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Record W4312109979 · doi:10.1002/mar.21779

Consumers' moral licensing of firms' CSR transgressions

2022· article· en· W4312109979 on OpenAlexafffund
Argiro Kliamenakis, Bianca Grohmann, Onur Bodur

Bibliographic record

VenuePsychology and Marketing · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsConcordia UniversityUniversity of Ottawa
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsAmbiguityCorporate social responsibilityMarine transgressionAttributionPsychologySocial psychologyHypocrisyPerceptionLicensePublic relationsPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Despite corporate social responsibility (CSR) engagement, firms may be implicated in CSR transgressions. Research is equivocal on whether CSR buffers negative consumer responses to subsequent firm transgressions. This research extends the observer moral licensing framework to consumer‐firm contexts and examines under what conditions consumers license transgressions following firms' CSR engagement. Study 1 demonstrates that consumer responses to firm transgressions depend on whether the transgression occurs in the same (vs. different) domain relative to CSR engagement and on transgression ambiguity. Study 2 shows that consumer responses to transgressions are less negative when a firm has (vs. has not) previously engaged in CSR. It replicates a shielding effect of CSR that is contingent on transgression domain relative to prior CSR and transgression ambiguity, and finds that blatant same domain transgressions generate hypocrisy perceptions and mitigate licensing effects. Study 3 further extends the moral licensing framework and shows that firm communication that situates CSR efforts on a continuum (continuous CSR positioning) before a transgression moderates, and insincere firm motive attributions mediate, the detrimental effect of blatant same domain transgressions.

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)0.003
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 score0.207
Threshold uncertainty score0.678

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.035
GPT teacher head0.297
Teacher spread0.262 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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