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Record W2048838488 · doi:10.1108/03090560910989939

Differentiation and silver medal winner effects

2009· article· en· W2048838488 on OpenAlexaff
Albert Caruana, Leyland Pitt, Pierre Berthon, Michael Page

Bibliographic record

VenueEuropean Journal of Marketing · 2009
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsReputationMedalOriginalityMarketingPersonality psychologyPersonalityValue (mathematics)Brand managementAdvertisingPsychologyBusinessSociologySocial psychologyCreativityComputer scienceSocial scienceArt

Abstract

fetched live from OpenAlex

Purpose The aim of this paper is to consider business schools and to elicit whether, in seeking differentiation, rankings are more desirable than brand personality and whether silver medal winner effects exist in the perceptions of brand personalities. Design/methodology/approach Literature on reputation, identity, differentiation, brand personality and its measurement is reviewed. In seeking to determine the role of rankings and the presence of silver medal effects two survey data collections among business schools are conducted using the identified brand personality instrument. Findings Results highlight the importance of a distinctive differentiation positioning and show that reputation reflected in published rankings are able to provide counterfactuals that can influence consumer emotions and help establish preferences. Silver medal effects are found to play an important role. Originality/value These results emphasise the point that it is simply not enough to be ranked highly. What seems to be more critical is to be perceived as different. It appears that brand personality rather than reputation in terms of ranking is more strongly related to customers' expressed preferences. The results also illustrate the need to understand and deal with the challenge faced by marketing managers when silver medal effects are present.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.300

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.012
GPT teacher head0.211
Teacher spread0.199 · 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

Citations5
Published2009
Admission routes1
Has abstractyes

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