Linda Court Salisbury

The Formation of Market-Level Expectations and Its Covariates

A formal model of market-level expectations is developed and used to identify testable hypotheses. The empirical findings indicate that market-level expectations are more adaptive in nature than previously thought. The study also provides the first systematic investigation of cross-industry variation in the formation of market-level expectations. Several factors, including advertising, word-of-mouth, market growth, and purchase frequency, are found to have a significant moderating influence on the adaptation rate. Finally, we find that market-level expectations adjust faster when perceived quality declines, suggesting that negativity biases manifest at a macrolevel--a phenomenon that has not been previously observed. Copyright 2003 by the University of Chicago.

Alleviating the Constant Stochastic Variance Assumption in Decision Research: Theory, Measurement, and Experimental Test

Analysts often rely on methods that presume constant stochastic variance, even though its degree can differ markedly across experimental and field settings. This reliance can lead to misestimation of effect sizes or unjustified theoretical or behavioral inferences. Classic utility-based discrete-choice theory makes sharp, testable predictions about how observed choice patterns should change when stochastic variance differs across items, brands, or conditions. We derive and examine the implications of assuming constant stochastic variance for choices made under different conditions or at different times, in particular, whether substantive effects can arise purely as artifacts. These implications are tested via an experiment designed to isolate the effects of stochastic variation in choice behavior. Results strongly suggest that the stochastic component should be carefully modeled to differ across both available brands and temporal conditions, and that its variance may be relatively greater for choices made for the future. The experimental design controls for several alternative mechanisms (e.g., flexibility seeking), and a series of related models suggest that several econometrically detectable explanations like correlated error, state dependence, and variety seeking add no explanatory power. A series of simulations argues for appropriate flexibility in discrete-choice specification when attempting to detect temporal stochastic inflation effects.

Minimum Required Payment and Supplemental Information Disclosure Effects on Consumer Debt Repayment Decisions

Repayment decisions—how much of the loan to repay and when to make the payments—directly influence consumer debt levels. The authors examine how minimum required payment policy and loan information disclosed to consumers influence repayment decisions. They find that while presenting minimum required payment information has a negative impact on repayment decisions, increasing the minimum required level has a positive effect on repayment for most consumers. Experimental evidence from U.S. consumers shows that consumers’ propensity to pay the minimum required each month moderates these effects; U.K. credit card field data indicate that borrowers’ credit limit and balance due also moderate these effects. However, increasing the minimum level is unlikely to completely eliminate the negative effect of presenting minimum payment information. In addition, disclosing supplemental information, such as future interest cost and time needed to repay the loan, does not reduce the negative effects of including minimum payment information and has no substantial positive effect on repayments. This research offers new insights into the debt repayment process and has implications for consumers, lenders, and public policy.

Order Effects in Customer Satisfaction Modelling

This research examines the effects ofquestion order on the output of a customer satisfaction model. Theory suggests that locating product attribute evaluations prior to overall evaluations of satisfaction and loyalty should increase the impact of performance drivers in the model, explain more variation in the overall evaluations, and make positive satisfaction and loyalty evaluations more extreme. Our results show that, although customers′ overall evaluations are more extreme and better explained when provided after attribute evaluations, the impact of satisfaction drivers is relatively unaffected. Consistent with expectations, question order does affect the explained variation in satisfaction and the levels of satisfaction and loyalty. Implications for satisfaction modelling are discussed.

All Things Considered? The Role of Choice Set Formation in Diversification

The diversification effect, a tendency toward greater variety as multiple choices are made simultaneously, in advance of consumption, is a robust and important phenomenon. Researchers have typically explained the diversification effect in terms of differences in the process by which people select items from among those available. This precludes the possibility that the locus of the effect lies in peoples choice sets themselves—that is, how people decide which options to consider when making choices. The authors examine the effects of set formation using an experimental choice sequence task and conjoined stochastic model of set formation and conditional choice. The findings demonstrate that set formation plays a critical role in diversification: Previously chosen options are indeed discounted, but only for simultaneous choices and only in the set formation portion of the model. Furthermore, the expected number of choice set items is substantially greater in multiple- versus single- item choice. Specifically, when consumers choose simultaneously choice set sizes appear relatively larger overall, but the previously chosen item is less likely to be in a persons (latent) choice set. These findings cannot be attributed to alternative patterns of covariation, including latent error correlations; to temporal stochastic inflation; or to unobserved heterogeneity.

Rejoinder---Temporal Stochastic Inflation in Choice-Based Research

We examine the specification and interpretation of discrete-choice models used in behavioral theory testing, with a focus on separating “coefficient scale” from “error scale,” particularly over time. Numerous issues raised in the thoughtful commentaries of Louviere and Swait [Louviere, J., J. Swait. 2010. Discussion of “Alleviating the constant stochastic variance assumption in decision research: Theory, measurement, and experimental test.” Marketing Sci.29(1) 18--22] and Hutchinson, Zauberman, and Meyer (HZM) [Hutchinson, J. W., G. Zauberman, R. Meyer. 2010. On the interpretation of temporal inflation parameters in stochastic models of judgment and choice. Marketing Sci.29(1) 23--31] are addressed, specifically the roles of response scaling, preference covariates, actual versus hypothetical consumption, “immediacy,” and heterogeneity, as well as key differences between the experimental setup in Salisbury and Feinberg [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] and those typifying intertemporal choice and construal level theory. We strongly concur with most of the general conclusions put forth by the commentary authors, but we also emphasize a central point made in our research that may have been lost: that the temporal inflation effects observed in our empirical analysis could be attributed to stochastic effects, deterministic influences, or an amalgam; appropriate inferences depend on the nature of ones data and stimuli. We also report on further analyses of our data, as well as a meta-analysis of HZMs Table 1 that is consistent with our original findings. Implications for, and dimensions relevant to, future research on temporal stochastic inflation and its role in choice-based research are discussed.

When Random Assignment Is Not Enough: Accounting for Item Selectivity in Experimental Research

Experimental methods are critical tools in marketing, psychology, and economics to isolate the effects of key variables from vagaries intrinsic to field data. As such, they are often considered exempt from the sort of sample selectivity artifacts widely documented in empirical research, in part because participants are randomly assigned to experimental conditions. To conserve time and resources, experiments often focus on items participants have chosen or are familiar with, for example, postchoice satisfaction ratings, certain free recall tasks, or specifying consideration sets preceding brand choice. When consumer input even partially influences the items about which researchers request subsequent data, the potential for item selectivity arises. In such situations, analyses are contingent on both the choice context(s) of the experiment and the alternatives participants elect to evaluate, potentially leading to substantial item selectivity overall and to differing degrees across conditions. We examine situations in which a nonignorable “choose one of many” (polytomous) selection process limits which items offer up subsequent information, and develop methods to allow substantive results to pertain to the full set of items, not only those selected. The framework is illustrated via two experiments in which participants choose and then evaluate a frequently purchased consumer good as well as data first examined by Ratner et al. [Ratner RK, Kahn BE, Kahneman D (1999) Choosing less-preferred experiences for the sake of variety. J. Consumer Res. 26(1):1–15]. Results indicate substantial item selectivity that, when corrected for, can lead to markedly different interpretations of focal variable effects, such as large effect size changes and even sign reversal. Moreover, failing to flexibly account for item selectivity across experimental conditions , even in well-designed experimental settings, can lead to inaccurate substantive inferences about consumers’ evaluative criteria. We further demonstrate robustness to theoretically driven (but not overtly misspecified) selection rules and provide researchers with a simple, “two-step” exploratory procedure akin to a “control function” approach—involving just one additional variable added to standard models—to determine whether and to what degree item selectivity may be affecting their substantive results.Data, as supplemental material, are available at https://doi.org/10.1287/mksc.2016.0991 .

Individuals' Decisions in the Presence of Multiple Goals

This paper develops new directions on how individuals’ use of multiple goals can be incorporated in econometric models of individual decision making. We start by outlining key components of multiple, simultaneous goal pursuit and multi-stage choice. Since different goals are often only partially compatible, such a multiple goal-based approach implies balancing goals, leading to a deliberate goal-level choice strategy on the part of the decision maker. Accordingly, we introduce a conceptual framework to classify different aspects of individuals’ decisions in the presence of multiple goals. Based on this framework we propose a formalization of individual decision making when pursuing multiple goals. We briefly review different previous streams on goal-based decision making and how the proposed goal-driven conceptual framework relates to earlier research in discrete choice models. The framework is illustrated using examples from different domains, in particular marketing, environmental economics, transportation and sociology. Finally, we discuss identification and modeling needs for goal-based choice strategies and opportunities for further research.