When and Why Choices For Others Diverge From Consumers’ Own Salient Goals
Bibliographic record
Abstract
Consumers frequently make choices and purchase products for other people (e.g., buying a gift for a friend). While extant research identified many factors that influence how choices for others are made, much of this literature focused on product-specific factors or motivations pertaining to the process of exchange to understand choice-for-others phenomena. Little is known about the influence of consumer-relevant factors on choices made for other people. In the current research we examine how choices for others are influenced by consumers’ own salient personal goals (e.g., to get fit, to succeed professionally). We find that consumers choose goal-inconsistent options for others to enhance their own perception of progress toward their salient goal. This effect is most robust when a salient goal is held at an individual (vs. group) level and when the choice-for-other situation exhibits a relationship (vs. recipient) focus. Results of seven experiments support these claims.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".