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Record W1935953820 · doi:10.1287/orsc.2015.1007

Assimilation or Contrast? Status Inequality, Judgment of Product Quality, and Product Choices in Markets

2015· article· en· W1935953820 on OpenAlexaff
Zhi Huang, Marvin Washington

Bibliographic record

VenueOrganization Science · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicSports, Gender, and Society
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProduct (mathematics)InequalityContrast (vision)Social statusAttendanceQuality (philosophy)Context (archaeology)Social psychologyPsychologyDemographic economicsSociologyEconomicsEconomic growthSocial science

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.007
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.055
Threshold uncertainty score0.746

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.110
GPT teacher head0.384
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2015
Admission routes1
Has abstractyes

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