Brands as Rivals: Consumer Pursuit of Distinctiveness and the Role of Brand Anthropomorphism
Bibliographic record
Abstract
Abstract Although past research has shown that anthropomorphism enhances consumers’ attraction to a brand when social-connectedness or effectance motives are active, the current research demonstrates that anthropomorphizing a brand becomes a detrimental marketing strategy when consumers’ distinctiveness motives are salient. Four studies show that anthropomorphizing a brand positioned to be distinctive diminishes consumers’ sense of agency in identity expression. As a result, when distinctiveness goals are salient, consumers are less likely to evaluate anthropomorphized (vs. nonanthropomorphized) brands favorably and are less likely to choose them to express distinctiveness. This negative effect of brand anthropomorphism, however, is contingent on the brand’s positioning strategy—brand-as-supporter (supporting consumers’ desires to be different) versus brand-as-agent (communicating unique brand features instead of focusing on consumers’ needs) versus brand-as-controller (limiting consumers’ freedom in expressing distinctiveness). Our results demonstrate that an anthropomorphized brand-as-supporter enhances consumers’ sense of agency in identity expression, compared to both an anthropomorphized brand-as-agent and an anthropomorphized brand-as-controller. In turn, enhancing or thwarting consumers’ sense of agency in expressing their differences from others drives the differential impact of anthropomorphizing a brand positioned to be distinctive.
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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 | 0.001 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".