Perceived economic mobility predicts evaluation of low‐fit co‐brands
Bibliographic record
Abstract
Abstract Co‐branding is an effective marketing strategy that is widely used by brands to expand the market, but research on the influence of consumer‐level factors is limited, with predominant emphasis on brand‐level factors that predict acceptance of brands that are seemingly “different” from each other co‐branding with one another. This research explores the effect of perceived economic mobility on perceived co‐branding fit. Findings from Experiment 1 indicate that co‐brands low or moderate (vs. high) fit with one another are perceived more favorably when perceived economic mobility is higher. Experiment 2 further examines the proposed mechanism that we propose to be holistic thinking style behind the influence of perceived economic mobility on distant co‐branding evaluation, and it also rules out two alternative explanations. Experiment 3 replicates above findings with an American dataset. Our findings contribute to the co‐branding literature by proposing a novel antecedent—perceived economic mobility—of low‐fit co‐branding. Our findings also provide managerial guidelines for enhancing co‐branding effectiveness.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".