Dilution and Enhancement of Celebrity Brands through Sequential Movie Releases
Bibliographic record
Abstract
This article examines the effects of sequential movie releases on the dilution and enhancement of celebrity brands. The authors use favorability ratings collected over a 12-year period (1993–2005) to capture movement in the brand equity of a panel of actors. They use a dynamic panel data model to investigate how changes of brand equity are associated with the sequence of movies featuring these actors, after controlling for the possible influence from the stars’ off-camera activities. The authors also examine the underlying factors that influence the magnitude and longevity of such effects. In contrast with findings from existing research in product branding, the authors find evidence that supports the general existence of dilution and enhancement effects on the equity of a celebrity brand through his or her movie appearances. They also find that star favorability erodes substantially over time. Finally, this research offers insights for actors regarding how to make movie selections strategically to maximize their brand equity.
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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 | 0.035 | 0.031 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".