Beyond celebrity endorsement: new opportunities for celebrity advertising and branding
Bibliographic record
Abstract
Celebrities appear in up to 70% of all advertising, and brands may allocate more than a quarter of their advertising budget to securing a celebrity endorser. Numerous celebrities however are moving away from endorser roles and are instead creating celebrity-brands, which can generate millions of dollars in revenue. For brands that continue to enlist celebrity endorsers, alternative approaches to presenting celebrities within campaigns are being used to generate attention. This thesis comprises of four core papers and two supplementary articles which examine the influence of various applications of celebrities in marketing, on consumer attitudes and behavioural intentions. Using primarily causal research, this thesis examines new avenues for celebrity advertising and branding, providing guidance for marketing academics and practitioners. Findings indicated that consumer attitudes and intentions to purchase were more favourable for celebrity-brands than endorsed brands. Results showed that this can be attributed to the perception that a celebrity has higher investment in a celebrity-brand and is more authentic when promoting their own brand as opposed to endorsing another brand. As with endorsed brands, celebrity-brands may at some point suffer from the celebrity’s involvement in a scandal, however as terminating the relationship is impossible for a celebrity-brand, alternative strategies needed investigation. Results show consumers are more likely to forgive a celebrity when an appropriate punishment has been implemented, with forgiveness leading to positive outcomes for the celebrity-brand. Finally, an examination of the effectiveness of film personas revealed that consumers respond more positively to film persona endorsements when compared to endorsements by traditional celebrities. This thesis recommends that celebrities need to have high levels of investment in their celebrity-brand to be perceived as authentic and to encourage positive consumer attitudes and behavioural intentions. Furthermore, a celebrity should enact an appropriate punishment strategy in response to a personal crime to protect their celebrity-brand. Brands using endorsements can present celebrities as film personas to generate positive consumer judgements and shield the brand from celebrity scandals.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.015 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".