Assimilation or Contrast? Status Inequality, Judgment of Product Quality, and Product Choices in Markets
Bibliographic record
Abstract
Much of organizational status research has been conducted at the micro level by examining the effect of individual status positions. To answer calls for more status research at the macro level, this study extends psychological research on assimilation and contrast effects to examine how status inequality as a distributional property influences product choices in markets. In the context of U.S. college bowls (a specific type of organization within U.S. collegiate athletics), this study analyzes how the status inequality among bowls influences bowls’ stadium attendance, which reflects the judgment of bowls by football fans as the key buyers. The analyses yield evidence consistent with assimilation and contrast effects. Below the middle level of status inequality, the relationship between status inequality and stadium attendance is positive for low-status bowls but negative for high-status bowls. Above the middle level of status inequality, the relationships are reversed. The effect of status inequality is also stronger for low-status bowls that are newer and thus more uncertain in product quality. These findings make significant contributions to understanding status hierarchies in markets by redirecting organizational status research with a macrolevel view, uncovering cognitive processes underlying buyers’ judgment of products based on organization status, and demonstrating the dynamics of status hierarchies and their consequences for organizations.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".