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This work advances and tests a theory of how news information evolves as it is successively retold by consumers. Drawing on data from over 11,000 participants across ten experiments, the authors offer evidence that when news is repeatedly retold, it undergoes a stylistic transformation termed “disagreeable personalization,” wherein original facts are increasingly supplanted by opinions and interpretations with a slant toward negativity. The central thesis is that when retellers believe they are more (vs. less) knowledgeable than their recipient about the information they are relaying, they feel more compelled to provide guidance on its meaning and to do so in a persuasive manner. This enhanced motivation to guide persuasively, in turn, leads retellers to not only select the subset of facts they deem most essential but, critically, to provide their interpretations and opinions on those facts, with negativity being used as a means of grabbing their audience’s attention. Implications of this work for research on retelling and consumer information diffusion are explored.

Some cultural products (e.g., books and movies) catch on and become popular, while others fail. Why? While some have argued that success is unpredictable, we suggest that period-to-period shifts in sentiment—what we term sentiment volatility—enhance engagement. Automated sentiment analysis of over 4,000 movies demonstrates that more volatile movies are evaluated more positively. Consistent with the notion that sentiment volatility makes experiences more stimulating, the effect is stronger in genres where evaluations are more likely to be driven stimulation (i.e., thrillers rather than romance). Further, analysis of over 30,000 online articles demonstrate that people are more likely to continue reading more volatile articles. By manipulating sentiment volatility in follow-up experiments, we underscore its causal impact on evaluations, and provide evidence for the role of stimulation in these effects. Taken together, the results shed light on what drives engagement, the time dynamics of sentiment, and cultural analytics or why some cultural items are more successful.

Many marketing communications, from verbal conversations to messaging and content sharing via apps such as Snapchat, limit the number of times people can view content. How do such restrictions affect consumers’ information processing? Building on the proposition that people strategically allocate cognitive resources, the authors hypothesize that consumers of content that cannot be viewed repeatedly consider the risk of failing to process it sufficiently and, consequently, allocate more cognitive resources to its processing (e.g., by increasing viewing time). The authors test this hypothesis in ten preregistered online studies (total N = 17,620), an exploratory analysis of eye-tracking data, and a field study on Facebook's advertising platform. Across the studies, they find that making content ephemeral elevates consumers’ perceived risk of missing information; consequently, it increases attention allocation, prolongs voluntary viewing time, and magnifies focus on relevant information. These effects have important downstream consequences, including improved content comprehension and recall, enhanced positive attitudes, and increased efficiency of sponsored content placement on social media. Taken together, the findings indicate that marketers can communicate information more effectively by restricting consumers from viewing it again.

Results from three large-scale field studies and two controlled experiments show that consumers tend to be more self-disclosing when generating content on their smartphone versus personal computer. This tendency is found in a wide range of domains including social media posts, online restaurant reviews, open-ended survey responses, and compliance with requests for personal information in web advertisements. The authors show that this increased willingness to self-disclose on one’s smartphone arises from the psychological effects of two distinguishing properties of the device: (1) feelings of comfort that many associate with their smartphone and (2) a tendency to narrowly focus attention on the disclosure task at hand due to the relative difficulty of generating content on the smaller device. The enhancing effect of smartphones on self-disclosure yields several important marketing implications, including the creation of content that is perceived as more persuasive by outside readers. The authors explore implications for how these findings can be strategically leveraged by managers, including how they may generalize to other emerging technologies.

When advertising products to consumers, firms sometimes conceal key aspects in an effort to arouse consumer curiosity. This research investigates when and how visual concealment tactics may benefit or hurt aesthetic product evaluations. The authors propose that when consumers are only able to view a portion of an aesthetic product, assessments of its appeal will be influenced by two interrelated mechanisms: curiosity to see the item completed and inferences about the item’s fully disclosed appearance. The authors show that heightened curiosity triggers feelings of positive affect that are transferred to the product itself, a process that may inflate preferences and choice likelihoods for products beyond what would occur if the full image were known. This transference effect, however, has an important boundary: it works only when initial consumer inferences about the appeal of the product are positive or emotionally congruent with the positive affect triggered by curiosity. The key implication is that, ironically, the products likely to benefit most from concealment tactics are those that have the least to hide. The authors provide evidence for these effects and the underlying mechanism using six experiments that manipulate concealment in a variety of task settings.

A common assumption made in structural approaches to empirical strategy research in marketing is that firms and consumers satisfy the assumptions of dynamic optimality when making decisions. When faced with problems of how best to allocate resources, firms are assumed consider the future consequences of different strategic options and, in each point in time, choose the option that maximizes long-term utility. The validity of such assumptions, however, is often called into question by behavioral researchers who point to work in psychology that finds that assumptions of optimality are frequently violated in experimental settings. If this is indeed the case, it would lend support to approaches that argue that markets have inefficiencies that can be discovered and exploited by simpler, largely correlational, methods. In this article, the authors attempt to reconcile these contrasting views by proposing a framework for assessing when assumptions of dynamic optimality are likely to be good ones and when they are likely to be untenable in empirical analysis.

Multipart tariffs are widely favored within service industries as an efficient means of mapping prices to differential levels of consumer demand. Whether they benefit consumers, however, is far less clear as they pose individuals with a potentially difficult task of dynamically allocating usage over the course of each billing cycle. In this paper we explore this welfare issue by examining the ability of individuals to optimally allocate consumption over time in a stylized cellular-phone usage task for which there exists a known optimal dynamic utilization policy. Actual call behavior over time is modeled using a dynamic choice model that allows decision makers to both discount the future (be myopic) and be subject to random errors when making call decisions. Our analysis provides a “half empty, half full” view of intuitive optimality. Participants rapidly learn to exhibit farsightedness, yet learning is incomplete with some level of allocation errors persisting even after repeated experience. We also find evidence for an asymmetric effect in which participants who are exogenously switched from a low (high) to high (low) allowance plan make more (fewer) errors in the new plan. The effect persists even when participants make their own plan choices. Finally, interventions that provide usage information to help participants eradicate errors have limited effectiveness.

In many domains, consumers must deal with an increasing number of choices—spanning where, when, what, and how many items to buy; how many and which options to consider; and how best to weigh the pros and cons of these options. This paper considers how consumer and managerial goals and the ensuing tradeoffs affect the optimal design of assortments in order to help enhance our understanding of assortment choice, identify issues that merit particular attention, review some of the recent research in pertinent areas, and suggest directions for future research.

This research investigates the process that underlies consumer decisions to abandon waits for service. The work centers on a hypothesis that stay-or-renege decisions reflect a process that blends two opposing psychic forces: escalating displeasure with waiting versus an escalating commitment to a wait that has been initiated. The consequence is a predicted tendency that abandonments are most likely near the midpoint of waits, which is suboptimal for many waiting time distributions. This study tests the hypothesis using data from three laboratory experiments in which participants play a time-management game that involves waiting for downloads from different computer servers, as well as field data about hang-ups in an emergency call center in India. The data lend support to the proposed competing hazards model and show that the trade-off between desires to abandon and persist is moderated by contextual factors, such as the initial number of alternative queues and the amount of distracting activity engaged in during a wait.

Game theoretic models of marketing channels typically rely on simplifying assumptions that, from a behavioral perspective, often appear naïve. However, behavioral researchers have produced such an abundance of behavioral regularities that they are impossible to incorporate into game theoretic models. We believe that a focus on three core findings would benefit both fields; these are: first, beliefs that are held by the various players regarding profit consequences of different actions are incomplete and often biased; second, players’ preferences and optimization objectives are not commonly known; and third, players have insufficient cognitive abilities to achieve optimization objectives. Embracing these three findings shifts the focus from rational decision making to how decision makers learn to improve their decision-making skills. Concluding, we believe that greater convergence of game theoretic modeling and behavioral research in marketing channels would lead to new insights for both fields.

The implications of Salisbury and Feinberg's (2010) paper [Salisbury, L. C., F. M. Feinberg. 2010. Alleviating the constant stochastic variance assumption in decision research: Theory, measurement, and experimental test. Marketing Sci. 29(1) 1–17] for the process of model development and testing in the field of intertemporal choice analysis is explored. Although supporting the overall thrust of Salisbury and Feinberg's critique of previous empirical work in the area, we also see their paper as illustrating the dangers of drawing strong inferences about the behavioral interpretation of statistical model parameters without seeking convergent empirical evidence. In particular, we are skeptical about the extent to which the reported effects of temporal distance on the estimated scale parameter, σc, are uniquely, or even primarily, due to unobserved error inflation that reflects consumer's uncertainty about future utility. This interpretation is brought into question by several lines of reasoning. Conceptually, we note that “uncertainty” is different from “error” and that, for choice data, the error inflation model is mathematically identical to a model in which the scale parameter is a deterministic function of the temporal discount rate. Empirically, a reanalysis of data from previously published experiments does not consistently support temporal error inflation, temporal convergence of choice shares, or the scale parameter as an explanation of variety seeking in choice sequences. In our opinion, the cumulative results of research on intertemporal choice require models in which the attributes of choice alternatives are differentially discounted over time. Despite these findings, we advocate that choice researchers should indeed follow Salisbury and Feinberg's advice to not assume that error variances will be unaffected by experimental manipulations, and such effects should be explicitly modeled. We also agree that uncovering effects on error variance is just the first step, and the ultimate goal should be to rigorously explain the reasons for such effects.

We review the discussion at a workshop whose goal was to achieve a better integration among behavioral, economic, and statistical approaches to choice modeling. The workshop explored how current approaches to the specification, estimation, and application of choice models might be improved to better capture the diversity of processes that are postulated to explain how consumers make choices. Some specific challenges include how to capture and parsimoniously describe heterogeneous mixes of heuristic choice rules, methods for building realistic models of choice, and nontraditional methods for estimating models. An agenda for important future work in these areas is also proposed.

We investigate biases in product valuation and usage decisions that arise when consumers consider a new generation of a product that offers an expanded set of capabilities of uncertain value. Two experiments using a novel computer game show evidence of a valuation-usage disparity: Participants display a high willingness to pay for a new version of the game that offers a new set of controls, but fail to fully exercise the option to use these controls after purchase. This discrepancy is attributed to a fundamental difference in how new capabilities are valued at the time of purchase versus use. Consumer usage decisions appear to be driven by such myopic concerns as a desire to avoid short-term learning costs, whereas purchase decisions often fail to take into account the factors that drive usage, and are further inflated by global optimism in the future usefulness of new capabilities. We show that this lack of foresight can be explained by an intertemporal judgment model in which consumers attempt to value the option to use new capabilities as would be prescribed by economic theory, but are prone to hyperbolic discounting in their temporal valuation of present versus future costs and benefits.

This research examines the ability of consumers to predict the appeal of complete visual patterns from small sample fragments. In a task designed to mimic the dilemma of choosing wallpaper from small swatches, study participants are shown fragments taken from a large pattern design and are asked to predict how attractive they would find the complete image. Drawing on prior research on affective forecasting, predictions are hypothesized to be driven by an anchoring-and-adjustment process that skews forecasts toward the attractiveness of fragments when judged in isolation. Results from 3 laboratory studies support this basic hypothesis: Respondents consistently overestimate the degree to which their initial reactions to fragments predict their subsequent evaluations of wholes. The size of this projection bias is, in turn, conditioned by such moderators as prior familiarity with product fragment, cognitive load, and visualization abilities—effects that are consistent with an anchoring-and-adjustment explanation for the data.

This article examines how unexpected changes in the marketing mix of one product in a retail setting can influence demand for other, unrelated, items. Results from two laboratory studies show that spillover effects can occur in response to both positive and negative changes in either the price or quality of a product, such that positive changes increase total spending on other items and negative changes reduce it. The results also demonstrate that an attributional process underlies these effects, indicating that consumers experience specific affective responses directed at the retailer that lead them either to reward or punish the retailer accordingly.

We explore issues in theory-driven choice modeling by focusing on partial-equilibrium models of dynamic structural demand with forward-looking decision-makers, full equilibrium models that integrate the supply side, integration of bounded rationality in dynamic structural models of choice and public policy implications of these models.

This paper reviews the state of the art of research on individual decision-making in high-stakes, low-probability settings. A central theme is that resolving high-stakes decisions optimally poses a formidable challenge not only to naive decision makers, but also to users of more sophisticated tools, such as decision analysis. Such decisions are difficult to make because precise information about probabilities is not available, and the dynamics of the decision are complex. When faced with such problems, naive decision-makers fall prey to a wide range of potentially harmful biases, such as failing to recognize a high-stakes problem, ignoring the information about probabilities that does exist, and responding to complexity by accepting the status quo. A proposed agenda for future research focuses on how the process and outcomes of high-stakes decision making might be improved.

Marketing channel interactions typically feature three characteristics that have not been incorporated together in an analytic study: (1) the parties can do business repeatedly over time, often under different terms of trade (e.g., prices may vary), (2) the terms that the seller offers one buyer may be different from those she offers another, giving each interaction the flavor of bilateral monopoly bargaining, and (3) the buyer and seller come to the interaction uncertain about the valuations each holds for the good, but they do know each other's reputation for valuation. The seller might, for example, come to the bargaining table aware that the buyer has a strong reputation for being willing to pay only low prices, and the buyer might come aware that the seller is strongly reputed for high cost and is, therefore, willing to offer only high prices. The latter characteristic raises an interesting question: When engaged in a marketing channel interaction, what type of reputation is best for a buyer or seller to take to the bargaining table? In this paper, we answer that question by incorporating each of the characteristics that typify channel interactions in a formal game-theoretic bargaining model. We determine how the reputations that buyers and sellers bring to the bargaining table affect their equilibrium strategies and payoffs. Our analysis shows that, in general, the best reputation for the seller to take to the bargaining table is one that makes the buyer nearly certain in his belief that the seller's cost is high, a result that matches intuition. The best reputation for the buyer, however, is counterintuitive. We show that an increase in the buyer's reputed willingness to pay can actually cause the seller to offer a lower price. The best reputation for the buyer to take to the bargaining table is, therefore, one that makes the seller believe that there is a significant chance that he is willing to pay a high price. This result is new to the literature and brings with it immediate managerial implications that we discuss. Our analysis also shows that modeling the buyer as a forward-looking strategic player yields different results than does following the normal convention of modeling the buyer as a nonstrategic price-taker. We discuss why future research on channels and on reference-dependent utility theory should consider these differences.

In collaborating to compete, firms forge different types of strategic alliances: same-function alliances, parallel development of new products, and cross-functional alliances. A major challenge in the management of these alliances is how to control the resource commitment of partners to the collaboration. In this research we examine both theoretically and experimentally how the type of an alliance and the prescribed profit-sharing arrangement affect the resource commitments of partners. We model the interaction within an alliance as a noncooperative variable-sum game, in which each firm invests part of its resources to increase the utility of a new product offering. Different types of alliances are modeled by varying how the resources committed by partners in an alliance determine the utility of the jointly-developed new product. We then model the interalliance competition by nesting two independent intra-alliance games in a supergame in which the groups compete for a market. The partners of the winning alliance share the profits in one of two ways: equally or proportionally to their investments. The Nash equilibrium solutions for the resulting games are examined. In the case of same-function alliances, when the market is large the predicted investment patterns under both profit-sharing rules are comparable. Partners developing new products in parallel, unlike the partners in a same-function alliance, commit fewer resources to their alliance. Further, the profit-sharing arrangement matters in such alliances—partners commit more resources when profits are shared proportionally rather than equally. We test the predictions of the model in two laboratory experiments. We find that the aggregate behavior of the subjects is accounted for remarkably well by the equilibrium solution. As predicted, profit-sharing arrangement did not affect the investment pattern of subjects in same-function alliances when they were in the high-reward condition. Subjects developing products in parallel invested less than subjects in same-function alliance, irrespective of the reward condition. We notice that theory seems to underpredict investments in low-reward conditions. Aplausible explanation for this departure from the normative benchmark is that subjects in the low-reward condition were influenced by altruistic regard for their partners. These experiments also clarify the support for the mixed strategy equilibrium: aggregate behavior conforms to the equilibrium solution, though the behavior of individual subjects varies substantially from the norm. Individual-level analysis suggests that subjects employ mixed strategies, but not as fully as the theory demands. This inertia in choice of strategies is consistent with learning trends observed in the investment pattern. A new analysis of Robertson and Gatignon's (1998) field survey data on the conduct of corporate partners in technology alliances is also consistent with our model of samefunction alliances. We extend the model to consider asymmetric distribution of endowments among partners in a same-function alliance. Then we examine the implication of extending the strategy space to include more levels of investment. Finally, we outline an extension of the model to consider cross-functional alliances.

We review current state-of-the-art practices for combining preference data from multiple sources and discuss future research possibilities. A central theme is that any one data source (e.g., a scanner panel source) is often insufficient to support tests of complex theories of choice and decision making. Hence, analysts may need to embrace a wider variety of data types and measurement tools than traditionally have been considered in applied decision making and choice research. We discuss the viability of preference-stationarity assumptions usually made when pooling data, as well as random-utility theory-based approaches for combining data sources. We also discuss types of models and data sources likely to be required to make inferences about and estimate models that describe choice dynamics. The latter discussion is speculative insofar as the body of literature on this topic is small.

We investigate how consumers learn to form price expectations for multiattribute goods in novel product categories. We hypothesize that expectations of price among novice consumers will be dominated by simplistic intuitive conjectures about the costs of goods that would cause expectations of prices to depart systematically from “true” values. Although we hypothesize that judgments should become more accurate—and configural—as expertise grows, we also hypothesize that learning will be limited, with even experienced consumer judges displaying some of the same biases in price expectations observed among novices. We tested these hypotheses in two studies: a dynamic laboratory task that simulated a period of learning about prices in a novel category and a cross-sectional survey that compared the multiattribute price-expectation rules used by novice and experienced consumers. The results provide a somewhat surprising view of the dynamics of price-expectation policies. First, Study 1 shows that composition rules used by novices can be highly configural in nature, even when they lack knowledge about the true rules that govern the price of goods in a category. Second, both studies support the hypothesis of limited learning, with judgments strategies of experienced consumers being only slightly more accurate in anticipating true normal market prices than those used by inexperienced consumers. A discussion of the implications of the work for research in how consumers learn to form price expectations is offered.

Research examining the process of individual decision making over time is briefly reviewed. We focus on two major areas of work in choice dynamics: research that has examined how current choices are influenced by the history of previous choices, and newer work examining how choices may be made to exploit expectations about options available in the future. A central theme of the survey is that if a general understanding of choice dynamics is to emerge, it will come through the development of boundedly-rational models of dynamic problem solving that lie on the interface between economics and psychology.

The process by which individuals make sequential searches among choice sets prior to choosing a single item is examined. Economic search theory prescribes that such searches should be made by focusing on expectations of the maximum utility likely to be found in each set, ignoring external features of sets such as their complexity. Empirical research in psychology, however, suggests that search behavior may be highly influenced by set features, most notably a tendency to defer choices from sets that are perceived as difficult to evaluate. An experimental investigation of choice-set search reveals that stopping decisions are indeed influenced by normatively irrelevant external features of sets, but not in a way that is completely consistent with previous work on context effects. In particular, we find that individuals are likely to stop sooner in search when encountering larger versus smaller choice sets (holding their maximum utilities constant), and decisions to stop are unaffected by two traditional measures of difficulty: whether a choice set lacks dominant options and the similarity of overall utilities of options in a set. Implications of the work for the ability to generalize findings in static choice to dynamic task settings are explored.

Are there general algebraic laws which describe how consumers make choices from sets of alternatives? In this paper we review the verdict of research which has sought to answer this question. We focus on the functional forms which have been found to best characterize three component processes of consumer choice: those of attribute valuation, attribute integration, and choice. Our central conclusion is that there exists support for three major generalizations about the form of consumer decision processes: (1) subjective attribute valuations are a nonlinear, reference-point dependent, function of the corresponding objective measure of product attributes; (2) the integration rule which best describes how these attribute valuations are integrated to form overall valuations is multiplicative-multilinear, characterizing an overweighting of negative attribute information; and (3) the choice rule which links overall valuations of an option to the likelihood that it is chosen from a set is a member of a family of functions which recognize the attributewise proximity of a considered alternative to others in the set. The evidence supporting these generalizations is reviewed, as well as their implications for future theoretical and applied work in consumer choice modeling.

Multi-part tariffs are widely favored within service industries as an efficient means of mapping prices to differential levels of consumer demand. Whether they benefit consumers, however, is far less clear as they pose individuals with a potentially difficult task of dynamically allocating usage over the course of each billing cycle. In this paper we explore this welfare issue by examining the ability of individuals to optimally allocate consumption over time in a stylized cellular-phone usage task for which there exists a known optimal dynamic utilization policy. Actual call behavior over time is modeled using a dynamic choice model that allows decision makers to both discount the future (be myopic) and be subject to random errors when making call decisions. Our analysis provides a “half empty, half full” view of intuitive optimality. Participants rapidly learn to exhibit far-sightedness, yet learning is incomplete with some level of allocation errors persisting even after repeated experience. We also find evidence for an asymmetric effect in which participants who are exogenously switched from a low (high) to high (low) allowance plan make more (fewer) errors in the new plan. The effect persists even when participants make their own plan choices. Finally, interventions that provide usage information to help participants eradicate errors have limited effectiveness.

This paper reviews recent research on normative and descriptive models of sequential choice. In particular, the basic results of dynamic decision theory, including Bellman's principle of optimality, are briefly described. We then consider the descriptive validity of three assumptions that are frequently made in applications of dynamic programming models: forward planning, optimal learning, and stationarity. We offer the preliminary conclusion that although the outcomes of human decision policies may often be nearly optimal, most evidence suggests that the decision processes themselves are likely to be rather different from those that are consistent with normative theory. Finally, we suggest four fundamental elements for a positive theory of dynamic decision making.

The ability of consumers to make rational sequential purchase quantity decisions under imperfect knowledge about future prices in a product category is explored. Normatively, a consumer should make such decisions by defining a series of reservation prices which define how many buying-periods' supply should be held given an observed price. An experiment is reported in which consumers make sequential purchase quantity decisions under variations in the shape of the distribution of prices and its trend over time. Results suggest a number of systematic deviations from optimality. When facing stationary uniform and bimodal price distributions, for example, subjects tend to systematically overbuy when small purchases are called for and underbuy when large purchases are called for. Likewise, when facing ascending or descending series, behavior is the opposite of that predicted by the normative analysis: there is an increasing tendency to defer purchases given increasing prices and accelerate purchases given descending prices. An explanation for the findings in terms of a Prospect Theory-type loss function in expenditures is offered.

The ability of consumers to optimally anticipate the value they will draw from new product features that are introduced to enhance the performance of existing technologies is explored. The work tests a hypothesis that when consumers are given the opportunity to buy a new generation of a products that offers enhanced features consumer will overvalue them, a bias the accrues to a tendency to overestimate both the extent that they will utilize these new features and the impact they will have on utility. This general hypothesis is tested in the context of a computer simulation in which subjects are trained to play one three different forms of an arcade game where icons are moved over a screen by different forms of tactile controls. Respondents are then given the option to play a series of games for money with either with their incumbent game platform or pay to play with an alternative version that offered an expanded set of controls. As hypothesized, subjects displayed an upwardly-biased valuation for the new sets of controls; adopters underutilized them and displayed a level of game performance that was not better than those who never upgraded. A follow-up study designed to resolve the process underlying the bias indicated that while adopters indeed over-forecast the degree to which they would make use of the new control, they did not over-forecast performance gains. Hence, the key driver of adoption decisions appeared to be an exaggerated belief of the hedonic pleasure that would be derived from owning and utilizing the new control as opposed to any objective value it might provide.

The ability of managers to utilize sales-history data to form accurate inferences about the long-term value of customers is examined. Although in recent years considerable advances have been made in the development of formal models that allow such assessments to be, statistically, in most real-world settings the identification of key customers remains an intuitive process. We examine the process that drives intuitive assessments using data from a laboratory simulation that asks respondents to identify which of two customers is likely to be the better long-run prospect based on sales histories. These data allow us to identify the features of sales-history data that appear most influential in driving intuitive key-customer predictions, and compare the nature of these decisions with those that would be made using a well-established statistical model, the Pareto/NBD. We find that respondents utilize sales data in a way that is quite dissimilar from that which would coincide with the Pareto/NBD. For example, empirical results reveal that intuitive judgments under-attend to information about the recency of transactions, and that the prediction process is altered depending on the managerial objective of the assessment task, such as whether to add a customer to a key-account list or delete the customer - a distinction that would not be recognized by the statistical model. Implications of the findings for both academic and applied work in customer relationship management are discussed.

The author reflects on deceased market researcher Paul Green, the development of periodical "Journal of Marketing Research" (JMR), and the disjunction between marketing theory and practice. Topics include marketing strategies, research on consumer behavior, and statistical reporting guidelines for prospective authors. Additional information is presented on the incidence of fraud in social psychology research, the peer review system, and the American Marketing Association's role in enhancing scientific reporting in scholarly journals.

The process by which individuals make recurrent decisions about when to replace durable goods is examined. Two experiments are reported in which subjects play the role of purchasing agents who must repeatedly decide whether to keep a currently owned manufacturing device or replace it with a superior new one, given uncertainty about the future performance of new and currently owned machines. Replacement patterns are compared with those that would be predicted if subjects made decisions as rational economic agents, following the principles of optimal replacement theory. This comparison reveals a number of systematic departures from optimality that do not vanish with experience. Among these are a tendency to replace at a slower rate than would be predicted by normative theory and a tendency to weigh opportunity costs arising from obsolescence greater than those arising from product deterioration. In addition, subjects display a bias against making replacement purchases given short lapses of time since the previous replacement. The findings are interpreted in terms of known biases in decision making under uncertainty in dynamic and static settings.

In assessing the performance of a choice model, we have to answer the question, “Compared with what?” Analyses of consumer brand choice data historically have measured fit by comparing a model's performance with that of a naive model that assumes a household's choice probability on each occasion equals the aggregate market share of each brand. The authors suggest that this benchmark could form an overly naive point of reference in assessing the fit of a choice model calibrated on scanner-panel data, or any repeated-measures analysis of choice. They propose that fairer benchmarks for discrete choice models in marketing should incorporate heterogeneity in consumer choice probabilities, evidence for which is by now well documented in the marketing literature. They use simulated data to compare the performance of parametric and nonparametric benchmark models, which allow for heterogeneity in consumer choice probabilities, with the performance of the aggregate share-based benchmark model, which assumes consumers are homogeneous in their choice probabilities. They also assess the performance of two previously published consumer behavior models against the proposed fairer benchmark models that allow for heterogeneity in consumer choice probabilities. They find that one provides a significantly better fit than their more conservative benchmark models and the other performs less favorably.

This article examines how multiattribute impressions are formed in riskless choice and judgment when there is uncertainty or ambiguity associated with attribute-importance weights. Building on previous results in the modeling of ambiguity in probabilities, a mathematical theory of multiattribute judgments under weight uncertainty is developed. The theory incorporates framing effects for ambiguous weights in multiattribute judgments, similar to the gain-loss framing effects found in studies of preferences for lotteries. Specifically, it is shown that the importance of an attribute depends on whether it is seen as enhancing or preserving an expected level of utility in a category. The results of three experiments provide support for the predicted dependency of the effects of weight uncertainty on an attribute's framing. The data also suggest that, within frames, the effects of increasing uncertainty may not be additive across attributes. In particular, given pairs of uncertain attributes with different frames, subjects acted as if they were applying a single frame to both attributes, resulting in a common direction for the weight-uncertainty effect.

A multiattribute judgment model is advanced which is purported to characterize how consumers form evaluations of products when information about products’ attributes is limited and variability or unreliability is present within attributes. The model is based on the hypothesis that the subjective value consumers associate with a product attribute can be represented in terms of a weighted average of the value of the information they have received about that attribute and the perceived mean value of that attribute within the product class. This average is integrated with the consumer's perception of the amount of variability both within and between other alternatives on that attribute—a measure of uncertainty. A series of laboratory experiments testing aspects of the model are described. Results support both the model's predictions and aspects of its structure.

The sensitivity of the parameters and fit of compensatory choice models to contextual variations in information processing strategies is examined. A set of predictions is derived concerning specification errors which may arise when a compensatory model misrepresents a “true,” noncompensatory choice process. These predictions are then tested in an experimental analysis of apartment choice behavior. Logit analysis and protocol analysis are employed to assess how the parameters and fit of a compensatory model vary in light of changes in the underlying pattern of information processing across choice sets of differing sizes. Although attribute usage and parameter variation across set sizes conformed to theoretical expectations, a hypothesized decrease in predictive accuracy was not supported.

The processes of consumer preference formation during product learning are analyzed. A hypothesis about how individuals form attribute expectations is used to derive a dynamic multinomial logit model of individual choice which endogenously recognizes product learning. The model and its underlying assumptions are then tested in the context of an interactive grocery store learning game. The results support most elements of the proposed model structure. An assumption of temporal stationarity in attribute salience, however, could not be supported. Approaches to implementation of the model are discussed, and implications for marketing management and research in individual choice modeling are addressed.

A general model for the behavioral analysis of traveler decision making is described. The model is one which blends research in human information processing with probabilistic choice theory. The model is used as a basis for discussing two studies of grocery store choice behavior. In these studies, algebraic models of individuals' grocery store preferences which are derived using controlled hypothetical choice situations are related to real-world choice behavior. Results show a high monotonic correspondence between laboratory and real-world behavior. Implications of the research for existing approaches to behavioral modeling are described.

A model of individual consumer choice is described which posits that the relative weights consumers associate with product attributes in forming preferences covary with the amount of variability on the attributes among choice alternatives in particular contexts. The model is derived in a logit-like form, and has advantages over previously used nested models in terms of not requiring an analyst to prespecify a decision hierarchy and being relatively simple computationally. A binomial form of the model is illustrated by an analysis of a set of experimental store choice data.

A new approach to inferring hierarchical models of consumer choice is described. A classification algorithm is used to estimate decision trees at an individual level without requiring prior assumptions about tree form. Derived models are analyzed within a modeling system that summarizes the diversity of decision rules in a sample as well as their implications for aggregate market shares. An application to the analysis of panel data and a comparison with disaggregate logit analysis are reported.

In a recent article, Keller and Staelin (1987) reexamined one of the more controversial questions in consumer research literature: Is it possible to provide consumers with too much information when making choices? Their work contained the valuable and insightful conjecture that increasing quantity of information available to consumers increases the overall quality of information as well, when quality is defined as the cumulative importance of each bit of information. Because prior research had not explicitly controlled these two effects, degrading effects of increasing information quantity may have been muted by unintended, but commensurate, increases in information quality. Their research objective was to decompose these two sources of variance experimentally. Keller and Staelin found that when information quality was held constant, increases in information had a strong negative effect on decision accuracy. When information quantity was held constant, increasing information quality had a positive effect, at least to a point. Keller and Staelin (1987, p. 212) interpreted their results as evidence that consumers can be overloaded with information. The results of this study imply that consumers are not able to shield themselves from being overloaded when "too much" information is made readily available to the decision maker; too much being related to the quantity and average quality of the available information.

Linear compensatory models, which involve tradeoffs between product attributes, have been argued to provide reasonably good predictions of choices made by noncompensatory heuristics, which do not involve tradeoffs. This robustness to misspecification of functional form may fail, however, when there are negative correlations among attributes in a choice set. A Monté Carlo simulation demonstrates that certain noncompensatory rules are poorly fit by linear models, even in orthogonal environments, and that this fit diminishes further in nonorthogonal environments. Two laboratory experiments assess the extent to which such model failure might arise in natural contexts. The first, a process-tracing analysis, examines the decision strategies consumers use in nonorthogonal choice environments. The second explores the ability of a compensatory choice model calibrated on actual choices to predict decisions made in orthogonal and nonorthogonal contexts. The authors conclude with a discussion of the work's implications for current research in applied choice modeling.