The Upside of Negative: Social Distance in Online Reviews of Identity-Relevant Brands
Bibliographic record
Abstract
Conventional wisdom in marketing emphasizes the detrimental effects of negative online reviews for brands. An important question is whether some firms could more effectively manage negative reviews to increase brand preference and improve outcomes. To address the question, this research examines how customers respond to online reviews of identity-relevant brands in particular, which have been overlooked in the online reviews literature. Eight studies (field data and experiments featuring consequential and hypothetical behaviors) show that negative online reviews may not be so detrimental for identity-relevant brands, especially when those reviews originate from socially distant (vs. socially close) reviewers. This occurs because a negative review of an identity-relevant brand can pose a threat to a customer's identity, prompting the customer to strengthen their relationship with the identity-relevant brand. To document the underlying process, the authors show that this effect does not emerge when the review is positive or the brand is identity-irrelevant. Importantly, the authors identify circumstances when negative reviews can actually produce positive outcomes (higher preference) for identity-relevant brands over no reviews or even positive reviews. By demonstrating the upside of negative reviews for identity-relevant brands, the findings have important implications for marketing theory and practice.
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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 | 0.007 | 0.093 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".