When Certainty Backfires: The Effects of Unwarranted Precision on Consumer Loyalty
Bibliographic record
Abstract
ABSTRACT Consumers are drawn to the promise of certainty that precise forecasts seem to provide, even though they are often misleading. Yet we know less about how consumers respond when precise forecasts prove inaccurate. In this paper, we investigate how inaccurate precise compared to range forecasts affect consumer judgments and decisions over time in an investment context. Specifically, we assess how they affect consumers' loyalty towards the forecaster as well as their willingness to make the same kind of investment again. Consumers were less trusting of and loyal to investment management firms that communicated inaccurate precise forecasts compared to firms that communicated inaccurate range forecasts, which acknowledged uncertainty. But we did not find evidence that consumers changed their minds as to the sector into which they wanted to invest. In other words, they seem to punish the firm for inaccurate forecasts, but this did not shift their preference for their type of investment. Interestingly, these effects largely persisted when consumers encountered similar inaccurate forecasts 1 week later, suggesting they do not learn to be suspicious of precise forecasts in general from exposure to inaccurate forecasts. Overall, our findings show that it is not in firms' interest to communicate overly precise forecasts under uncertainty as they risk punishment by consumers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.054 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".