Awe‐inspired: Appraising awe's consequences for consumers and brands
Bibliographic record
Abstract
Abstract This article builds on Keltner's conceptual model of awe, innovation, and choice (Keltner, 2025). This article expands on the framework in two main ways by outlining (1) when awe could have positive versus negative consequences for consumer choice and (2) how focusing on distinctive aspects of the consumer behavior setting may further enhance understanding of awe. Building on these themes, this article proposes several areas for research: examining granular aspects of the core appraisals, further characterizing different cognitive functions, considering consequences for different consumer choice domains (e.g., decision making, indulgence, customization), and focusing on how different kinds of relationships (e.g., brand communities), types of prosocial action (e.g., donating vs. volunteering), and forms of brand generated awe (direct vs. indirect) impact consumer behavior. This article offers specific propositions to encourage future research on how awe may impact consumers and brands.
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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 | 0.003 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".