Mickey D’s Has More Street Cred Than McDonald’s: Consumer Brand Nickname Use Signals Information Authenticity
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
Consumers often observe how other consumers interact with brands to inform their own brand judgments. This research demonstrates that brand relationship quality–indicating cues, such as brand nicknames (e.g., “Mickey D’s” for McDonald’s, “Wally World” for Walmart), enhance perceived information authenticity in online communication. An analysis of historical Twitter data followed by six experiments (using both real and fictitious brands across different online platforms [e.g., online reviews, social media posts]) show that brand nickname use in user-generated content signals a writer’s relationship quality with the target brand from the reader’s perspective, which the authors term “inferred brand attachment.” The authors demonstrate that inferred brand attachment boosts perceived information authenticity and leads to positive downstream consequences, such as purchase willingness and information sharing. The authors also find that this effect is attenuated when brand nicknames are used in firm-generated content. How consumers’ relationships with brands are portrayed and perceived in a social context (e.g., via brand nickname use) serves as a novel context to examine user-generated content and provides valuable managerial insight regarding how to leverage consumers’ brand attachment cues in brand strategy and online information management.
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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 | 0.009 | 0.036 |
| 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 | 0.001 | 0.002 |
| 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