Knowledge that Transforms

In recent years, interest in category management has surged, and as a consequence, large retailers now systematically review their product assortments. Variety is a key property of assortments. Assortment variety can determine consumers' store choice and is only gaining in importance with today's increasing numbers of product offerings. To support retailers in managing their assortments, insight is needed into the influence of assortment composition on consumers' variety perceptions, and appropriate measures of assortment variety are required. This paper aims to extend the assortment variety model recently proposed by Hoch et al. (1999) in Marketing Science. It conceptualizes assortment variety from an attribute-based perspective and compares this with the productbased approach of Hoch, Bradlow, and Wansink (HBW). The attribute- based approach offers an alternative viewpoint for assortment variety. Attribute- and product-based approaches reflect basic conceptualizations of assortment variety that assume substantially different perception processes: a consumer comparing products one-by-one versus a consumer examining attributes across products in the assortment. While the product-based approach focuses on the dissimilarity between product pairs in an assortment, the attribute-based approach that we propose focuses on the marginal and joint distributions of the attributes. We conjecture and aim to show that an attribute-based approach suffices to predict consumers' perceptions of assortment variety. In operationalizing the attribute-based approach, two measures of assortment variety are described and compared to productbased measures. These two measures relate to the dispersion of attribute levels, e.g., if all products have the same color or different colors, and the dissociation between attributes, e.g., if product color and size are unrelated. The ability of product-based and attributed- based measures to predict consumers' perceptions of assortment variety is assessed. The product-based measures (Hamming) tap the dissimilarity of products in an assortment across attributes. The attribute-based measures tap the dispersion of attribute levels across products (Entropy) and the dissociation between product attributes (1–Lambda) in an assortment. In two studies, we examine the correlations between these measures in a well-behaved environment (study 1) and the predictive validity of the measures for perceived variety in a consumer experiment (study 2). Study 1, using synthetic data, shows that the attribute-based measures tap specific aspects of assortment variety and that the attribute-based measures are less sensitive to the size of assortments than product-based measures are. Whereas HBW focus on assortments of equal size, study 1 indicates that an extension to assortments of unequal size results in summed Hamming measures that correlate highly with assortment size. The latter is important when assortments of different size are compared. Next, we examine how well the measures capture consumers' perception of variety. Study 2, a consumer experiment, shows that the attribute-based measures account best for consumers' perceptions of variety. Attribute- based measures significantly add to the prediction of consumers' perceptions of variety, over and above the product-based measures, while the reverse is not the case. Interestingly, this study also indicates that assortment size may not be a good proxy for perceived assortment variety. The findings illustrate the value of an attribute-based conceptualization of assortment variety, since these measures (1) correlate only moderately with assortment size and (2) suffice to predict consumers' perceptions of assortment variety. In the final section we briefly discuss how attribute-based and product-based measures can be used in assortment management, and when productand attribute-based approaches may predict consumers' variety perceptions. We discuss how an attribute-based approach can identify which attribute levels and attribute combinations influence consumers' perceptions of variety most, while a productbased approach can identify influential products. Both approaches have applications in specific situations. For instance, an attributebased approach can identify influential attributes in an ordered, simultaneous presentation of products, while a product-based approach can assess the impact of sequential presentations of products better. In addition, we indicate how the random-intercept model estimated in study 2 can be further extended to capture the influence of, e.g., consumer characteristics.

The identification of geographic target markets is critical to the success of companies that are expanding internationally. Country borders have traditionally been used to delineate such target markets, resulting in accessible segments and cost efficient entry strategies. However, at present such "countries-as-segments" strategies may no longer be valid. In response to the accelerating trend toward global market convergence and within-country fragmentation of consumer needs, cross-national consumer segmentation is increasingly used, in which consumers in different countries are grouped based on the similarities in their needs, ignoring the country borders. In this paper, we propose new methodology that helps to improve the identification of spatial segments by using information on the location of consumers. Our methodology identifies spatial segments based on consumer needs and at the same time uses spatial information at the subcountry level. We suggest that segments of consumers are likely to demonstrate spatial patterns and develop a hierarchical Bayes approach specifying several types of spatial dependence. Rather than assigning consumers to segments, we identify spatial segments consisting of predefined regions. We develop four models specifying different types of spatial dependence. Two models characterize situations of spatial independence and countries-as-segments, which represent existing approaches to international segmentation. The other two models accommodate spatial association within and spatial contiguity of segments and are new to the segmentation literature. The models account for within-segment heterogeneity in multiattribute-based segmentation, covering numerous applications in response-based market segmentation. We show that the models can be estimated using Gibbs sampling, where for the spatial contiguity model, a rejection sampling procedure is proposed. We conduct an analysis of synthetic data to assess the performance of the most restrictive spatial segmentation model in situations where spatial patterns do or do not underlie the data-generating process. Data for which the true properties are known were analyzed with models of spatial contiguity and spatial independence of segments. The results indicate that a substantial improvement in parameter recovery may be realized if a spatial pattern underlies the data-generating process, but that the spatial-independence model may provide a better alternative when this is not the case. We empirically illustrate our approach in the setting of international retailing, using survey data collected among consumers in seven countries of the European Union. A store image measurement instrument was used. This instrument is based on the multiattribute model of store image formation, with overall evaluations of stores as a dependent variable and image perceptions as predictor variables. The segmentation basis consists of (latent) importances of store image attributes, i.e., product quality, service quality, assortment, pricing, store atmosphere, and location. We argue that store image attribute importances are likely to display spatial variation and expect spatial concentration of segments, or even contiguity, to occur. We apply and compare the four spatial segmentation models to the store image data. The countries-as-segments model receives lowest support from the data, less than that of the spatially independence model, which is in line with the current notion that consumer preferences cut across national borders. However, the spatially contiguity model and spatial-association model demonstrate the best fit. Although the differences between the various models are not very large, we find support, consistent across the two fit indices, for the spatial models. Substantive results are presented for the spatial contiguity model. We identified five spatial segments that cut across borders. The segments give rise to different retail positioning strategies, and their importance estimates and location demonstrate face validity.

There are now available a number of new subscription services that comprise a dual pricing system of a monthly access fee (rental) and a per-minute usage charge. Examples include cellular phones, the Internet, and pay TV. The usage and retention of such services depend on the absolute and relative prices of this dual system. For instance, a moderate access fee but a low-usage charge might initially appeal to customers, but later a low-usage customer might find the monthly fee unjustified and thereby relinquish the service. Providers of such services, therefore, usually offer several pricing packages to cater to differing customer needs. The purpose of this study is to derive a revenue-maximizing strategy for subscription services, that is, the combination of access and usage price that maximizes revenue over a specified time period. An additional objective is to determine access and usage price elasticities because they have historically played an important role in theoretical pricing models. The application area is the cellular phone market, but for a new rather than an existing product. To help gauge the likely usage rates and customer retention, a field experiment is conducted in which several alternative price combinations are used. Specifically, a sample of potential residential customers (most of whom did not have an existing cell phone) were divided into four treatment groups. The first group were not charged an access fee but did have to pay a small per-minute usage charge. The second group also paid a small usage charge but in addition had three access price increases over the duration of the trial. The third group paid no access fee but had usage charge increases, while the fourth group had both access fee and usage charge increases. Usage levels for each respondent are recorded, as is their month of dropout if they discontinue the service. An initial examination of the data shows that higher access fees result in higher customer attrition, and higher usage cost results in lower usage. Furthermore, usage and retention are related in that declining usage levels over time often signal impending customer attrition. Hence, two phenomena need to be modeled: usage of the service and customer retention conditional on usage. Some seasonal effects are also observed and are allowed for in the model. Modeling customer attrition simultaneously with usage is important because ignoring customer attrition will likely result in an underestimate of price sensitivity. This results from a censoring effect, whereby respondents who remain in the trial tend to be wealthier, and hence, less price sensitive. Given the known problems of ignoring customer attrition, we develop a theoretical model of usage, which explicitly incorporates attrition by extending a time-series model introduced by Hausman and Wise (1979). We make two extensions of the Hausman and Wise model. The first is to generalize it from two to many time periods and the second is to allow for respondent heterogeneity by incorporating latent classes. We fit the model by maximum likelihood and find that a two-segment model is best. In addition, we examine the predictive validity of our model and find it to be reasonably good. In general, the results show that access and usage prices have different relative effects on demand and retention. There are five key results. First, access price has some effect on usage but a much stronger effect on retention. Second, usage price has a strong effect on usage and a moderate effect on retention, in that if usage price increases so much that usage declines, then lower usage levels results in higher attrition. Third, access price elasticity is about half that of usage price, with both elasticities generally being much smaller than 1, indicating relative inelasticity for this particular service. Fourth, customer attrition rate (churn) is much more sensitive to access than usage price and, last, if just observed usage is examined and customer attrition is ignored, then price sensitivity is very likely to be substantially underestimated (on the order of 45% in our case). Finally, when developing the revenue-maximizing price combination we allow for the cost of customer acquisition by using some typical advertising-to-sales ratios for the telecommunications industry. We find that the revenue maximizing price is $27.70 per month for the access fee and $0.81 per minute for the airtime charge. These values are in line with current access fees and usage costs in the given market.

A computationally attractive model for the analysis of conjoint choice experiments is the mixed multinomial logit model, a multinomial logit model in which it is assumed that the coefficients follow a (normal) distribution across subjects. This model offers the advantage over the standard multinomial logit model of accommodating heterogeneity in the coefficients of the choice model across subjects, a topic that has received considerable interest recently in the marketing literature. With the advent of such powerful models, the conjoint choice design deserves increased attention as well. Unfortunately, if one wants to apply the mixed logit model to the analysis of conjoint choice experiments, the problem arises that nothing is known about the efficiency of designs based on the standard logit for parameters of the mixed logit. The development of designs that are optimal for mixed logit models or other random effects models has not been previously addressed and is the topic of this paper. The development of efficient designs requires the evaluation of the information matrix of the mixed multinomial logit model. We derive an expression for the information matrix for that purpose. The information matrix of the mixed logit model does not have closed form, since it involves integration over the distribution of the random coefficients. In evaluating it we approximate the integrals through repeated samples from the multivariate normal distribution of the coefficients. Since the information matrix is not a scalar we use the determinant scaled by its dimension as a measure of design efficiency. This enables us to apply heuristic search algorithms to explore the design space for highly efficient designs. We build on previously published heuristics based on relabeling, swapping, and cycling of the attribute levels in the design. Designs with a base alternative are commonly used and considered to be important in conjoint choice analysis, since they provide a way to compare the utilities of pro- files in different choice sets. A base alternative is a product profile that is included in all choice sets of a design. There are several types of base alternatives, examples being a socalled outside alternative or an alternative constructed from the attribute levels in the design itself. We extend our design construction procedures for mixed logit models to include designs with a base alternative and investigate and compare four design classes: designs with two alternatives, with two alternatives plus a base alternative, and designs with three and with four alternatives. Our study provides compelling evidence that each of these mixed logit designs provide more efficient parameter estimates for the mixed logit model than their standard logit counterparts and yield higher predictive validity. As compared to designs with two alternatives, designs that include a base alternative are more robust to deviations from the parameter values assumed in the designs, while that robustness is even higher for designs with three and four alternatives, even if those have 33% and 50% less choice sets, respectively. Those designs yield higher efficiency and better predictive validity at lower burden to the respondent. It is noteworthy that our “best” choice designs, the 3- and 4-alternative designs, resulted not only in a substantial improvement in efficiency over the standard logit design but also in an expected predictive validity that is over 50% higher in most cases, a number that pales the increases in predictive validity achieved by refined model specifications.

The objective of the present research is to study consumer decisions to utilize a line of credit. The life-cycle hypothesis from economics argues that consumers should intertemporally reallocate their incomes over their life stream to maximize lifetime utility. One form of intertemporal allocation is to use past income (in the form of savings) in the future. A second form is the use of future income in the present. This can only be done if consumers have access to a temporary pool of money that they can draw from and replenish in the future—a function performed by consumer credit. However, our research reinforces prior findings that consumers are unable to correctly value their future incomes, and that they lack the cognitive capability to solve the intertemporal optimization problem required by the life-cycle hypothesis. Instead, we argue that consumers use information such as the credit limit as a signal of their future earnings potential. Specifically, if consumers have access to large amounts of credit, they are likely to infer that their lifetime income will be high and hence their willingness to use credit (and their spending) will also be high. Conversely, consumers who are granted lower amounts of credit are likely to infer that their lifetime income will be low and hence their spending will be lower. However, based on research in the area of consumer skepticism and inference making, we also argue for a moderating role of the credibility associated with the credit limit. Specifically, we argue that the above effect of credit availability would be particularly strong for consumers who believe that the credit limit credibly signals their future earnings potential (i.e., a naïve consumer who has limited experience with consumer credit). However, as consumers gain experience with credit, they start discounting credit availability as a predictor of their future and start questioning the validity of the process used to set the credit limit. Hence, with experience the effect of credit limit on the willingness to use credit should be attenuated. We test these predictions in five separate studies. In the first experimental study, we manipulate credit limit and credibility and pose subjects with a hypothetical purchase opportunity. Consistent with our prediction, credit limit impacted the propensity to spend, but only when the credibility was high. In the second experimental study, we replicate these findings even when subjects were given information about their expected future salaries, and also show that the credit limit influences their expectation of future earnings potential. In the third study, we show that the mere availability (and increase) of current liquidity cannot explain our findings. In the fourth study, we conduct a survey of consumers in which we measure a number of demographic characteristics and also ask them for their propensity to spend in a given purchase situation. In the fifth study we use the Survey of Consumer Finances (SCF) dataset, a triennial survey of U.S. families that is designed to provide detailed information on the use of financial services, spending behaviors, and selected demographic characteristics. Results from both studies 4 and 5 provide further support for our proposed framework—credit limits influence spending to a greater extent for consumers with lower credibility: younger consumers and less-educated consumers. Across all studies we achieved triangulation by using a variety of approaches (surveys and experiments), subjects types (young students and older consumers), nature of predictor variables (manipulated and measured), dependent measures (purchase likelihood, credit card balance, new charges), and methods of analysis (ANOVA and regression), and consistently found that increasing credit limits on a credit card increases spending, especially when the credibility of the limit is high. This paper joins a growing body of literature in marketing and behavioral decision theory that goes beyond the traditional domains of inquiry (e.g., product choice, effects of marketing mix variables) and focuses on consumer decisions relating to the appropriate use of income to finance consumption. Our framework differs from prior research on the effect of payment mechanisms on spending in two significant ways. First, we are interested in the effects of the availability of credit on spending, and not necessarily in the effect of the transaction format that is associated with each payment mechanism. Second, while prior research has studied the point-of-purchase and historic (i.e., prepurchase) effects of credit, the present research is concerned with the availability of credit in the future. Specifically, our framework is invariant to the current and prior usage of credit by the consumer.

The increasing availability of customer information is giving many firms the ability to reach and customize price and other marketing efforts to the tastes of the individual consumer. This ability is labeled as consumer addressability. Consumer addressability through sophisticated databases is particularly important for direct-marketing firms, catalog retailers such as L.L Bean and Land's End, credit card-issuing banks, and firms in the long-distance telephone market. We examine the strategic implications of consumer addressability on competition between database/direct marketing firms. We address questions such as: In a competitive environment, how should firms invest in addressability? Will future improvements in the degree of addressability increase or mitigate the intensity of competition between the firms? Will greater addressability always be beneficial for firms? We model competition between two firms in a market where consumers differ on a horizontal attribute of product differentiation. The market comprises consumers located on a linear attribute space and firms located at the ends of the line. We represent the degree of addressability (or the reach of a firm's database) as the proportion of consumers at each point in the market who are in the firm's database. Consequently, the firm can offer these consumers customized prices. Consumer addressability creates two effects that govern the competition between firms: a "surplus extraction" effect because a firm might address a consumer who is not reached by its competitor and a "competitive" effect that is created by the set of consumers who can be addressed by both firms. The key results of the paper pertain to when the addressability decision is endogenous. When the extent of market differentiation (or consumer heterogeneity in preferences for a product/brand attribute), as well as the incremental cost of addressability, are sufficiently large, firms make symmetric investments in equilibrium. Given high costs, firms choose sufficiently low levels of addressability. Low addressability and high levels of market differentiation both help reduce price competition, which facilitates symmetric choice of addressability by the firms in equilibrium. However, when market differentiation and the cost of incremental addressability become small, firms face the prospect of destructive competition. As a result, they strategically differentiate in their choice of addressability to mitigate this competition. Interestingly, even in the extreme case when incremental addressability is costless, not every firm chooses full addressability in equilibrium. This has useful implications for direct marketing. Given that the advances in information technology should improve the ability of firms to address their consumers, it might indeed not be desirable for all direct marketing firms to indefinitely pursue greater addressability as costs of doing so decline. The analysis also shows an interesting effect of market differentiation in addressable markets: Equilibrium profits can decrease with an increase in market differentiation when the marginal cost of addressability is sufficiently high. Finally, we discuss the competitive outcome that would result when firms compete with addressable as well as uniform posted prices.

As firms jockey to position themselves in emerging markets, firms need to evaluate the relative attractiveness of market expansion in different countries. Since the attractiveness of a market is a function of the eventual market potential and the speed at which the product diffuses through the market, a better understanding of the determinants of market potential and diffusion speed across different countries is of particular relevance to firms deliberating their market expansion strategies. Despite a recent spurt in research on multinational diffusion, there exist significant gaps in the literature. First, existing studies tend to limit their analysis to industrialized countries, thus reducing the ability to generalize the insights to many emerging markets. Second, these studies tend to focus on the coefficients of external and internal influence in the Bass diffusion model but do not analyze the determinants of market potential. Third, the choice of variables that affect the parameters of the Bass diffusion model has been rather limited. In this paper, we seek to address these gaps in the literature. To address the scope issue, we assembled a novel dataset that captures the diffusion of 6 products in 31 developed and developing countries from Europe, Asia, and North and South America. The set of countries in our dataset encompasses 60% of the world population and includes such emerging economies as China, India, Brazil, and Thailand. This should provide us with a stronger basis to make empirical generalizations about the diffusion process. For firms seeking to expand into emerging international markets, our findings about penetration potential have considerable significance. For example, we find that for the set of products that we analyze the average penetration potential for developing countries is about one-third (0.17 versus 0.52) of that for developed countries. We also find that it takes developing countries on average 17.9% (19.25 versus 16.33 years) longer to achieve peak sales. Thus, despite the well-known positive effect of product introduction delays on diffusion speed, we find that developing countries still continue to experience a slower adoption rate, compared to that of developed countries. Our study also investigated the impact of several new macroenvironmental variables on penetration potential and speed. For example, our findings indicate that a 1% change in international trade or urbanization level can potentially change the penetration potential by about 0.5% and 0.2% respectively. These are some of the key variables projected to change significantly over the coming years for developing countries. While business managers have relatively little influence on such variables, our findings can still serve as valuable empirical guide for the variables that they should consider in evaluating diverse international markets and in performing sensitivity analysis with respect to their projected trends. Finally, our study also holds implications for managers seeking to combine information about past diffusion patterns across products and countries for better prediction. We pool information efficiently across multiple products and countries using a Hierarchical Bayes estimation methodology. By sharing information across countries and products in a single, coherent framework, we find that this pooling approach leads to substantial improvements in prediction accuracy. Our technique is particularly superior in predicting sales and BDM parameter values in the early years of new product introduction in a new country, when forecast estimates are managerially most useful. We also decompose the variance in the BDM model parameters into product, country, and product-country components. These results give guidelines to managers about which market experience they should weigh more to arrive at forecasts of market potential and diffusion speed. We find that while past experiences of other products in a country (country effects) are relatively more useful to explain penetration level (cumulative sales), past experiences in other countries where a product was earlier introduced (product effects) are more useful to explain the coefficients of external and internal influence (and thus the speed with which the product will attain peak sales).

Consumers are often observed to purchase more than one variety of a product on a given shopping trip. The simultaneous demand for varieties is observed not only for packaged goods such as yogurt or soft drinks, but in many other product categories such as movies, music compact disks, and apparel. Multinomial (MN) choice models cannot be applied to data exhibiting the simultaneous choice of more than one variety. The random utility interpretation of either the MN logit or probit model uses a linear utility specification that cannot accommodate interior solutions with more than one variety (alternative) chosen. To analyze data with multiple varieties chosen requires a nonstandard utility specification. Standard demand models in the economics literature exhibit only interior solutions. We propose a demand model based on a translated additive utility structure. The model nests the linear utility structure, while allowing for the possibility of a mixture of corner and interior solutions where more than one but not all varieties are selected. We use a random utility specification in which the unobservable portion of marginal utility follows a log-normal distribution. The distribution of quantity demanded (the basis of the likelihood function) is derived from these log-normal random utility errors. The likelihood function for this class of models with mixtures of corner and interior solutions is a mixed distribution with both a continuous density portion and probability mass points for the corners. The probability mass points must be calculated by integrals of the log-normal errors over rectangular regions. We evaluate these high-dimensional integrals using the GHK approximation. We employ a Bayesian hierarchical model, allowing household-specific utility parameters. Our utility specification related to the approach of Wales and Woodland (1983) who employ a translated quadratic utility function. Wales and Woodland were only able to study, at the most, three varieties because there was no practical way to evaluate the utility function at that time. In addition, the quadratic utility specification is not a globally valid utility function, making welfare computations and policy experiments questionable. Hendel (1999) and Dube (1999) present an alternative approach in the utility function which is constructed by summing up over unobservable consumption occasions. While only one variety is consumed on each occasion, the marginal utilities of varieties change over the consumption occasions, giving rise to a simultaneous purchase of multiple varieties. Our Bayesian inference approach allows us to obtain individual household estimates of utility parameters. Household utility estimates are used to compute the value of each variety. We compute a compensating value for the removal of each flavor; that is, we compute the monetary equivalent of the household's loss in utility from removal of a flavor. These calculations show that households highly value popular flavors and would incur substantial utility losses from removal of these flavors from the yogurt assortment. Next we consider the implications of our model for retailer assortment and pricing policies. Given limited shelf space, only a subset of the possible varieties can be displayed for purchase at any one time. If consumers value variety, then a retailer with lower variety must compensate the consumers in some way, such as a lower price level. We see this trade-off between price and variety across different retailing formats. Discount or warehouse format retailers often have both lower variety and lower prices. To measure this trade-off, we explore the utility loss from reduction in variety and find the reductions in price that will compensate for this utility loss. These price reduction calculations must be based on a valid utility structure. Heterogeneity in tastes is critical in these utility computations and policy experiments. We find that a relatively small fraction of households with extreme preferences dominate the compensating value computations. That is, some households are observed to purchase mostly or exclusively one variety. These households must be heavily compensated for the removal of this variety from the assortment. In some retailing contexts, customization of the assortment is possible at the customer level. We show that such customization virtually eliminates any utility loss from reduction in variety.

People are frequently exposed to potentially attractive events that are subsequently and unexpectedly reversed and to potentially painful events, which are also unexpectedly reversed. In the process of being returned to the initial asset position, does the sequence in which the positive and negative events occur matter? This issue of the combined effect of pleasurable and painful stimuli has received scant theoretical or empirical attention. We attempt to fill this lacuna in the literature by studying the retrospective evaluation of surprises that return individuals to their original economic state. Although such surprises do not change an individual's original economic state, we argue that the individual's psychological state changes, and the final affective state is, among other things, a function of the sequence in which the events occur. From a theoretical standpoint, several perspectives can be brought to bear on the issue. For instance, one reading of mental accounting, based on prospect theory's value function, would predict that losses should dominate gains, and therefore, regardless of sequence, people should be unhappy when exposed to two economically equivalent outcomes of different signs. Conversely, the literature on intertemporal choice would suggest that a series that ends on an up note is preferred to a series that ends on a down note, because people like to defer gratification so that they may savor positive outcomes. Similarly, people apparently have a preference for "happy endings." Finally, the extant literature on "recency effects" would predict that the last event in a series should have a disproportionate influence on overall affect. Our model relies on a shift in the reference point to explain how a surprising reversal of an event will lead to a nonzero evaluation of the sequence. We suggest that people's reference points shift immediately but imperfectly after a stimulus is presented. Intuitively, this implies that the first stimulus will shift the reference point in its direction, as a result of which the evaluation of a sequence of events in which an initial event is unexpectedly reversed will be more favorable if the first event is a loss than if it is a gain. This model captures the unanticipated nature of the second event (i.e., the surprise element) by allowing the first event to move the reference point. Consequently, by the time the next event occurs the reference point has been updated, as a result of which the zero economic outcome of the sequence yields nonzero utility. We further posit that the magnitude of the reference point shift should be affected by the time elapsed between the two stimuli. Specifically, the reference point shifts gradually with time, until it is fully updated. Consequently, the final affective state of the sequence is also a function of the temporal distance between the two events. The main predictions of the model were empirically supported first in a survey using a mall-intercept sample. Subsequently, we conducted a study of student subjects involving a coin-tossing game in which real money was at stake and in which subjects in one condition experienced the second outcome after a two-day delay. Our results from this second study supported the model's prediction regarding the impact of the elapsed time between the events. The experimental tasks involved surprising reversals of initial outcomes, thus ensuring that "savoring/dread" types of explanations (which require that subjects anticipate the second event) could not be operating. Finally, in a series of three follow-up studies, we tested the claim that the magnitude of outcomes would have an impact on observed affect, and consistent with our theory and contrary to recency predictions, we observed similar results across different magnitudes. While theoretically interesting, we should also note that our research is of potential pragmatic significance. People's reactions to a series of events is of considerable interest to marketers desirous of generating enhanced attitude, affect, purchase intention, and the like without offering economic inducements such as rebates, coupons, or other costly discounts. Additionally, public policy officials may be interested in protecting people from being manipulated into purchasing a product simply because of changes in the sequence in which a series of offers is made by the merchant.

We examine the retailer's store brand positioning problem. Our game-theoretic model helps us identify a set of conditions under which the optimal strategy for the retailer is to position the store brand as close as possible to the stronger national brand. In three empirical studies, we examined whether market data are consistent with some of the implications of our model. In the first study, using observational data from two US supermarket chains, we found that store brands are more likely to target stronger national brands. Our second study estimated cross-price effects in 19 product categories, and found that only in categories with high-quality store brands, store brand and the leading national brand compete more intensely with each other than with the secondary national brand. In a third product perception study, we found that although explicit targeting by store brands influenced consumer perceptions of physical similarity, it had no influence on consumers' perceptions of overall or product quality similarity. While it appears that retailers do follow a positioning strategy consistent with our model, it changes buying behavior in the intended fashion only if the store brand offers quality comparable to the leading national brands.