Lead by Example? Custom-Made Examples Created by Close Others Lead Consumers to Make Dissimilar Choices
Bibliographic record
Abstract
Abstract Prior to customizing for themselves, consumers often encounter products customized by other people within their social network. Our research suggests that when encountering a custom-made example of an identity-related product created by an identified social other, consumers infer this social other was motivated to express uniqueness. After making this inference, consumers are also motivated to express uniqueness, particularly when the example was created by a close versus distant social other. Consumers express uniqueness through their own customization choices, choosing fewer options shown in the example or choosing fewer best-selling options. Consumers sometimes even pay a monetary cost or sacrifice preferred choices in order to make their own product unique. Further, this effect dissipates when motivations other than expressing uniqueness are inferred about a social other (e.g., for functionally related products). Across eight studies that span different product contexts, involve real choices, and isolate the underlying theoretical mechanism (i.e., motivation to express uniqueness), our research documents the unique role of custom-made examples, demonstrates the importance of social distance for customization choices, and identifies a novel path explaining when and why individuals express uniqueness.
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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 | 0.002 | 0.014 |
| 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 | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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".