Bharti, Soaham, and Abigail B. Sussman (forthcoming), “Consumers Prefer Products That Work Using Directionally Consistent Causal Chains,” Journal of Consumer Research. Job Market Paper.
Products often aim to help consumers achieve desired outcomes such as increasing energy levels or removing fabric stains. These products typically work via rich causal paths. The current research suggests that the structure of these paths influences consumer judgments of product efficacy. In particular, sequential steps in these paths can evoke distinct directionalities—either increasing or decreasing variables in each step along the way. For example, a face cream could be described as “increasing the turnover of skin cells to reduce wrinkles.” Under our framework, the action influencing skin cells would correspond to increasing directionality, while the action influencing wrinkles would correspond to decreasing directionality. Ten experiments provide evidence that consumers prefer products with directionally consistent causal chains (i.e., all steps evoking the same directionality) over those with directionally inconsistent ones (i.e., steps evoking contrasting directionalities). This occurs because consumers find directionally consistent causal chains easier to process, which in turn leads them to infer higher efficacy from products working via such consistent chains. These findings advance our understanding of how consumers evaluate product descriptions and provide prescriptions for marketers tasked with composing product descriptions to convey efficacy.
Bharti, Soaham, and Berkeley J. Dietvorst, “Consumers opt for more attribute upgrades when selecting between preconfigured products as opposed to configuring the product themselves,” Revise & Resubmit at the Journal of Consumer Research.
Marketers often structure a shopping task in two key formats: by offering a choice between preassembled products (preconfigured choice) or having consumers configure and customize a product’s attributes (customization). Results from six studies find that consumers tend to opt for more attribute upgrades when choosing between preconfigured options compared to customizing the products themselves. We propose that this occurs because the selection format serves as a contextual cue that shapes consumers’ evaluation of attribute upgrades. In preconfigured choice, upgrades are perceived as substantive differentiators between models, while in the customization format, these upgrades are viewed as more incremental enhancements over the base model. Consistent with such an account, the effect of selection format is attenuated when more deliberative processing overrides the influence of contextual cues, as in the case of highly important attributes. From a theoretical standpoint, these results extend research on how observed consumer preferences are sensitive to the elicitation method. From an applied standpoint, we deliver recommendations to marketers interested in structuring shopping tasks with the aim of promoting attribute upgrades.
Dietvorst, B. J., and Soaham Bharti (2020), People Reject Algorithms in Uncertain Decision Domains Because They Have Diminishing Sensitivity to Forecasting Error, Psychological Science, 31(10), pp.1302-1314.
Will people use self-driving cars, virtual doctors, and other algorithmic decision-makers if they outperform humans? The answer depends on the uncertainty inherent in the decision domain. We propose that people have diminishing sensitivity to forecasting error and that this preference results in people favoring riskier (and often worse-performing) decision-making methods, such as human judgment, in inherently uncertain domains. In nine studies (N = 4,820), we found that (a) people have diminishing sensitivity to each marginal unit of error that a forecast produces, (b) people are less likely to use the best possible algorithm in decision domains that are more unpredictable, (c) people choose between decision-making methods on the basis of the perceived likelihood of those methods producing a near-perfect answer, and (d) people prefer methods that exhibit higher variance in performance (all else being equal). To the extent that investing, medical decision-making, and other domains are inherently uncertain, people may be unwilling to use even the best possible algorithm in those domains.
Bartels, D., Ye Li, and Soaham Bharti (2023), "How Well Do Laboratory Derived Measures of Time Preference Predict Real-world Behaviors? Comparisons to Four Benchmarks." Journal of Experimental Psychology: General, 152(9):2651-2665.
A large literature implicates time preference (i.e., how much an outcome retains value as it is delayed) as a predictor of a wide range of behaviors, because most behaviors involve sooner and delayed consequences. We aimed to provide the most comprehensive examination to date of how well laboratory-derived estimates of time preference relate to self-reports of 36 behaviors, ranging from retirement savings to flossing, in a test–rest design using a large sample (N = 1,308) and two waves of data collection separated by 4.5 months. Time preference is significantly - albeit modestly - associated with about half of the behaviors; this is true even when controlling for 15 other demographic variables and psychologically relevant scales. There is substantial variance in the strengths of associations that is not easily explained. Time preference’s predictive validity falls in the middle of these 16 possible predictors. Finally, we ask time preference researchers (N = 55) to predict the variation in the relationship between time preference and behaviors, and although they are reasonably well-calibrated, these experts tend to overestimate the predictive power of time preference estimates. We discuss implications of invoking time preference as a predictor and/or determinant of behaviors with delayed consequences in light of our findings.
Bharti, Soaham, and Daniel M. Bartels, “Asymmetry in the perceived impact of increasing versus decreasing a causal factor.”
In many domains, from consumer products to policy interventions, decision-makers often need to predict how changes in the magnitude of a causal factor (such as drug dosage) will influence resulting outcomes. Across multiple experiments, we find a robust directional asymmetry: People expect a larger impact from increasing a causal input than from decreasing it by the same amount. For example, people believe that raising the dose of a drug will enhance its effects more than lowering the dose would diminish them. This pattern generalizes across a range of causal systems, and holds for both intended outcomes as well as unintended side-effects. Furthermore, this our evidence suggests that this asymmetry stems from a bias in judgment, rather than a coherent mental model of causal relationships.