People often consult experts to provide solutions to their problems. Oftentimes, an initial attempt to solve a problem fails, and these experts (e.g., advisors, consultants, specialists, and service providers) will resort to a backup plan. A backup plan can be presented when it is needed (i.e., after an initial failure) or in advance (i.e., before a potential failure). We examine how the timing of disclosure of a backup plan affects judgments of an expert’s competence. We show that although people expect experts to have a backup plan, they react more negatively if it is presented after (versus before) it is needed, all things equal. This “competence penalty” disappears when people are prompted to think about what an expert would do in case of failure as indeed, an alternative plan is expected. Importantly, our findings indicate that there is no competence penalty for early disclosure of a backup plan, regardless of the outcome of the initial plan: if it has failed, has succeeded, or is still ongoing. However, we do find one exception; early disclosure has a negative impact if multiple backup plans are disclosed while the initial plan is in progress. This paper was accepted by Jack Soll, behavioral economics and decision analysis. Funding: Funding for all studies was provided by the MUMA College of Business, University of South Florida. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2023.02615 .

Abstract Marketers make various forecasts, including those about new products, financial instruments, sporting events, and medical procedures, to influence consumer decisions. In communicating the likelihood that a forecaster assigns to an outcome, the forecaster can refer to their confidence in the outcome (e.g., “I’m 30% confident”) or the probability of the outcome (e.g., “There is a 30% probability”). The authors propose that the choice of language (e.g., probability vs. confidence) affects the perceptions of forecasters, yielding predictable consequences on consumer decisions. Specifically, some languages (e.g., confident/sure/certain) encourage internal attributions (e.g., to a forecaster), whereas other languages (e.g., probability/likelihood/chance) encourage external attributions (e.g., to an outcome). As a result, expressions of a forecaster’s confidence (vs. outcome probability) make the forecaster look less competent, especially when the likelihood of the outcome is low. A series of studies shows the effect in various consumption scenarios. The effect is mediated by internal (vs. external) attributions, influences real betting decisions, and is mitigated when the likelihood is high and among consumers with a weaker tendency to make internal attributions (i.e., a weaker correspondence bias).

Consumers often wonder about the product's maximum output: the highest rotation speed of a blender or the best printing quality of a printer. We examine how the number of levels (e.g., a blender with 3 vs. 7 speeds) influences judgments of maximum product output. Objectively speaking, the number of levels is no more than a set of breakpoints in an already predetermined continuum from the product's minimum to maximum output. Nevertheless, because of the ubiquitous association between number of breakpoints and quantity in daily life, consumers do not simply view more levels as a signal of greater precision (i.e., giving consumers more control over the possible outputs). They also incorrectly believe that the product has greater power (i.e., a higher maximum output), even when such an inference is in conflict with diagnostic attribute information (e.g., watts). A series of five studies documents the phenomenon, its asymmetric nature, and its boundary conditions. Reliance on the inaccurate “more levels, more power” lay theory weakens when participants consider a reduction rather than an increase in number of levels, and it disappears when the consumer is presented with an explicit relationship between each level and its corresponding output value (e.g., level 4:400 W).