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Record W2950304728 · doi:10.1108/jpbm-12-2018-2150

Brand management in the era of fake news: narrative response as a strategy to insulate brand value

2019· article· en· W2950304728 on OpenAlex
Adam J. Mills, Karen Robson

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Product & Brand Management · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicMisinformation and Its Impacts
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsStorytellingBrand managementMisinformationNarrativeValue (mathematics)OriginalityAdvertisingBrand engagementBrand equityBrand awarenessPublic relationsBusinessMarketingPsychologyPolitical scienceSocial mediaSocial psychologyComputer scienceCreativityLaw

Abstract

fetched live from OpenAlex

Purpose Brand value is increasingly threatened by fake news stories; the purpose of this paper is to explain how narrative response can be used to mitigate this threat, especially in situations where the crisis is severe and consumers are highly involved. Design/methods This conceptual paper derives recommendations and guidance for the use of narrative response based on storytelling and brand management literature. Findings This paper highlights authenticity and emotional engagement as keys to effective storytelling. Practical implications Current managerial approaches to dealing with misinformation are insufficient, as they presuppose an audience that can be convinced based on facts; this paper can be used to help brand managers respond to fake news stories when rational appeals fail. Originality/value This paper provides insight into brand management strategies in the era of fake news.

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.

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)0.005
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.344
Threshold uncertainty score0.439

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.326
Teacher spread0.308 · 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