Some like it warm: How warm brands mitigate the negative effects of social exclusion
Bibliographic record
Abstract
Abstract Consumers' feelings of being excluded—which indicate a deficit in important social resources such as connection, acceptance, and support—have increased over the last 50 years. In this research, by adopting a resource‐based view of brands, we examine how and why brands play a role in socially excluded consumers' lives. Across a series of studies, we find that excluded consumers perceive warm (vs. less warm) brands as better relationship partners. Because of this, excluded consumers choose warm (vs. less warm) brands more often, and they feel less lonely as a result. We also test the role of brand warmth relative to brand competence and to individual differences in self‐acceptance. We find that excluded consumers' preferences for warm brands persist even when the warm brands are low in competence and even when consumers possess high self‐acceptance. This research reveals the relational, resource‐restorative role of warm brands and provides implications for consumers' coping and emotional well‐being in our increasingly isolated society.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| 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".