisions implications derived from the model are tested using diary panel data

models of health plan choice this paper considers alternative approaches to accounting for consumer heterogeneity in econometric models of health plan choice in particular we compare and contrast approaches used by health economists and marketing researchers to explicitly allow for consumer heterogeneity in assessment of plan provider quality and characteristics as a result we are able to identify conditions under which approaches utilized by health economists are preferred over those utilized by marketing researchers and vice versa for example we find that in the absence of data on prior year choice which is often the case of models estimated in the health economics literature it would be valuable to estimate random coefficient models which have not been estimated in such works and in the presence of data on prior year choice which is often the case of models estimated in the marketing literature a model wherein consumer characteristics simply interact with plan specific constants and premium and which has not been estimated in that literature offers better fits than random coefficient semi parametric random coefficient and latent class models which have been previously estimated in that literature the model offers important managerial insights useful for targeting benefits of various plans to different customer segments

measuring the impact of promotions on brand switching when consumers are forward looking abstract logit choice models have been used extensively to study promotion response this article examines whether brand switching elasticities derived from these models are overestimated as a result of rational consumer adjustment of purchase timing to coincide with promotion schedules and whether a dynamic structural model can address this bias using simulated data the authors first show that if the structural model is correct brand switching elasticities are overestimated by stand alone logit models a nested logit model improves the estimates but not completely second the authors estimate the models on real data the results indicate that the structural model fits better and produces sensible coefficient estimates the authors then observe the same pattern in switching elasticities as they do in the simulation third the authors predict sales assuming a 50 increase in promotion frequency the reduced form models predict much higher sales levels than does the dynamic structural model the autho

a micro econometric analysis of risk aversion and the decision to self insure this study estimates a von neumann morgenstern utility function using market data and micro econometric methods we investigate the decision whether to purchase insurance against the risk of telephone line trouble in the home using the choices of approximately 10 000 residential customers we determine the shape of the utility function and the degree of risk aversion we find that risk aversion varies systematically in the population and varies with the level of income and that the observed choice behavior is consistent with expected utility maximization we are unable to detect the presence of ambiguity effects or over weighting of low probability events

a stochastic inventory model incorporating intra year purchases and accounting tax incentives it has been observed that firms alter year end inventory policies in response to accounting tax incentives this study proposes a stochastic ordering policy model which quantifies these effects an innovative feature is its use of two decision variables an initial order up to level at the beginning of each year and a desired year end inventory level the ability to place a second order after demand has been assessed allows for an explicit consideration of the effects of tax incentives on order quantity decisions separate formulations are developed for the two most widely used inventory accounting methods lifo and fifo the model provides new implications for choices between lifo and fifo the forms of optimal ordering policies under each and for year end inventory levels a procedure is given for calculating the optimal fifo policy calculating optimal lifo policies is very difficult so myopic approximate policies are given which bound the optimal policy

the value of marketing expertise this paper has three objectives 1 to construct a theoretical model that aids in evaluating marketing expertise 2 to use that theoretical model to identify factors influencing the value of marketing expertise and 3 to empirically test the model by observing how different market conditions influence whether companies emphasize marketing expertise we accomplish these objectives as follows first we use decision theory to find an expression for the expected value of marketing expertise we do not use decision analysis in the normative tradition nor do we assume that firms actually use formal decision analysis rather we assume that firms are rational and that decision theory describes their actions given that assumption we predict how firms should evaluate marketing here marketing expertise helps a firm make better marketing decisions at least on average hence the value of marketing expertise increases as marketing decisions become more important second consistent with decision theory

a likelihood approach to estimating market equilibrium models this paper develops a new likelihood based method for the simultaneous estimation of structural demand and supply models for markets with differentiated products we specify an individual level discrete choice model of demand and derive the supply side assuming manufacturers compete in prices the proposed estimation method considers price endogeneity through simultaneous estimation of demand and supply allows for consumer heterogeneity and incorporates a pricing rule consistent with economic theory the basic idea behind the proposed estimation procedure is to simulate prices and choice probabilities by solving for the market equilibrium by repeating this many times we obtain an empirical distribution of equilibrium prices and probabilities the empirical distribution is then smoothed and used in a likelihood procedure to estimate the parameters of the model the advantage of this method is that it avoids the need to perform a transformation of variables if the tastes of consumers are independent across market periods our approach yields maximum likelihood estimates otherwise it yields consistent but not fully efficient partial likelihood estimates

taste variation in discrete choice models this paper develops an extension of the classical multinomial logit model which approximates a class of models obtained when there is uncontrolled taste variation across agents and choices in addition to the stochastic noise inherent in the logit model unlike semiparametric and parametric alternatives the extended logit model is easy to estimate even when there are many potential choices unlike parametric alternatives it does not require the specification of a distribution of varying tastes the extended logit model can give a quick indication of the impact of taste variation on estimates and it generates estimates of the covariances of the taste shifters it can be used as an exploratory device en route to the construction of a model incorporating a particular form of random taste variation and it can be used to determine whether such effort is required at all when the amount of taste variation is not excessive the approximate model can be adequate itself the model nests the conventional logit model which leads to a misspecification diagnostic a method for estimating the model using conventional logit model software is proposed asymptotic properties of estimators are derived and an application is presented

note consumer store choice and choice set definition consumer store choice results from a process whereby information on various alternatives is evaluated by the consumer prior to the selection of one of these alternatives in the application of store choice models it is often assumed that the information processing strategy underlying store choice is a simultaneous one in which all possible alternatives are evaluated by an individual a competing assumption increasingly recognized in aspatial choice is that individuals initially evaluate clusters of alternatives and then only evaluate alternatives within a chosen cluster the nested logit model is a well known formulation for capturing this type of choice process this paper adds to the above understanding of consumer spatial choice in the following ways i it describes an alternative to the nested logit model known as a competing destinations model which can be used to model hierarchical spatial choice processes while this model has been described previously in the geography and planning literature this paper contains a novel derivation of it from within the random utility maximization framework ii a general choice formulation is derived from which the logit nested logit and competing destinations models can be obtained this allows the behavioral assumptions embedded in each model to be seen clearly and it allows a comparison of the nested logit and competing destinations formulations the latter is shown to be preferable in most spatial choice situations in particular the latter can be used when restricted choice sets are fuzzy iii the errors that arise when spatial choice results from a hierarchical process and when a simultaneous choice structure is assumed are demonstrated and are related to the presence of consumer competition agglomeration effects between stores which are thought to be important in store choice

latent homophily or social influence an empirical analysis of purchase within a social network consumers who are close to one another in a social network often make similar purchase decisions this similarity can result from latent homophily or social influence as well as common exogenous factors latent homophily means consumers who are connected to one another are likely to have similar characteristics and product preferences social influence refers to the ability of one consumer to directly influence another consumer s decision based upon their communication we present an empirical study of purchases of caller ring back tones using data from an asian mobile network that predicts consumers purchase timing and choice decisions we simultaneously measure latent homophily and social influence while also accounting for exogenous factors identification is achieved due to our dynamic panel data structure and the availability of detailed communication data we find strong influence effects and latent homophily effects in both the purchase timing and product choice decisions of consumers this paper was accepted by sandra slaughter information systems

testing for choice dynamics in panel data this article applies different approaches to distinguish state dependence from unobserved heterogeneity and serial correlation and hence test for state dependence in consumer brand choices first we apply a simple method proposed by chamberlain which involves lagged exogenous variables only second we also estimate a lagged dependent variable specification proposed by wooldridge third we use the estimation approach suggested by wooldridge to estimate a model with both lagged dependent and exogenous variables to distinguish between the two different sources of choice dynamics state dependence and lagged effects of the exogenous variables our analysis reveals that the best approach is to use models with both lagged dependent and exogenous variables our findings include strong evidence for state dependence in five out of the six product categories studied in this article

new empirical generalizations on the determinants of price elasticity abstract the importance of pricing decisions for firms has fueled an extensive stream of research on price elasticities in an influential meta analytical study tellis 1988 summarized price elasticity research findings until 1986 however empirical generalizations on price elasticity require modifications because of 1 changes in market characteristics i e characteristics of brands product categories and economic conditions and 2 changes in the research methodology used to assess price elasticities therefore the authors present a meta analysis of price elasticity with new empirical generalizations on its determinants across a set of 1851 price elasticities based on 81 studies the average price elasticity is 2 62 a salient finding is that over the past four decades sales elasticities have significantly increased in magnitude whereas share and choice elasticities have remained fairly constant the authors find that accommodating price endogeneity has a strong magnitude increasing impa

the bright side of loss aversion in dynamic and competitive markets a well established phenomenon of consumer buying behavior is that consumers evaluate prices relative to a reference point and exhibit loss aversion i e their propensity to buy is more negatively affected by prices above the reference point than it is positively affected by prices below the reference point the objective of this paper is to analytically examine how the competitive strategy and profitability of firms are affected by the presence of consumer loss aversion in the price dimension although we assume that consumer loss aversion increases consumer propensity to search for lower prices we find that it does not necessarily lead to lower prices or profits when firms compete over multiple periods and when the consumer reference price in subsequent periods is affected by current prices specifically consumer loss aversion could lead to higher prices and profits when consumer valuation is sufficiently high relative to search costs and the proportion of consumers with positive search costs is in an intermediate range we also show that when forward looking firms incorporate the negative effect of price promotions on future profits the equilibrium range of price promotions may actually increase

parameter bias from unobserved effects in the multinomial logit model of consumer choice abstract over the past two decades validation of choice models has focused on predictive validity rather than parameter bias in real world validation of choice models true parameter values are unknown so examination of parameter bias is not possible in contrast the main focus of this study is parameter bias in simulated scanner panel choice data with known parameter values study of parameter bias enables the assessment of a fundamental issue not addressed in the choice modeling literature the extent to which the logit choice model is capable of distinguishing unobserved effects that give rise to persistence in observed choices e g heterogeneity and state dependence although econometric theory provides some information about the causes of bias the extent of such bias in typical scanner data applications remains unclear the authors present an extensive simulation study that provides information on the extent of bias resulting from the misspecification of four unobserved effects that receive fr

an empirical comparison of logit choice models with discrete versus continuous representations of heterogeneity abstract 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

an empirical comparison of the validity of a neural net based multinomial logit choice model to alternative model specifications applications of choice models to brand purchase data as a rule specify a linear deterministic utility function we estimate deterministic utility by means of a neural net able to approximate any continuous multivariate function and its derivatives to a desired level of precision we compare this model to related alternatives both with linear and nonlinear utility functions alternatives with nonlinear utility functions are based on generalized additive modeling and taylor series expansion respectively we analyze purchase data of the six largest brands in terms of market share for two product groups neural choice models outperform the alternative models studied w r t posterior probabilities they also attain the best crossvalidated log likelihood values these results demonstrate that the increase in complexity caused by the neural choice model is justified by higher validity in the empirical study the neural choice models imply elasticities different from those obtained by linear utility multinomial logit models for several predictors neural choice models discover inversely s shaped saturation and interaction effects on utility

modeling multiple sources of heterogeneity in multinomial logit models methodological and managerial issues we offer a framework to specify and estimate various sources of heterogeneity in multinomial logit brand choice models we let each brand specific intercept and each parameter of the explanatory variable vector vary randomly across households in addition we distinguish loyal households from the rest our results suggest that incorporating multiple sources of heterogeneity improves the model fit and suggests higher impact of marketing mix elements on brand choice we highlight the importance of incorporating multiple sources of heterogeneity the model we implement yields valuable managerial insights

a model of brand choice and purchase quantity price sensitivities many consumer decisions involve a discrete choice and a continuous outcome examples of such decisions are whether to own a home or rent one and how much to spend which brand of orange juice to buy and how many ounces to buy in cases like these the choice decision is typically modeled separately say using a logit model and the continuous outcomes modeled separately using regression analysis however the continuous outcomes may not be independent of the discrete choice and vice versa and modeling the two decisions independently can lead to inefficient choice parameter estimates and biased and inconsistent regression parameter estimates in this paper we present a methodology from the limited dependent variable literature to model the dependence between the choice and quantity decisions our substantive interest is in the role of price in the choice and quantity decisions when choosing among alternatives we argue that consumers consider prices of all the competitive brands in the quantity decision on the other hand only the price of the chosen alternative is expected to impact how much of the alternative is purchased the analysis of three brands using disaggregate level panel data strongly supports our hypothesis about the role of competitive prices in the choice and quantity decisions

modeling preference and structural heterogeneity in consumer choice consumer heterogeneity is fundamental to the marketing concept providing the basis for market segmentation targeting and positioning as well as micro marketing substantial effort has already been devoted to incorporate heterogeneity in brand choice models however most of the research in this area has focused on differences in preferences or tastes across consumers in contrast limited attention has been given to the possibility that consumers might also differ in the process they follow when making choices failure to account for either form of consumer heterogeneity may lead to misinterpretations of market structure and market segments as demonstrated in our simulations our main research objective in this paper is to account for these two forms of consumer heterogeneity with an integrated model we develop a choice model that simultaneously identities consumer segments on the basis of their preferences response to the marketing mix and choice processes this choice model is a finite mixture of nested logit models that incorporates the mixture of multinomial logits as a special case one main limitation of the now popular multinomial logit model is that choices by each consumer are assumed to be independent from irrelevant alternatives as a consequence that model predicts that within each segment a brand has exactly the same cross elasticities upon every competitor in contrast our mixture of nested logits allows for violations of the iia property within each segment leading to more realistic patterns of brand competition where one brand draws differently from each competitor depending on the choice process used by members of the segment in addition to the flexible combination of choice processes and preference structures our model also incorporates a latent class of hard core loyal consumers who are expected to always buy the same brand and are thus insensitive to price and promotions application of our model to household scanner data in the peanut butter category led to four types of segments a hard core loyals who do not respond to price and promotions b brand type for which the brand choice precedes the choice of product form creamy vs crunchy c form type members of these segments first choose the product form and then decide for the brand peter pan jif skippy or store brand d iia type members of these segments make choices according to the iia property our results showed a large 14 hard core loyal segment we also identified three segments of the brand type one segment of the form type and three iia type segments all these segments also differ in their preferences for brands and product forms one useful feature of our model is that it allows for cross elasticity structures with non proportional draw even when computed within a homogeneous segment most importantly the proposed model allows for more complex cross elasticity structures within a segment rather than constraining the elasticities to any particular pattern a priori these cross elasticity patterns imply distinct competitive environments within each segment leading to different price and promotion strategies in each segment for example a promotion by jif crunchy to a brand type segment is more likely to cannibalize on shares of the creamy form of the same brand than to draw from competitors if offered to a form type segment the same promotion is more likely to draw shares from competitors because of its finite mixture formulation our proposed model shares the same limitations of this category of models for example maximum likelihood estimation of the model may lead to local optima thus requiring a multiple number of random starts in order to increase the chances of finding the model that best fits to the data our model also requires specification of the number and type of segments in each particular application several criteria are available to select the number of segments however the specification of choice processes must be based on a heuristic procedure or on the basis of prior knowledge

a nonparametric density estimation method for brand choice using scanner data nonparametric density estimation using a kernel method is proposed to model consumer brand choice recent availability of large scanner panel data allows the use of nonparametric approach which has few or at least fewer underlying assumptions and affords greater structural flexibility by removing as many assumptions as possible the author constructs the ultimate nonparametric model radically departing from the traditional approaches to highlight the differences in implementation and performance the proposed model does not involve either parameters that approximate certain distributions as in stochastic models or latent concepts such as utility as in utility maximization models the performance criteria include prediction of market response and brand choice share tracking and robustness under violation of various assumptions involved in parametric choice models such as correlated disturbance and misspecification the method is compared with a popular parametric counterpart the multinomial logit model on simulated and actual scanner panel data the paper emphasizes the conceptual importance of the nonparametric approach by discussing its advantages limitations and its complementary role in developing refining and diagnosing parametric models this perspective affords insight to modeling philosophy and suggests the possibility of a hybrid approach

an alternative explanation for lower repeat rates after promotion purchases several authors have observed that average repeat rates are lower after a promotion purchase one interpretation of this result is that a media coupon or cents off promotion undermines the consumer

observed and unobserved preference heterogeneity in brand choice models this paper extends the scanner based choice literature by explicitly incorporating individual level brand preference data we illustrate our model using a unique data set that combines survey and scanner data collected from the same individuals the addition of individual specific brand preference information significantly improves fit and prediction furthermore this observed heterogeneity better explains choice than does unobserved heterogeneity in the standard scanner model s parameters more importantly we find that the standard model underestimates the importance of consumers brand preferences and overestimates both brand loyalties and price sensitivities brand loyalty is overestimated because models without preference information confound state dependence heterogeneity and preference effects price sensitivities are inflated because the average preference based consumer is implicitly assumed to be more willing to switch from his preferred brand than is the real preference based consumer further standard models overestimate the heterogeneity in price and loyalty sensitivities and misidentify both price and loyalty sensitive consumers the managerial implications of our findings and the applicability of our methodology when survey data are collected infrequently and for only a subsample of consumers are pursued we demonstrate that even under these circumstances better populationwide pricing and promotion decisions are identified and more accurate targeting results

reference effects of price and promotion on brand choice behavior when consumers are exposed to pricing and promotional activity by frequently purchased packaged goods they may develop expectations that are used as points of reference in evaluating future activi

accounting for heterogeneity and nonstationarity in a cross sectional model of consumer purchase behavior when calibrating a brand choice model cross sectionally a measure of brand loyalty is often introduced into the utility function to account for differences in utility across households and over time one of the most widely used measures of brand loyalty proposed by guadagni and little 1983 is an exponential smoothing model of past choice behavior by the household in this study we argue that the exponential smoothing model of brand loyalty cannot properly distinguish between sources of variation in utility due to heterogeneity across households and sources of variation due to nonstationarity within household over time we introduce a new measure of brand loyalty derived from a nonstationary dirichlet multinomial choice model in which heterogeneity and nonstationarity are handled distinctly

a simultaneous approach to the whether what and how much to buy questions consumer s purchase decisions of whether to buy what to buy and how much to buy are examined simultaneously based on a consumer utility maximization problem an unobservable threshold price about which a consumer pivots from nonpurchase to purchase can be deduced with any model specification the interrelationships between purchase decisions can be explicitly derived the model is applied to coffee purchasing data in addition by suppressing the nonpurchase options the model is compared with an alternative approach appearing in the marketing literature

heterogeneity in price rigidity evidence from a case study using microlevel data we combine two data sets to study price rigidity the first consists of weekly time series of retail wholesale and spot prices for twelve products these time series contain two exogenous cost shocks we find that prices exhibit more rigidity in response to the second shock than the first the second data set consists of all publicly available information about the shocks content analysis of these information reveals that the first shock is larger and more persistent and the market has more information on it than the second we conclude therefore that prices are more flexible in response to cost shocks that are larger that are more persistent and on which market participants have more information

modeling the distribution of price sensitivity and implications for optimal retail pricing this article focuses on the distribution of price sensitivity across consumers we employ a random coefficient logit model in which brand specific intercepts and price slope coefficients are allowed to vary across households the model is estimated with panel data for two product categories the implications of the estimated model are deduced through an optimal retail pricing analysis that combines the panel data with chain level cost figures we test parametric distributional assumptions using semiparametric density estimates based on series expansions

price flexibility in channels of distribution evidence from scanner data in this study we empirically examine the extent of price rigidity using a unique store level time series data set consisting of i actual retail transaction prices ii actual wholesale transaction prices which represent both the retailers costs and the prices received by manufacturers and iii a measure of manufacturers costs for 12 goods in two widely used consumer product categories we simultaneously examine the extent of price rigidity for each of the 12 products at both final goods and intermediate goods levels we study two notions of price rigidity employed in the existing literature i the frequency of price changes and ii the response of prices to exogenous cost changes we find that retail prices exhibit remarkable flexibility in terms of both notions of price rigidity i e they change frequently and they seem to respond quickly and fully to cost changes furthermore we find that retail prices respond not just to their direct costs but also to the upstream manufacturers costs which further reinforces the extent of the retail price flexibility at the intermediate goods level of the market in contrast we find relatively more evidence of rigidity in the response of manufacturers prices to cost changes this despite the fact that wholesale prices change frequently and therefore exhibit flexibility according to the first notion of price rigidity

endogeneity and individual consumer choice abstract in many markets there is likely to be correlation both across periods in terms of the choices of one consumer and across consumers in terms of their choices in each period the former is caused by consumer heterogeneity and the latter may be the result of demand common shocks across consumers furthermore if firms partially observe these common shocks their market decisions may end up being endogenous and correlated with the common shocks because researchers cannot typically fully observe consumer heterogeneity and the common shocks the estimation method must account for the endogeneity of firms decisions in this article the authors present a test for endogeneity under unobserved consumer heterogeneity and common shocks which is based on a quasi likelihood estimation method to estimate the model parameters consistently when endogenous firm behavior unobserved heterogeneity and common shocks are present the test examines the differences in general method of moments coefficient estima

success in high technology markets is marketing capability critical we propose a conceptual framework with the resource based view rbv of the firm as its theoretical underpinning to explain interfirm differences in firms profitability in high technology markets in terms of differences in their functional capabilities specifically we suggest that marketing r d and operations capabilities along with interactions among these capabilities are important determinants of relative financial performance within the industry this paper contributes to the rbv literature by proposing the input output perspective to conceptualize the notion of capabilities specifically this approach entails modeling a firm s functional activities viz marketing r d and operations as transformation functions that relate the productive factors resources to its functional objectives if the firm were to deploy these resources most efficiently any underattainment of the functional objective then is attributable to functional inefficiency or equivalently to a lower functional capability of the firm the input output conceptualization of a firm s capabilities is then estimated using the stochastic frontier estimation sfe methodology sfe provides the appropriate econometric technique to empirically estimate the efficient frontier and hence the level of efficiency achieved by the various firms our study contributes to a number of literatures both methodologically and substantively first it contributes both conceptually and methodologically to the rbv literature conceptually our study suggests that firm capabilities can be viewed in an input output framework methodologically the study suggests the use of stochastic frontier estimation to operationalize and estimate firm capabilities this methodology is to the best of our knowledge the first to allow the researcher manager to infer capabilities from archival data substantively our study contributes to the literature on market orientation by suggesting that a stronger market orientation of a firm should be reflected in a higher marketing capability it also adds to the literature on design for manufacturability by explicating the complementarity among the various functional capabilities and offering empirical evidence on their relative importance in influencing a firm s performance finally our study builds on prior literature that has highlighted the importance of marketing r d coordination as important determinants of new product development and success we highlight below some of our main findings 1 a strong base of innovative technologies enhances a firm s sales by favorably influencing consumers expectations about the externality benefits associated with its product this suggests that a past track record of consistent innovation is a credible signal to current and potential customers of the firm s continued excellence in a technologically evolving market given the importance of influencing customers managers need to tailor their marketing activities around the need to inform customers of the technological excellence of their firm thus customers need to be informed of the innovative technologies that the firm possesses and of the future r d initiatives undertaken by it similarly any potential applications of innovative technology developed by the firm and of technologies under development should be emphasized 2 marketing capability has its greatest impact on the quality adjusted innovative output for firms that have a strong technological base in other words firms with a strong r d base are the ones with the most to gain from a strong marketing capability 3 marketing capability strongly influences the width of applicability of innovations i e a firm s marketing capability enhances its ability to generate innovative technologies that have applications across a range of industries this result carries a strong message for managers a strong market orientation is one of the most fertile sources of ideas for innovation thus marketing needs to be involved from the beginning of the innovation process namely right at the stage when technological ideas are being generated 5 the most important determinant of a firm s performance is the interaction of marketing and r d capabilities this supports the assertion that firms in high technology markets need to excel at two things the ability to come up with innovations constantly and the ability to commercialize these innovations into the kinds of products that capture consumer needs and preferences this finding offers further evidence on the importance of coordination between r d and marketing as suggested in the extant marketing literature finally using archival data our methodology can be used to benchmark a firm s capabilities with other firms in the industry along various functional dimensions this would be an important step in making more informed resource allocation decisions thus the firm can spend more money on those capabilities where it most lags the competition or on those capabilities that are shown to have the maximum impact on firm performance

predictive testing of consumer choice models not subject to independence of irrelevant alternatives the probabilistic modeling of the relationship between objective or perceived characteristics of multiattribute alternatives and consumer choice is receiving increased attention in marketing and ot

reassessing brand loyalty price sensitivity and merchandising effects on consumer brand choice this article reports the results of an empirical study of household brand choices over four scanner panel data sets the study uses a random effects autocorrelated logistic regression model the analysis presents evidence that the brand choice process is not zero order in addition the influence of in store displays and feature advertisements on switching is shown to be about two to three times more effective than estimates obtained from previous studies finally the analysis indicates systematic differences between frequent and infrequent buyers of products in terms of their sensitivity to price displays and feature advertising

a multiplicative fixed effects model of consumer choice the issue of consumer heterogeneity in discrete choice analysis has been attracting much attention recently research has suggested that heterogeneity can result in biased parameter estimates which in turn can lead to incorrect conclusions among the many methods proposed in the literature to handle heterogeneity fixed effects models seem to be the most attractive from a substantive point of view however in order to provide consistent estimates these models typically require long purchase histories this difficult constraint has prevented their widespread use in this paper we propose a new model which offers the benefits of fixed effects models without requiring long purchase histories our approach differs from the classic formulation in two ways first we calibrate the common and fixed effects sequentially rather than simultaneously this two stage estimation permits us to obtain unbiased estimates of the common parameters even when the sample has households with very few observations second we incorporate the fixed effects in a multiplicative rather than in an additive form our assumptions regarding the error term result in a probit model with heteroskedastic and temporally correlated random utilities we develop a method of moments based estimator to calibrate parameters of models with this type of error structure this procedure exploits the property that the method of moments yields estimates which are asymptotically as efficient as the maximum likelihood estimates given an appropriate starting point and a matrix of instruments this is a very general procedure in that its mechanics are not tied to the proposed model it can be readily applied to other problems where probit models are used to analyze data with serial correlation this is a methodological contribution of the paper the model and estimation procedure are illustrated on the a c nielsen scanner data base for the liquid detergent category our results indicate that the proposed model can provide a better fit than random effects and loyalty based models and better predictive performance than random effects models from a substantive perspective our analysis provides the following findings a the distribution of price sensitivities has a low variance thus most households have very similar price sensitivity in this category b the promotion response distribution has a high variance suggesting that there are some households that are highly responsive to promotions while there are quite a few households that are less influenced by promotions c preferences and price sensitivity have a negative covariance which is an indication that a reduction in preference for a brand is associated with an increase in price sensitivity and vice versa d preferences and promotion response have a positive covariance thus as the preference for a brand increases response to promotions by the brand also increases and e price sensitivity and promotion response have a negative covariance implying that as households become more price sensitive purchases may be driven more by price than by promotions in other words rather than responding to promotions such as displays and features as signals of a reduced price price sensitive households actively evaluate prices in arriving at a choice the proposed approach can be used by theoretical as well as applied researchers of brand choice behavior specifically estimates of fixed effects provided by the model can be used to cluster households into groups with similar parameters profiling the resulting groups in terms of demographics would provide interesting insights from a theoretical perspective regarding the household characteristics that are associated with different types of response behavior it would also help managers to better target their brands using the proposed approach for cross category analyses of fixed effects would be an interesting area of future research for instance given a sample of households and their purchase histories in multiple categories the proposed model can be used to explore the relationship between household behavior and category characteristics in particular issues such as the following can be investigated by comparing household level estimates across categories a are households that are highly price sensitive in one category also price sensitive in other categories b what household characteristics are associated with high low price sensitivity or high low promotion response across categories and c in what types of categories are households likely to exhibit high low price sensitivity or high low promotion response

price uncertainty and consumer search a structural model of consideration set formation we offer an econometric framework that models consumer s consideration set formation as an outcome of her costly information search behavior because frequently purchased products are characterized by frequent price promotions of varying depths of discounts a consumer faces significant uncertainty about the prices of the brands the consumers engage in a fixed sample search strategy that results in their discovering the posted prices of a subset of the available brands this subset is referred to as the consumer s consideration set the proposed model is estimated using the scanner data set for liquid detergents our key empirical results are i consumers zincur significant search costs to discover the posted prices of the brands ii whereas in store displays and feature ads do not influence consumers quality perceptions of the brands they significantly reduce search costs for observing the prices of the brands iii per capita income of consumer s household significantly increases her search costs and iv the consumers price sensitivity is seriously underestimated if we were to assume that consumers get to know all the posted prices at zero cost the proposed model is also estimated for the ketchup category to enable us to do cross category comparisons of consumers price search behavior

a model of balanced choice behavior in some product categories consumers appear to alternate among items in a consistent and purposeful though not entirely deterministic fashion such alternation occurs even in the absence of any apparent external influences on choice to explain such balanced choice behavior we present a model based upon the premise that past consumption influences the utility of items on subsequent choice occasions individuals may seek variety on certain product attributes and exhibit loyalty toward others the model specification yields a logit choice model which may be calibrated using consumption diary panel data at the individual or cross sectional level

similarities in choice behavior across product categories differences between consumers in sensitivity to marketing mix variables have been extensively documented in the scanner panel data all studies of consumer heterogeneity focus on a specific category of products and ignore the fact that the purchase behavior of panel households is often observed simultaneously in multiple categories if sensitivity to marketing mix variables is a common consumer trait then one should expect to see similarities in sensitivity across multiple categories the goal in this paper is to measure the covariance of both observed linked to measured characteristics of households and unobserved heterogeneity in marketing mix sensitivity across multiple categories measurement of correlation in sensitivities across categories will serve to guide the interpretation of the literature on household heterogeneity if there is a large correlation one can be more confident that sensitivity to marketing variables is a fundamental household pr operty and not simply a category specific anomaly detection of correlation in sensitivities across categories requires an appropriate methodology that can handle the high dimensional covariance structures and properly account for uncertainty in estimatio n for example a simple approach might be to fit a brand choice model to each of the available categories in turn ignoring the data in the other categories for each category household parameter estimates could be obtained for the parameters corresponding to price display and feature sensitivity these parameter estimates could be viewed as data and the correlations across categories could be computed such a procedure could induce a downward bias in the estimation of correlation dueto the independent sampling errors which are present in each parameter estimate we develop a hierarchical model structure that introduces an explicit correlation structure across categories and utilizes the data in multiple categories at the same time to reduce the size of the covariance matrix we use a variance components approach we introduce household specific demographic variables to decompose the correlation across categories into that which can be ascribed to observable and unobservable sources shopping behavior variables such as shopping frequency and market basket size as well as intensity of shopping in a category are also included in the model using data on five categories we find substantial and statistically important correlations ranging from 32 for price sensitivities to 58 for feature sensitivity these correlations are much larger than the correlations obtained with the state of the art techniques available prior to our work we attribute our ability to detect substantial correlations to our method which involves the joint use of multiple category data in a parsimonious and efficient manner unlike previous studies with panel data household demographic variables are found to be strongly related to price sensitivity higher income households are less price sensitive and large families are more price sensitive shopping behavior variables are also important in explaining price sensitivity households that visit the store often are more price sensitive households with larger market baskets are less price sensitive confirming the view of bell and lattin 1998 heavy user households tend to be both less price sensitive and less display sensitive the evidence presented here of substantial correlations validates in part the notion that sensitivity to marketing mix variables is a consumer trait and is not unique to specific product categories it also opens the possibility of using information across categories in making inferences about consumer brand preference and marketing mix sensitivity providing a richer source of information for target marketing

optimal conflict in preference assessment conflict arises indecision making when the choice alternatives present strong advantages and disadvantages over one another that is when the trade offs involved are large conflict affects human response to choice in particular it increases decision difficulty and response unreliability on the other hand larger trade offs i e higher conflict reveal more information about an individual s preferences and mitigate the influence of measurement unreliability on preference model estimation this suggests somewhat counterintuitively that there may exist some optimal level of conflict for efficient measurement of preferences how to determine this level this issue is examined from behavioral and analytical angles we outline a general analysis of the interaction between trade off size and modeling accuracy and demonstrate its application on a simple example the kind of analysis developed here can be conveniently implemented in a computer spreadsheet and would be especially valuable when large amounts of preference data are to be collected as in consumer preference studies experimental research and contingent valuation surveys

model of brand choice with a no purchase option calibrated to scanner panel data abstract in usual practice researchers specify and estimate brand choice models from purchase data ignoring observations in which category incidence does not occur i e no purchase observations this practice can be problematic if there are unobservable factors that affect the no purchase and the brand choice decisions when such a correlation exists it is important to model simultaneously the no purchase and the brand choice decisions the authors propose a model suitable for scanner panel data in which the no purchase decision depends on the price feature and display of each brand in the category and on household stock of inventory they link the no purchase model to the brand choice outcome through marketing mix covariates and through unobservables that affect both outcomes the authors assume that model parameters are heterogeneous across households and allow for a flexible correlation structure between the coefficients in the no purchase model and those in the brand choice model the model fo

brand loyalty and market equilibrium two concepts of brand loyalty are defined inertial brand loyalty resulting from time lags in awareness and cost based brand loyalty resulting from intertemporal utility effects their market level implications are formally derived in a continuous time model it is found that inertial brand loyalty leads to equilibria with price dispersion while cost based brand loyalty also may allow single price equilibria in all cases as brand loyalty vanishes so does the difference between the average trading price and the price which obtains with no brand loyalty consistent with empirical results the theory predicts that the relationship between market share and performance is positive in cross sectional studies but flat in time series studies the theory is also consistent with the view that market share is an asset in itself after developing the theory several strategic implications are drawn in the end some questions for further theoretical and empirical research are raised

the impact of prior decisions on subsequent valuations in a costly contemplation model this paper develops and tests a model of how recall of information from past decisions affects subsequent related decisions a boundedly rational individual has to determine her willingness to pay for a good that she previously considered purchasing at a given price or provide valuations for a set of goods that she previously ranked in order of preference the individual is ex ante uncertain about her utility from consumption of the goods and can exert costly cognitive effort to reduce this uncertainty we show that incorporating information from a prior decision has three primary effects a valuations are expected to exhibit higher variance in particular the spread of valuations between the most and least preferred alternatives increases b decision makers will in expectation exert more effort during the valuation phase and c the relative impact of prior decisions on valuation spread increases the more each attribute contributes to overall utility the model predictions are then tested in a series of controlled lab experiments