BMW Is Powerful, Beemer Is Not: Nickname Branding Impairs Brand Performance
Bibliographic record
Abstract
This research investigates nickname branding, a novel phenomenon whereby firms incorporate the "street" names consumers give brands into their own marketing (e.g., Bloomingdale's opening a "Bloomie's" store). While practitioners anticipate positive results from deploying this tactic, the current research serves as the first empirical investigation of its likely effectiveness. Drawing on speech act theory, the authors theorize that using a nickname in place of a formal name serves as an act of power redistribution, effectively signaling submission to consumers, thereby reducing the perception of a brand's power and weakening its performance. Through a multimethod approach that incorporates secondary data analyses, field studies, and preregistered experiments, the results support this view across a range of performance metrics. In addition, the authors show that this effect is contingent on two factors, such that nickname branding (1) harms performance more for competent brands than warm brands and (2) is less pronounced when nicknames are used in messages that are communal-oriented (vs. transactional-oriented). This research introduces a new theoretical perspective centering on the illocutionary meanings embedded in the process of naming brands and highlights actionable insights on how marketers should approach or avoid consumer-based slang in their marketing.
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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 | 0.002 | 0.010 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".