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Record W2196981679 · doi:10.1080/02650487.2015.1090521

The effectiveness of movie trailer advertising

2015· article· en· W2196981679 on OpenAlex

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

VenueInternational Journal of Advertising · 2015
Typearticle
Languageen
FieldArts and Humanities
TopicArt History and Market Analysis
Canadian institutionsOntario Tech University
FundersMinistry of Education, IndiaMinistry of Earth Sciences
KeywordsTrailerAdvertisingAppealBusinessSample (material)PsychologyMarketingPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Prior to a movie release in theaters, trailer advertising provides valuable information that can help viewers and investors form expectations about the movie's future success. While previous research has looked at the financial implications of movie advertising budgets, the effects of trailers' creative characteristics on abnormal returns have not yet been investigated. Using a sample of movie trailers, results from our event study and cross-sectional analysis show that the appeal of the movie plot revealed in the trailer, the number of scene cuts and the inclusion of violent, sexual, or humorous scenes influence the movie's abnormal returns. However, the use of special effects in the movie trailer does not impact investors. Results also suggest that investors react more strongly to first than to follow-up trailers released for the movie, and that early release of the first positively impacts the movie's returns.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.916
Threshold uncertainty score0.219

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.023
GPT teacher head0.260
Teacher spread0.238 · 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