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Record W4388956797 · doi:10.1108/ejm-04-2022-0254

Consumers’ attributions in performance- and values-related brand crises

2023· article· en· W4388956797 on OpenAlexaff
Liangyan Wang, Eugene Y. Chan, Ali Gohary

Bibliographic record

VenueEuropean Journal of Marketing · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicConsumer Behavior in Brand Consumption and Identification
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsBlameAttributionReputationValue (mathematics)OriginalityMarketingBrand equityBrand managementBusinessSocial psychologyAdvertisingPsychologyPolitical science

Abstract

fetched live from OpenAlex

Purpose During a brand crisis, consumers construct attributions to understand the cause of the crisis and to assign blame, with attributions of blame to firms consequently lowering brand attitudes. The purpose of this paper is to explore attributions of blame in performance- versus values-related brand crisis. Do consumers assign different levels of blame to values- versus performance-related brand crises? Design/methodology/approach The authors conducted three experimental studies, plus one pilot study, with American, British and Australian participants in which they manipulated the type of brand crisis as values- or performance-related to determine the extent to which consumers attribute blame to the firm and the effects of those attributions on consumers’ brand attitudes. Findings Findings indicated that consumers assign more blame to firms for a values-related brand crisis than for a performance-related brand crisis. Research limitations/implications The findings of this study explain how consumers are harsher towards firms that violate some moral or social standards than those that exhibit product defects. Practical implications For branding and public relations officials, finding greater internal attribution for values-related brand crises offers implications for how and what information about such crises ought to be conveyed to manage consumer response and brand reputation. Originality/value To the best of the authors’ knowledge, the findings are the first to explore attributions in blame toward values- and performance-related brand crises.

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)0.006
metaresearch head score (Gemma)0.001
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 score0.321
Threshold uncertainty score0.359

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.038
GPT teacher head0.245
Teacher spread0.207 · 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

Citations12
Published2023
Admission routes1
Has abstractyes

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