Tunnel Vision: Local Behavioral Influences on Consumer Decisions in Product Search
Bibliographic record
Abstract
We introduce and test a behavioral model of consumer product search that extends a baseline normative model of sequential search by incorporating non-normative influences that are local in the sense that they reflect consumers’ undue sensitivity to recently encountered alternatives. We propose two types of such local behavioral influences that, at each stage of a search process, can manifest themselves both in which of the products inspected up to that point is deemed to be the most preferred one (the product comparison decision) and whether to terminate the search at that stage (the stopping decision). The first of these influences is that consumers respond excessively to the attractiveness of the currently inspected product, at the expense of all others (“focalism”). The second proposed behavioral influence is that consumers overreact to the difference in attractiveness between the current product and the one encountered just prior to it (“local contrast”). Converging evidence from two experiments, which combine to guarantee both high internal and high external validity, provides support for the proposed behavioral influences. Our findings demonstrate that consumers’ product comparison and stopping decisions in sequential product search are jointly governed by normative principles and by the proposed local behavioral influences.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".