Ethical Branding in A Divided World: How Political Orientation Motivates Reactions to Marketplace Transgressions
Bibliographic record
Abstract
In today's marketplace, users (e.g., purchasers, influencers) are increasingly the “face” of brands to potential consumers, increasing the risk for brands should these users act poorly. Across seven studies, we document that political orientation moderates the desire for punishment toward users of ethical (vs. conventional) brands who commit moral transgressions. In response to identical marketplace transgressions, we observe that liberals punish ethical brand users less than conventional brand users. In contrast, conservatives punish the same users of ethical brands more than conventional brand users. We document that this bias stems from how people interpret the inconsistency between the ethical branding and the act of transgression, rather than from a group‐identity effect, showing how it does not arise in the absence of inconsistent information or when consumers are not able to integrate the inconsistent information to their judgments. We also investigate an avenue by which firms can reframe their ethical branding to reduce this politically motivated bias. We discuss this work's implications for moral judgments, marketplace attribute formation, and the branding of ethical goods in a politically divided world.
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.
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 | 0.004 | 0.020 |
| 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 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".