Fake news and brand management: a Delphi study of impact, vulnerability and mitigation
Bibliographic record
Abstract
Purpose Fake news is presently one of the most discussed phenomena in politics, social life and the world of business. This paper aims to report the aggregated opinions of 42 brand management academics on the level of threat to, the involvement of, and the available actions of brand managers resulting from fake news. Design/methodology/approach A Delphi study of 42 academics with peer-reviewed publications in the brand management domain. Findings The study found that on some dimensions (e.g. the culpability of brand managers for incentivizing fake news by sponsoring its sources), expert opinion varied greatly. Other dimensions (e.g. whether the impact of fake news on brand management is increasing) reached a high level of consensus. The general findings indicate that fake news is an increasing phenomenon. Service brands are most at risk, but brand management generally is need of improving or implementing, fake news mitigation strategies. Research limitations/implications Widely diverse opinions revealed the need for conclusive research on the questions of: whether brands suffer damage from sponsoring fake news, whether fake news production is supported by advertising and whether more extensive use of internet facilitated direct interactions with the public through crowdsourcing increased vulnerability. Practical implications Experts agreed that practitioners must become more adept with contemporary tools such as fake news site blacklists, and much more aware of identifying and mitigating the brand vulnerabilities to fake news. Social implications A noteworthy breadth of expert opinion was revealed as to whether embellished or fabricated brand narratives can be read as fake news, inviting the question as to whether brands now be held to higher standards of communication integrity. Originality/value This paper provides a broad-shallow exploratory overview of the professional opinions of a large international panel of brand management academics on how the recent arrival of industrial fake news does, and will, impact this field.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".