Awe‐inspired: Appraising awe's consequences for consumers and brands
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 it